[
 {
  "id": "T01",
  "target": "bestpractice_pdf_to_markdown",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "## Cause\n\nOn Apple Silicon macOS, Docling's **RT-DETR V2 layout-detection model calls `torch.float64`** during the layout detection phase. The **MPS backend does not support `float64`**, so that operation fails and the entire PDF conversion crashes.\n\n## Workaround\n\nForce Docling onto the CPU backend with the `--device cpu` flag:\n\n```bash\ndocling input.pdf --to md --device cpu --output output_dir\n```\n\nFor text-only PDFs that don't need OCR, also add `--no-ocr` to cut out extra model paths and reduce false recognition:\n\n```bash\ndocling input.pdf --to md --device cpu --no-ocr --output output_dir\n```\n\n## Notes\n\n- **Don't reach for `PYTORCH_ENABLE_MPS_FALLBACK=1` as your first option.** In practice the fallback may still not take effect with certain version combinations; `--device cpu` is more deterministic.\n- If the CPU path still fails, or the PDF is a Chinese-language scan, image PDF, or pitch deck, switch to the **LM Studio VLM OCR** path instead — Docling carries MPS float64 risk for those document types.\n- If you're using the bundled wrapper CLI rather than Docling's official CLI, the equivalent invocation is:\n  ```bash\n  python rules/skills/pdf_to_markdown_cli.py docling input.pdf --output output.md --format json\n  ```",
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  "opened": [
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 {
  "id": "T02",
  "target": "mac_universal_clipboard",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "Yes — the item is **`handoff-own-encryption-key`** in Keychain Access. That's the Mac's local Handoff identity key; deleting it forces the Mac to regenerate its own key instead of reusing stale state.\n\nBut note the skill's escalation order puts two cheaper steps ahead of Keychain surgery, and you've only done one of them (the Handoff toggle). Here's the sequence:\n\n## Step 1: The daemon-level fast fix (do this first)\n\nYou said you tried \"basic restarts and toggling Handoff,\" but the specific daemon reset is a different thing and often fixes exactly your symptom (iPhone↔iPad fine, anything with the Mac broken):\n\n```bash\ndefaults delete ~/Library/Preferences/com.apple.coreservices.useractivityd.plist ClipboardSharingEnabled 2>/dev/null; \\\ndefaults write ~/Library/Preferences/com.apple.coreservices.useractivityd.plist ClipboardSharingEnabled 1; \\\nkillall useractivityd 2>/dev/null; \\\nkillall sharingd 2>/dev/null; \\\nkillall pboard 2>/dev/null; \\\nsleep 2; \\\npgrep -fl 'useractivityd|sharingd|pboard'\n```\n\nYou should see all three processes come back (launchd restarts them). Then test **both directions**: iPhone→Mac and Mac→iPhone.\n\nWhy this matters: `pboard` handles local pasteboard state, `useractivityd` holds the Handoff/Universal Clipboard preference and shared pasteboard blobs, and `sharingd` handles nearby-device discovery. If iPhone and iPad still sync with each other, your Apple ID and iOS-side Handoff are almost certainly fine — the problem is stale Mac-side daemon state, not your identity key.\n\n## Step 2: Safe Mode boot\n\nIf Step 1 doesn't hold, boot the Mac into Safe Mode once, then restart normally. This clears system caches and has historically restored broken Continuity state after macOS updates.\n\n## Step 3: Delete the Keychain item\n\nOnly now, if Steps 1–2 failed:\n\n1. Open **Keychain Access** (Applications → Utilities).\n2. Search for `handoff-own-encryption-key`.\n3. Delete that item.\n4. Restart Handoff (toggle it off/on in System Settings → General → AirDrop & Handoff) so the Mac regenerates the key.\n\n## Step 4: iCloud sign-out — last resort only\n\nSigning out of iCloud can trigger iCloud Drive, Photos, and Keychain resync, so it's a much bigger hammer than the problem warrants. Keep it as the final option.\n\n## One caveat\n\nIf you're on a **macOS beta build**, treat this as a possible OS regression after the first reset fails. In that case, don't keep escalating to heavier local changes — the symptoms likely match a beta Continuity bug, and the fix will come from Apple.\n\nIf you want root-cause evidence before escalating, run the diagnostics block (`sw_vers`, the `defaults read` of `ClipboardSharingEnabled`, `pgrep -fl 'pboard|useractivityd|sharingd|bluetoothd'`, the shared-pasteboard directory listing, and the `useractivityd`/`sharingd` log tails). Use `/usr/bin/log` rather than bare `log` in zsh, since `log` can resolve to a shell builtin in some environments.",
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  "opened": [
   "mac_universal_clipboard"
  ],
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 {
  "id": "T03",
  "target": "openreview",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The fix is in the skill's Known Limitations section, item 4:\n\n**Delete the cached token file and retry once:**\n\n```bash\nrm tmp/openreview_token.json\npython tools/openreview_cli.py papers ICLR.cc/2024/Conference\n```\n\nWhy this works: the CLI caches its auth token to `tmp/openreview_token.json` to avoid repeated logins (OpenReview allows only 3 logins per time window). When that cached token expires, the API returns HTTP 403. Removing the file forces the CLI to perform a fresh login using your credentials and write a new token.\n\nA few things to check if the retry still fails:\n\n1. **Credentials are present and correct.** The CLI reads `openreview_email` / `openreview_password` from `.env`, or the `OPENREVIEW_EMAIL` / `OPENREVIEW_PASSWORD` environment variables. A wrong password will also surface as an auth failure.\n2. **Don't loop the retry.** Because of the 3-logins-per-window rate limit, repeatedly deleting the token and retrying can lock you out for the window. Delete once, retry once, and if it still fails, verify credentials rather than retrying again.\n3. **Confirm the token path.** The cache location is `tmp/openreview_token.json` relative to the project root — make sure you're deleting it from the right directory (the same one you run `tools/openreview_cli.py` from).\n\nThen re-run your original query (e.g. `papers`, `profile`, `search`, or `profiles`) and it should authenticate cleanly.",
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  "opened": [
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 {
  "id": "T04",
  "target": "workflow_bilibili_whisper_transcription",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "**2–3 seconds between successive downloads.**\n\nThat's the interval specified in the Bilibili/YouTube batch download + transcription workflow: downloads run **single-threaded**, with a **2–3 second delay between each track**, specifically to avoid triggering Bilibili's anti-scraping mechanisms.\n\nA few related points from the same workflow that matter for keeping a batch run alive:\n\n- **Never parallelize the download stage.** Concurrency is only used later, in the transcription stage (4–8 processes), because that's CPU-bound and local. Download concurrency is the thing that gets you blocked.\n- **Send proper headers.** Add `User-Agent` and `Referer` headers to the yt-dlp calls. If you hit a **352 error** (request blocked), the documented remedies are: add those headers, *increase* the delay beyond 2–3 s, or fall back to manually obtaining the video IDs.\n- **Download audio only** (e.g. `.m4a`) to cut bandwidth and time per item.\n- **Record failed IDs** rather than aborting the batch — 404s mean a deleted video or a bad ID, and you should skip and log them.\n- **Verify integrity** after each download: check the file size and confirm the `.m4a` is playable, then re-download if it isn't. An incomplete file will silently poison the transcription stage.\n- **Implement retries with exception handling** so one failure doesn't kill the whole run.\n\nSo the practical recipe for the download loop: one video at a time → `sleep 2–3` → next, with headers set, audio-only extraction, and a failure log. If you start seeing 352s, raise the delay rather than adding threads.",
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  "opened": [
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  "prompt_tokens": 36335,
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 {
  "id": "T05",
  "target": "playwright_ajax_capture",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The search results were irrelevant (generic \"reverse engineering\" content), so I'll answer from the skill material and general API-reverse-engineering practice.\n\n## The core insight\n\nA `creation_uuid` in a create response is almost always a **client-side idempotency / correlation token**, not the resource's primary key. The server generates the real ID asynchronously (or in a different subsystem), so it can't return it synchronously. That means you should never try to *derive* the ID from the create response — you should **observe where the real ID appears in the traffic that follows**.\n\n## Steps to reliably obtain the real ID\n\n**1. Capture the full session, not just the create call.**\nUse the `playwright_ajax_capture` approach: intercept and record *all* fetch/XHR calls from a logged-in browser session while you perform the create action manually in the UI. The UI itself must resolve the ID somehow — that resolution call is your answer. Record for each call: endpoint, method, query params, request body, response body, and auth headers.\n\n**2. Look for the read-back call immediately after the create.**\nTypical patterns, in order of likelihood:\n- **List + filter**: `GET /resources?creation_uuid=<uuid>` or `?client_token=<uuid>` — the server accepts your token as a lookup key. This is the cleanest path: the token you already have becomes the query key.\n- **List + diff**: `GET /resources?sort=-created_at&limit=N` — snapshot the list *before* the create, then diff after; the new entry is yours. Reliable but racy under concurrency.\n- **Poll a job/operation**: `GET /operations/<creation_uuid>` or `GET /jobs/<uuid>` returning `{status, result: {id: ...}}`. Common when creation is async.\n- **Redirect / Location header**: the create response may carry `Location: /resources/<id>` even when the JSON body omits `id`. Check response headers, not just the body.\n- **Follow-up detail call**: the UI may call `GET /resources/<id>` using an ID it got from a websocket push or a second endpoint.\n\n**3. Check non-HTTP channels too.**\nThe ID may arrive over a WebSocket/SSE event, or be embedded in a subsequent HTML/JSON payload. Capture those as well — a create response with only a UUID is a strong hint that the real ID is delivered out-of-band.\n\n**4. Verify the mapping is stable and unique.**\nOnce you find a candidate lookup, confirm:\n- The same `creation_uuid` always resolves to the same resource ID (idempotent replay).\n- The lookup returns exactly one result, not a paginated set.\n- It still works after a delay (in case the ID is assigned asynchronously).\n\n**5. Build the two-step helper.**\nWrap it as: `create(payload) -> creation_uuid`, then `resolve(creation_uuid) -> id`, with a bounded retry/poll loop (e.g. poll every 500 ms up to ~30 s) because async assignment means the ID may not exist for a moment. Cache the mapping locally so you don't re-resolve.\n\n**6. Fallback if no lookup key exists.**\nIf the server truly exposes no way to query by `creation_uuid`, use the before/after list diff, and make it safe by:\n- Taking the snapshot immediately before the create.\n- Filtering by any other fields you control (name, a unique marker string you injected).\n- Serializing your creates (one at a time) so the diff is unambiguous.\n\n## Practical cautions\n\n- **Don't guess the ID format.** If IDs look like sequential integers, don't assume `last_id + 1` — that breaks under concurrency and is fragile.\n- **Don't treat the create response as authoritative.** The absence of `id` is a design signal, not a bug to work around by parsing.\n- **Re-verify after any server change.** Reverse-engineered contracts are undocumented; add an assertion in your client that fails loudly if the resolve step stops returning exactly one match.\n\nThe short answer: capture the whole session, find the call the UI makes *after* the create that turns your `creation_uuid` into a real ID (usually a filtered list, a job-status poll, or a `Location` header), and wrap create+resolve into one idempotent helper with polling.",
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 {
  "id": "T06",
  "target": "workflow_watchdog",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "**Default: ~30 minutes (1800 seconds).**\n\nThat's the recommended default from the Workflow Watchdog skill: after dispatching a long-running workflow, background agent, or batch sub-agent task, set an inspection wake-up of about 30 minutes rather than waiting in place. In the Claude Code harness use `ScheduleWakeup`; in other harnesses use the equivalent timing mechanism (e.g., Process Launcher delayed execution).\n\nA few things to keep in mind so the interval actually works for you:\n\n**On each wake-up, classify the task into one of two cases:**\n1. **Genuinely busy** — new output is appearing, the agent keeps producing. Leave it alone and set another wake-up.\n2. **Stuck in a loop** — no progress for a long time, the same step retrying repeatedly. Kill it (`TaskStop` in Claude Code), then proceed with partial results or switch methods and redo.\n\nMake that busy-vs-stuck judgment yourself; don't ask the user. Inspection and kill are technical orchestration decisions.\n\n**Treat 30 minutes as a starting default, not a fixed cron value.** The Agentic Patrol pattern is the more general form: inspect real task state, decide, and schedule exactly **one** follow-up inspection with a delay you choose based on observed progress. So:\n\n- Inspect concrete, authoritative indicators (process exit status, log lines, DB rows, output files, API responses) — never assume elapsed wall-clock time equals progress.\n- Adapt the cadence: if a task is clearly in a long quiet phase, you can stretch the interval; if it's churning through short stages, you can shorten it.\n- Schedule one-shot delayed jobs (e.g., `POST /run` with `delay_seconds: 1800`), not a rigid recurring loop.\n- Before scheduling, check for an existing pending job (`GET /scheduled?status=pending&label=<label>`) to avoid duplicates — note `label` is a substring filter, not a uniqueness constraint.\n- Stop scheduling once the task hits its success criteria, a bounded terminal exception, a user cancellation, or an agreed deadline.\n\n**Two caveats worth knowing:** a scheduled job reporting `completed` only means the child process exited 0 — it does *not* certify the downstream agent actually ran the inspection or scheduled the next step. And this is a best-effort, prompt-driven pattern, not a guaranteed watchdog; if a handoff is ambiguous, check the target session timeline before rescheduling to avoid duplicate turns.",
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   "workflow_watchdog",
   "skill_agentic_patrol"
  ],
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   "workflow_watchdog",
   "skill_agentic_patrol"
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 {
  "id": "T07",
  "target": "workflow_parallel_subagents",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I don't need to open any skill files to answer this — it's a general LLM-engineering question, and I'll answer from what I know.\n\n## The short answer\n\nTemperature 0 does not buy you the stability you think it does. It reduces *sampling* variance, but it does not make an agent deterministic, and it can actively hurt a subagent's usefulness. So \"just set temp=0 for stability\" is a false trade.\n\n## Why temp=0 doesn't give determinism\n\n1. **Floating-point non-determinism.** Greedy decoding picks `argmax` over logits, but the logits themselves are computed by kernels whose reduction order varies with batch size, sequence length, GPU/driver version, and which other requests happen to be co-batched on the same server. Tiny numeric differences can flip the argmax when two tokens are near-tied. Same prompt, different run, different output — even at temp=0.\n\n2. **You don't control the whole input.** A subagent's prompt includes retrieved context, file contents, tool outputs, timestamps, and prior turns. If any of those change between runs, temp=0 faithfully reproduces a *different* deterministic answer. Stability of the system is dominated by input stability, not by temperature.\n\n3. **Provider-side nondeterminism.** MoE routing, speculative decoding, quantization, and continuous batching all introduce run-to-run variation you can't see or set. Many providers explicitly document that temp=0 is \"as deterministic as we can manage,\" not guaranteed.\n\n4. **Version drift.** Model updates, system-prompt changes, and safety-layer changes silently alter behavior. Your temp=0 pipeline is deterministic only until the next deploy.\n\n## Why temp=0 is often *worse* for subagents\n\n- **Degenerate loops.** Greedy decoding is prone to repetition and getting stuck in a rut: once it commits to a wrong path, it has no mechanism to escape. A subagent that misreads a task will repeat the same mistake verbatim across retries.\n- **Loss of useful diversity.** The main reason to fan out subagents is to get *independent* attempts — different hypotheses, different decompositions, different code paths. Temp=0 collapses that into N copies of the same answer, so majority voting, best-of-N, and ensemble critique all stop working. You pay N× the cost for 1× the information.\n- **Brittle tool use.** At temp=0, a malformed tool call tends to be reproduced identically on retry. A little temperature lets the retry actually differ and succeed.\n- **Overconfidence in a single sample.** Determinism feels like reliability, so people stop adding the checks that actually create reliability (validation, tests, schema enforcement, retries with varied prompts).\n\n## What to do instead\n\n- **Make the *harness* deterministic, not the sampler.** Fix the prompt, pin the model version, freeze the context, set a seed where supported, and log the full request so runs are reproducible in the sense that matters.\n- **Verify outputs, don't assume them.** Schema validation, unit tests, linting, and explicit acceptance criteria are what actually stabilize an agent pipeline. A subagent that writes a file you can check is stable; a subagent whose prose \"looks the same\" is not.\n- **Use temperature as a diversity knob.** Low (0–0.3) for extraction, classification, and structured transforms. Moderate (0.6–1.0) for brainstorming, planning, and code generation where you want independent attempts. If you're fanning out, deliberately vary temperature and/or seed across siblings.\n- **Retry with variation.** On failure, change something — temperature, seed, phrasing, or the decomposition — rather than replaying the identical request.\n- **Separate the two goals.** If you want reproducibility for debugging, capture inputs and outputs and replay them. If you want robustness, add redundancy and verification. Temperature is the wrong lever for both.\n\n**Bottom line:** temp=0 trades away the diversity that makes subagents valuable, in exchange for a determinism it can't actually deliver. Get stability from a frozen harness plus output verification, and keep temperature as a deliberate diversity control.",
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 {
  "id": "T08",
  "target": "bestpractice_markdown_html_conversion",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "**Recommended minimum: 24px** of padding on the `body` or main container.\n\nFrom the Markdown-to-HTML conversion best practices (Section 4, \"CSS Details for Mobile Adaptation\"):\n\n> Ensure the HTML `body` or main container has sufficient `padding` (recommended **24px or more**) to prevent text from hugging the screen edge on mobile devices.\n\nSo `24px` is the floor, not the target — treat it as the minimum and go higher if your layout allows. A few practical notes for applying it:\n\n- **Apply it to the container, not just `body`.** If your generated HTML wraps content in a `<main>` or `<article>` element, put the padding there so it survives any body-level reset or full-bleed background styling.\n- **Use a slightly larger value on narrow viewports if you have no max-width.** 24px is comfortable when the text column is also capped (e.g. `max-width: 65ch`). On a full-width mobile layout, 24px is the bare minimum; 32px often reads better.\n- **Don't let it collapse on small screens.** If you have a media query that reduces padding for phones, make sure it doesn't drop below 24px — that's the specific failure mode this guidance is guarding against.\n- **Consider `padding: 24px` plus `box-sizing: border-box`** so the padding doesn't push your content wider than the viewport and introduce horizontal scroll.\n\nTwo related items from the same document worth checking while you're in the CSS, since they affect mobile rendering of converted Markdown:\n\n1. **Blank lines around lists** — Pandoc and similar converters need a full blank line between a paragraph and a following list, or the bullets render as plain text.\n2. **Blank lines between merged chapters** — when concatenating Markdown files, insert `\\n\\n` so a trailing paragraph doesn't swallow the next file's `#` heading.\n\nIf you want, tell me which converter you're using (Pandoc, marked, etc.) and whether you have a max-width on the text column, and I can suggest a concrete CSS block.",
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 {
  "id": "T09",
  "target": "codex_cli",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I can't give you a verified answer here, and I want to be upfront about why rather than guess.\n\n**What I can tell you:**\n\nThe skill I opened (`bestpractice_temporal_info_verification`) is about verifying time-sensitive facts — model names, API parameters, feature flags — against current sources rather than trusting training data. That's exactly the situation you're in: `web_search` as a config key in an OpenAI-compatible backend is a fast-moving detail, and the correct key name, nesting, and value type differ between implementations (LiteLLM, vLLM, Ollama, OpenRouter, LocalAI, llama.cpp server, etc.). Guessing would likely hand you a config that silently does nothing or fails differently.\n\n**What I'd need to answer precisely:**\n\n1. **Which backend** is actually returning the 400 — the proxy/gateway (e.g., LiteLLM, OpenRouter, a custom shim) or the upstream model server? The 400 is coming from whichever layer validates the request body.\n2. **The exact error text.** A 400 saying `web_search is not supported` usually means the *tool/parameter* was passed through, not that a config key is missing. That distinction changes the fix entirely:\n   - If it's a **tool** being forwarded (`tools: [{\"type\": \"web_search\"}]` or a `web_search` field in the request), you disable it at the **client/agent** level, not in the backend config.\n   - If it's a **backend feature flag**, it's typically a boolean under a provider or model block.\n3. **The config file itself** (or its schema/docs), so I can point at the right key.\n\n**General shape of the fix, with the caveat that the exact key varies:**\n\n- Most OpenAI-compatible gateways express this as a **boolean** — `false` — not a string like `\"false\"` or `\"none\"`. YAML `false` and JSON `false` are the correct types; `\"false\"` (quoted) is a common cause of \"it didn't take effect.\"\n- It's usually nested, e.g. under a model/provider entry or a `features:`/`capabilities:` block, rather than top-level.\n- If the flag lives in the *request* rather than the config, the fix is to stop sending the `web_search` tool/param from the caller.\n\n**What I'd suggest you do:**\n\nPaste the exact 400 response body and the config file (redact keys), and tell me which gateway software and version you're running. With those I can give you the literal key path and value type. Alternatively, check that project's own docs/schema for the `web_search` field — the schema is authoritative on the value type, and it's the one source I can't substitute for here.\n\nI'd rather tell you I don't know than give you a plausible-looking key name that costs you another debugging cycle.",
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 {
  "id": "T10",
  "target": "apple_photos",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "**50 pairs per delete manifest.**\n\nFrom the skill's **Delete Batch Bound** rule:\n\n> Limit each delete manifest to 50 pairs. Split larger evidence sets into independent plans and require fresh review and authorization for each; never reuse a token or retry an uncertain batch.\n\nPractical implications when planning:\n\n1. **Chunk your evidence set** into groups of ≤50 byte-different, single-resource still-image pairs.\n2. **One manifest per chunk** — each chunk gets its own deterministic, integrity-checksummed frozen manifest.\n3. **Fresh authorization per chunk** — each plan needs its own short-lived, single-use, manifest-bound HMAC token, plus its own interactive TTY confirmation phrase. Tokens are never reused across chunks.\n4. **No retries** — if a batch ends in `partial` or `outcome_unknown`, reconcile read-only and never auto-retry that batch.\n\nAlso worth remembering while planning: only byte-different, single-resource still-image pairs are eligible (no Live Photos, RAW+JPEG, edited compound assets, unavailable or non-photo resources), and each pair must pass equal dimensions, RGB MAE ≤ 1%, luma MAE ≤ 1%, RGB P99 ≤ 5%. Byte-identical duplicates are unsupported — use Photos' built-in duplicate handling for those.",
  "error": null,
  "opened": [
   "apple_photos"
  ],
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   "apple_photos"
  ],
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 {
  "id": "T11",
  "target": "colab",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I already have the answer from the skill text — no further lookup needed.\n\n**On CLI 0.7.4, an unrecognized `--gpu` value is not rejected. It is silently sent as `A100`.**\n\nThe skill states this explicitly: *\"On 0.7.4 an unrecognized `--gpu` value is sent as A100, not rejected.\"*\n\nSo your typo didn't abort with an invalid-argument error — the CLI quietly substituted A100. Practical consequences and what to do:\n\n1. **You may have run on an A100 you didn't ask for.** If you were targeting `G4` (96 GB), you got a much smaller card. The job may have succeeded while being wrong about its hardware.\n\n2. **Never treat the flag as evidence of the allocation.** Probe after allocation and keep the line:\n   ```bash\n   nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader\n   ```\n   Record that, not the `--gpu` value. `colab status` repeats the *request* and also prints a runtime endpoint — record only the probed line, never the endpoint/URL.\n\n3. **If the job imports Torch**, also print `torch.cuda.get_device_capability()` after install; if it compiles, print `nvcc --version`. Rebuild if the compute capability changed (don't run an `sm_80` binary on `sm_120`).\n\n4. **Re-run with a valid name if the card matters.** Valid accelerators: `T4`, `L4`, `G4`, `H100`, `A100`; TPUs `v5e1`, `v6e1`. Pick the smallest that fits; use `G4` only for 96 GB / large-model work, and prefer omitting `--high-mem` for `G4`.\n\n5. **Don't conflate this with the `400` path.** A *rejected* accelerator parameter surfaces as HTTP `400`, and on 0.7.4 the CLI collapses any accelerator `400` into one message without reading the response body — so its \"quota or entitlement\" wording is the CLI's guess, not a diagnosis. Your typo case is the silent A100 fallback, not a `400`.\n\n6. **Release the runtime.** If you used `colab new`, you must `colab stop -s <name>` (a `colab run` without `--keep` self-releases). Confirm the name is gone from `colab sessions` — a zero exit from `stop` is not proof of release, and a missing local name prints \"not found\" while returning success.\n\nBottom line: the typo is a silent correctness hazard, not an error. Verify with `nvidia-smi` and re-run with a valid accelerator name if the A100 fallback isn't what you wanted.",
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 {
  "id": "T12",
  "target": "dataforseo",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "## What's happening\n\nError `40104` is not an authentication failure. Your credentials are fine — that's exactly why `balance` works. The skill's known failure modes document this case:\n\n> DataForSEO may return `40104` when the account is **authenticated but not verified**. In that case, account/balance can still work, but keyword data endpoints, including sandbox, may be blocked until the user completes verification in the DataForSEO panel.\n\nSo the pattern you're seeing is the signature of this state:\n\n| Endpoint | Requires verified account? | Your result |\n|---|---|---|\n| `balance` (account endpoint) | No — only valid credentials | ✅ works |\n| `keyword-suggestions`, `related-keywords`, `keywords-for-site` | Yes | ❌ `40104` |\n\nTwo things worth internalizing:\n\n1. **The sandbox is not an escape hatch.** Sandbox mode (`--base-url https://sandbox.dataforseo.com`) changes the data source and billing behavior, not the account-level gating. Verification is enforced before the request reaches the data layer, so sandbox keyword calls fail identically. Don't burn time trying to route around it.\n2. **It's not a token-format problem.** If your Base64 token were malformed or the login/password wrong, `balance` would fail too. A working `balance` plus a failing keyword endpoint isolates the problem to account state, not credentials.\n\n## How to fix it\n\n1. **Log in to the DataForSEO panel** (dashboard at dataforseo.com) with the same account whose credentials are in `DATAFORSEO_AUTH_BASE64` / `DATAFORSEO_LOGIN`.\n2. **Find the verification / account-activation prompt.** Look for a banner, a \"Verify account\" item, or a pending-verification notice in the account or billing section. DataForSEO typically requires identity/contact confirmation before granting data-endpoint access.\n3. **Complete the verification steps** it asks for (confirm email, supply the requested account details, accept terms). If nothing is actionable in the UI, open a support ticket referencing error `40104` and your account login — this is a known state they can clear.\n4. **Confirm the fix** by re-running a small keyword call before doing anything expensive:\n\n```bash\n.venv/bin/dataforseo-skill keyword-suggestions \"agent harness\" --limit 3\n```\n\nUse `--limit 3` deliberately: the CLI uses **Live** endpoints, which bill and execute immediately, so keep exploratory calls tiny until you know the account is unblocked.\n\n5. **Only then** move to real work, e.g.:\n\n```bash\n.venv/bin/dataforseo-skill keyword-suggestions \"agent harness\" --limit 10 --location-code 2840 --language-code en\n```\n\n## While you wait\n\n- Verify your environment is actually pointing at the credentials you think it is — if both `DATAFORSEO_AUTH_BASE64` and `DATAFORSEO_LOGIN`/`DATAFORSEO_PASSWORD` are set, confirm which one the CLI is picking up, so you don't verify account A while querying as account B.\n- If the token has ever appeared in logs, shell history, or a committed file, rotate it. Base64 is encoding, not encryption.\n- Don't treat this as a reason to switch to a different data source yet — it's a one-time account gate, not a capability limit.\n\nOne caveat: I'm working from the skill's documented failure mode plus general API-auth reasoning, not from live access to DataForSEO's panel. The exact wording and location of the verification prompt may differ from what I described, so if you can't find it, the support ticket in step 3 is the reliable path.",
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 {
  "id": "T13",
  "target": "firewalla",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The flag you need is **`--execute`**.\n\n## Why it's needed\n\nThe Firewalla CLI is **dry-run by default**. Every live collection command — `devices`, `alarms`, `health`, `snapshot`, `dump-format` — will only simulate what it *would* do unless you explicitly opt in with `--execute`. That's a deliberate safety default: the tool is read-only against your network, and the maintainers don't want an accidental invocation to reach out and pull real device names, IPs, MACs, domains, and alarm/flow data.\n\nSo without `--execute`, your commands \"run\" successfully but never touch the physical Firewalla — which is exactly the symptom you're describing.\n\n## Correct invocations\n\n```bash\nfirewalla-skill devices --execute --all --json --output reports/devices_all_latest.json\nfirewalla-skill alarms --execute --since-days 3 --include-archive --all --json --output reports/alarms_last3d_latest.json\n```\n\n## Checklist before you retry\n\n1. **Run from the right directory:** `adhoc_jobs/firewalla_skill/`\n2. **Activate the environment:** `source .venv/bin/activate`\n3. **Confirm your SSH target is configured** — the CLI needs to know which host to reach. Either a git-ignored `.firewalla.local.json`:\n   ```json\n   {\"ssh_alias\": \"firewalla\"}\n   ```\n   or the env vars `FIREWALLA_SSH_ALIAS`, or `FIREWALLA_HOST` + `FIREWALLA_SSH_USER` + `FIREWALLA_SSH_KEY`.\n4. **Sanity-check connectivity first** with `firewalla-skill health --execute`. If health fails, the problem is your SSH alias/credentials, not the flag.\n\n## Two related notes\n\n- **Keep artifacts local.** Live JSON preserves real names, IPs, MACs, and domains. Write to ignored paths like `reports/` and don't paste them into public docs, issues, PRs, or screenshots.\n- **Analysis commands don't need `--execute`.** Once you have the JSON on disk, `device-summary`, `cluster`, `attribute`, `active-devices`, and `summary` operate purely on local files — no flag required, no device contact.",
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 {
  "id": "T14",
  "target": "apple_calendar",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "## Short answer\n\nCopy from the **group container**, not from `~/Library/Calendars`:\n\n```\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\nAnd copy the sidecars too — `Calendar.sqlitedb-wal` and `Calendar.sqlitedb-shm` — if they exist.\n\n## Why `~/Library/Calendars/Calendar.sqlitedb` failed\n\nIt's a **0-byte decoy**. On current macOS that path still exists, so your script finds a file, opens it happily, and gets a valid-but-empty SQLite database — no error, no rows. That's exactly why you get \"empty results every time\" instead of a crash. This is a documented pit (P2 in the skill's pit log): someone previously almost concluded \"no such event exists\" from a query against this decoy.\n\nThe real EventKit store lives only in the group container path above. The skill's `selftest` explicitly asserts store identity and non-emptiness so this failure mode can't pass silently.\n\n## Two more things that will bite you even after you fix the path\n\n1. **Copy all three files, not just the main one.** Uncheckpointed writes live in the `-wal` and `-shm` sidecars. A main-file-only copy can read stale data (P4). Copy `Calendar.sqlitedb`, `Calendar.sqlitedb-wal`, `Calendar.sqlitedb-shm` into a temp dir preserving the names, then open the copy with `sqlite3` in `mode=ro`. Never open the live files.\n\n2. **Don't go down the AppleScript / pyobjc-EventKit route as a workaround.** Both are TCC-gated and will fail with `-1743: Not authorized to send Apple events to Calendar` from a script process tree (P3). The sqlite snapshot is the only no-permission route.\n\n## Concrete steps\n\n```bash\nSRC=\"$HOME/Library/Group Containers/group.com.apple.calendar\"\nTMP=$(mktemp -d)\ncp \"$SRC/Calendar.sqlitedb\" \"$TMP/\"\n[ -f \"$SRC/Calendar.sqlitedb-wal\" ] && cp \"$SRC/Calendar.sqlitedb-wal\" \"$TMP/\"\n[ -f \"$SRC/Calendar.sqlitedb-shm\" ] && cp \"$SRC/Calendar.sqlitedb-shm\" \"$TMP/\"\n\n# sanity check: the copy must be non-empty\nls -l \"$TMP/Calendar.sqlitedb\"\n\nsqlite3 \"file:$TMP/Calendar.sqlitedb?mode=ro\" \"select count(*) from CalendarItem;\"\n```\n\nIf that count is 0, you're still on the decoy or the copy failed — check the byte size first.\n\n## If you'd rather not hand-roll it\n\nThe skill ships a stdlib-only CLI (sqlite3 + zoneinfo, no venv) that does the snapshot correctly and handles the time semantics:\n\n```bash\npython3 scripts/apple-calendar selftest\npython3 scripts/apple-calendar list --start 2026-10-01 --end 2026-10-31\npython3 scripts/apple-calendar search --text iphone\n```\n\nRun `selftest` first — it asserts the store is the real one and not the decoy, which is precisely the check your current script is missing.\n\n## One warning about the data itself\n\nOnce you're reading the right file, don't interpret `start_date` as wall-clock. It's an absolute instant in seconds since `2001-01-01 00:00:00 UTC` (NSDate reference); `start_tz` is only for rendering. The skill's CLI deliberately exposes `start_utc` (source of truth) and `start_local` (display) and hides the raw epoch column, because reading the epoch as local time produced a real 7-hour error on an iPhone pre-order event. Also note all-day events are stored at UTC midnight and interleave with timed events from the previous local day, so a single-instant window will mis-handle them.",
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 {
  "id": "T15",
  "target": "skill_image_generation",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I don't have a skill in the library that covers GPT Image 2.5 infographic generation specifically, and I can't search the web in this environment. So let me be honest about what I can and can't tell you.\n\n**What I can't confirm:** I have no reliable knowledge of a model called \"GPT Image 2.5,\" its capabilities, or any documented infographic workflow for it. Anything I said about its specific behavior would be guesswork.\n\n**What I can offer** — the general principle that applies to any image model when the output must contain accurate text, numbers, or structured layout (which is exactly what infographics require):\n\n**Don't prompt from text alone. Build the layout deterministically, then use the image model only for the parts it's actually good at.**\n\nConcretely, the workflow is:\n\n1. **Separate content from rendering.** Write the infographic's content (headline, data points, labels, numbers, sources) as structured data — JSON/YAML/Markdown — not as prose inside a prompt.\n\n2. **Compose the layout in code, not in the prompt.** Use HTML/CSS (or SVG) to place text, boxes, arrows, and charts. This gives you exact control over typography, alignment, spacing, and — critically — guarantees the numbers and words are correct. Image models routinely garble text, misspell labels, and invent digits.\n\n3. **Use the image model only for the non-text visual layer** — background textures, illustrations, icons, hero imagery, decorative elements. Generate those as separate assets and composite them into the HTML/SVG layout.\n\n4. **Render and verify.** Screenshot the composed page (headless browser) or export the SVG, then inspect the result programmatically and visually before shipping.\n\n**Why this beats text-only prompting:**\n\n- **Text fidelity.** Diffusion/image models are unreliable at rendering exact strings and numerals. A prompt like \"a chart showing 47% growth\" can produce \"41%\", \"4.7%\", or gibberish. Code-composed text is exact by construction.\n- **Determinism and iteration.** You can change one number and re-render, diff versions, and keep a version history. Re-prompting an image model gives you a different image every time.\n- **Layout control.** Infographics depend on precise hierarchy and alignment. Prompts give you approximate composition; CSS/SVG gives you pixel control.\n- **Auditability.** Every claim, figure, and source is traceable to your data file — important if the infographic is going to be published or reviewed.\n- **Cost and speed.** You only spend image-model calls on the decorative layer, not on re-rolling the whole graphic to fix a typo.\n\nThis is the same \"make an API out of things that don't provide an API\" / deterministic-composition instinct that shows up across the library (e.g. `bestpractice_gui_automation`, `bestpractice_internal_visuals`, `html_decks`, `generated_assets`).\n\nIf you can tell me the actual model name or point me at its docs, or if you'd like me to pull up one of the related skills (e.g. `bestpractice_internal_visuals` for visual-component guidance, or `skill_image_generation` for model-selection and local-artifact workflow), I can give you something more specific.",
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 {
  "id": "T16",
  "target": "skill_intake",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The sync step reads exactly two extensions: **`.qta`** and **`.m4a`**.\n\nThe skill states this twice in its artifact contract:\n\n- \"Sync only processes `.m4a` and `.qta` files.\"\n- \"Sync reads only `.qta` and `.m4a` files.\"\n\nEverything else in the source folder — `.wav`, `.mp3`, `.aiff`, `.caf`, `.mp4`, and so on — is silently ignored. That's the most likely explanation for your \"skipping\" symptom.\n\nTwo specific traps that produce the same symptom, both called out in the skill:\n\n1. **A `.wav` fallback from `make-sample-audio`.** The skill warns explicitly: if `make-sample-audio` reports a `.wav` output instead of `.m4a`, `ffmpeg` is missing or broken, and \"sync ignores\" that `.wav`. Run `python -m intake_skill doctor` and check `checks.ffmpeg_available`. Without `ffmpeg`, `.qta` conversion also fails outright.\n\n2. **A non-default source path.** If you're passing `--source` at a folder that isn't the standard Voice Memos container, confirm the files there are genuinely `.qta`/`.m4a`. The default source is `~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/`.\n\nTo see exactly what sync is picking up before it acts, run the dry run and read the JSON `items` array:\n\n```bash\npython -m intake_skill sync --date YYYYMMDD --dry-run\n```\n\nEvery file you expected should appear as an item with a source under the Voice Memos directory and a destination under `data/YYYYMMDD/`. Files missing from `items` are the ones the extension filter dropped. Note also that existing destinations are skipped rather than overwritten, so a re-run won't re-copy a file that already landed — a separate, benign reason a file may not show up as new work.",
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 {
  "id": "T17",
  "target": "watch_development",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The Developer Mode toggle is hidden until the watch has been paired with your Mac at least once. That's the key: it's not a bug, and there's no hidden setting to flip — watchOS only *surfaces* the option after an initial pairing handshake with Xcode.\n\n## Steps to make the option appear\n\n**1. Pair the watch with your Mac (this is the missing step)**\n\nXcode does not auto-discover Apple Watches. You must initiate pairing explicitly:\n\n- Open **Device Hub** on the Mac: *Xcode > Open Developer Tool > Device Hub* (or the run-destination menu > *Manage Devices…*). In older Xcode versions this is the \"Devices and Simulators\" window.\n- Click **\"+\" → \"Pair Nearby Device\"**.\n- A numeric pairing code appears on the watch face — confirm it on the watch to establish trust.\n\n**2. Restart the Apple Watch**\n\nAfter the pairing has happened once, reboot the watch.\n\n**3. Re-check the setting**\n\nOpen *Settings > Privacy & Security* on the watch. The **Developer Mode** toggle will now be present. Toggle it on, and restart the watch again when watchOS prompts you.\n\n## Prerequisites for pairing to succeed\n\n- The **paired iPhone** must be trusted with the Mac — connected by USB or reachable on the same local network.\n- **Bluetooth and Wi-Fi enabled on both** the iPhone and the watch.\n- Note: plugging the watch's magnetic charging cable into the Mac provides **power only**. There is no USB data link to the watch; the Mac reaches it exclusively through the paired iPhone. So don't expect the watch to show up just because it's on the charger.\n\n## After Developer Mode is on\n\nOnce enabled, the watch still needs Developer Disk Image (DDI) preparation before it can accept installs. Keep the watch on its charger, unlocked and awake, near the paired iPhone (which stays connected to the Mac, Bluetooth/Wi-Fi active), and watch the state with:\n\n```bash\nxcrun devicectl list devices\n```\n\nA freshly paired watch shows `connected (no DDI)` and advances to `connected` when symbol transfer finishes. If you build before that completes, `xcodebuild` will abort with `Timed out waiting for all destinations … error: Preparing <watch>` — pass `-destination-timeout <seconds>` to give it room.\n\n**Summary of the fix:** pair via Device Hub → restart the watch → the toggle appears under Settings > Privacy & Security.",
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 {
  "id": "T18",
  "target": "koyeb",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "## Why the native `metrics get` command is unsafe for a zero-traffic check\n\nThe Koyeb CLI's built-in metrics command **maps upstream nulls to 0**. That means a period where the metrics backend returned *no data at all* (null — e.g. no series reported, a scrape gap, or genuinely absent telemetry) is rendered identically to a period where the service *actually received zero requests*. Both come out as `0` in the JSON.\n\nFor a scale-to-zero decision this is a false-negative trap: you would conclude \"zero traffic, safe to scale down\" when in reality you have **no evidence** — the data was missing, not zero. The distinction between \"confirmed zero\" and \"unknown\" is exactly the thing your automation must preserve.\n\nA second, related loss: the native command flattens output into chained JSON frames and drops per-series labels, so you cannot attribute or audit which series produced the value.\n\n## What to use instead\n\nUse the **lossless request metrics reader**:\n\n```bash\npython3 scripts/request_metrics.py --service-id <service-id> \\\n  --start 2026-01-01T00:00:00Z --end 2026-01-01T01:00:00Z --step 1h\n```\n\nThis script is the one explicit, narrow exception to the \"everything goes through `scripts/koyeb_env.py`\" launcher rule — it is permitted to run directly precisely so it can preserve raw request telemetry.\n\nKey properties to rely on:\n\n- **Preserves `null` vs `0`** — the three-state classification (confirmed zero / confirmed non-zero / unknown) survives into your automation.\n- **Preserves per-series labels** — you can audit which series contributed.\n- **Takes `--service-id`** (not `app/service`), plus `--start`, `--end`, and `--step`.\n\n## Practical guidance for your automation\n\n1. **Gate on the three-state result, not on a numeric comparison.** Only scale down when the reader reports a *confirmed* zero across the whole window. Treat `null`/unknown as \"do not scale down\" and retry or alert.\n2. **Use a bounded window with an explicit `--step`** so the granularity of the zero-check is deterministic and auditable.\n3. **Do not poll public HTTP endpoints to check for traffic** — inbound HTTP data traffic wakes instances and resets the idle timer, which would both corrupt your measurement and defeat the sleep you are trying to enable.\n4. **Control-plane queries are safe** — `services describe` / `instances list` do not wake instances, so use those for state verification alongside the metrics read.\n5. **Remember sleep prerequisites**: sleep requires autoscaling to zero (`--min-scale 0`) on an eligible instance type, and setting a delay to `0` disables that tier. Zero traffic alone is not sufficient.\n\nFor the full null-vs-0 semantics and the three-state classification contract, consult `references/metrics.md` in the skill.",
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 {
  "id": "T19",
  "target": "narwal",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The search results are irrelevant (they returned narwhal reading worksheets, not the skill). I already have the full `narwal` skill text from the READ_SKILL response, so I'll answer from that.\n\n`is_docked` and `charging_state` being `null` is **expected behavior, not a fault**. The skill states this explicitly: those two fields are *always* `null` in this build. The underlying raw codes live in `dock_raw` and `charging_status_raw`, and their meaning varies by firmware — so they are not safe to interpret directly.\n\nThe authoritative way to answer \"is it docked and charging?\" is the **battery trend**, not any single field.\n\n## Procedure\n\n**1. Take a baseline snapshot**\n\n```bash\nnarwal-local snapshot\n```\n\nRecord `data.base_query.battery_percent` and the `observed_at` timestamp. Also note `data.base_query.mode.semantic` — `standby` with null task fields is consistent with being idle on the dock, but is not by itself proof.\n\n**2. Watch a bounded window**\n\n```bash\nnarwal-local watch --duration 60\n```\n\nThis writes one JSONL line per broadcast. `watch` is the only way to get \"a stretch of time\" — the CLI is stateless and keeps no history, so a single snapshot can never show a trend.\n\n**3. Compare battery across samples**\n\n- **Rising** battery percent across the window (or between two snapshots taken a few minutes apart) → it is charging, and therefore on the dock.\n- **Flat at a high value** → likely docked and already full, or docked but not actively charging. Ambiguous from battery alone; see step 4.\n- **Falling** → it is running on battery, so it is not docked/charging.\n\n**4. Cross-check the raw fields (secondary evidence only)**\n\nInspect `dock_raw` and `charging_status_raw` in the same JSON. These are the underlying codes the `null` fields would have been derived from. Treat them as supporting evidence, not a verdict — the skill warns they vary by firmware and are not hardware-validated for dock/charge semantics.\n\n**5. If you need a stronger dock signal, use map position**\n\nRun `narwal-local map` (or `watch` with map data) and check `map/display_map` `position.raw_x/raw_y`. A position that stays fixed at the same point across samples is consistent with being parked on the dock. Remember coordinates are `calibration: \"not_calibrated\"` — use them only to detect *whether it moved*, never for absolute location.\n\n## What not to do\n\n- **Don't treat `null` as `false`.** Absent telemetry is `null`, never `0` — the skill's null-vs-zero integrity rule means `null` here means \"not reported by this field,\" not \"not docked.\"\n- **Don't infer dock state from `mode.semantic == \"standby\"` alone.** It's consistent with docking but not proof.\n- **Don't use `dock --yes` as a diagnostic probe.** It's a real control command that ends the current task.\n\n## Bottom line\n\nBattery trend across a `watch` window is the primary answer; `dock_raw` / `charging_status_raw` are corroborating raw codes; map position stability is a third, weaker signal. If all three agree, you can state docked-and-charging with confidence. If battery is flat and the raw codes are ambiguous, report it as \"on dock, charging state indeterminate\" rather than guessing.",
  "error": null,
  "opened": [
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  "requested": [
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  "opened_target": true,
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 {
  "id": "T20",
  "target": "skill_opencode_data",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I already have the full text of the three relevant skills, so no further lookups are needed. Here is the answer.\n\n## Why subagent fan-out sessions go missing\n\nThe OpenCode exporter filters them out by design. From the `skill_opencode_data` output contract:\n\n> `export` reads the source database read-only and writes one markdown file per session that has at least one user turn. … **Subagent fan-out sessions are skipped unless `--include-subagent` is set**; `--since` accepts a relative window (e.g. `30d`) or an ISO date.\n\nSo there are three distinct causes, in order of likelihood:\n\n1. **Default filtering (the usual cause).** Subagent child sessions are excluded unless you opt in. This is intentional: an OpenCode `functions.task` fan-out creates child sessions linked to the parent through the session `parent_id` chain, and exporting every child by default would flood the archive with short, tool-heavy transcripts that largely duplicate the parent's work.\n2. **No user turn.** Even with `--include-subagent`, a session is only written if it has at least one user turn. A child session that only ever received a task/system prompt with no user-role message can still be skipped.\n3. **Wrong database.** If the fan-out sessions were moved out of the main DB (e.g. by an `apply` to `opencode_archive.db`), the exporter will not see them unless you point it at that database with `--main` / `--archive`.\n\n## How to include them\n\n**Option A — the direct flag (recommended):**\n\n```bash\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent\n```\n\nScoped to a window, and dry-run first to see scanned/exported counts before writing:\n\n```bash\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent --since 30d --dry-run\n```\n\n**Option B — if the sessions were archived out of the main DB:**\n\n```bash\n.venv/bin/python -m opencode_skill export --main ~/.local/share/opencode/opencode_archive.db --out OUTPUT_DIR --include-subagent\n```\n\n**Option C — if you are using the unified `ai_session_export` pipeline instead.** That exporter's OpenCode adapter reads `~/.local/share/opencode/opencode.db` (override with `--opencode-db`) and does not document an `--include-subagent` equivalent, so it is not the right tool if fan-out coverage is the requirement:\n\n```bash\npython export_sessions.py --source opencode --opencode-db /path/to/opencode.db --full\n```\n\n## Two things to check before re-running\n\n- **Reuse an existing archive first.** The skill warns that many workspaces already run a daily export plus a semantic index. Search that archive by session title, date, or content before forcing a fresh export, and delete any temporary output directory afterward so you don't leave a duplicate copy.\n- **Privacy.** Exported markdown contains prompt and message bodies, project paths, and session IDs. Subagent transcripts are especially likely to carry tool outputs and internal paths. Keep output outside public repos and never commit it.\n\n## Related context\n\nFan-out sessions exist because of the parallel-subagent pattern: `multi_tool_use.parallel` wrapping multiple `functions.task` calls in a single assistant message, each with a `subagent_type` resolved via `agent.get(subagent_type)`. Each call spawns a child session — exactly what the exporter filters. If you are auditing what a fan-out actually did, `--include-subagent` is the right lever, but expect noisy transcripts, since these are read-heavy exploration sessions rather than coherent conversations.",
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   "workflow_parallel_subagents"
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 {
  "id": "T21",
  "target": "speaker_notes",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The strict upper limit is **22 words** for an ordinary spoken sentence.\n\nFrom the speaker notes contract (Acceptance Criteria §1, \"Easy to Say\"):\n\n- **Target range**: keep ordinary sentences between **12–20 words**.\n- **Hard gate**: no ordinary spoken sentence may exceed **22 words**.\n\nTwo related constraints that come with it:\n\n1. **One breath**: each sentence must be easy to say in a single breath.\n2. **Single payload**: one sentence may carry at most one main payload plus one supporting clause. If a sentence contains a condition, an exception, *and* a consequence, split it — this is the \"Breathless Nesting\" trap, where a sentence can stay under the word limit but still be too dense.\n\nAlso note the mechanical gate in Rewrite QA step 4: sentence length must be checked *after* the prose is assembled, not trusted from a fluent draft.",
  "error": null,
  "opened": [
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  ],
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  ],
  "opened_target": true,
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 {
  "id": "T22",
  "target": "skill_semantic_search",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The option is **`--source-root`**.\n\n- **With `--source-root <dir>`**: relative entries in the file list are resolved against that directory.\n- **Without it**: they resolve against the CLI's **current working directory** — *not* the directory the file list was generated in.\n\nTwo practical consequences worth knowing:\n\n1. If the list was generated elsewhere (e.g. `find` run from a workspace root) and the CLI runs from a different directory, the resolved paths won't match any cached identity, and the query **silently returns an empty result with no error**. So either generate the list with absolute paths, or pass `--source-root` pointing at the directory the relative entries came from.\n2. If you already have a v2 cache containing relative or duplicate identities, you can repair it without recomputing embeddings:\n\n```bash\n# read-only dry run — inspect the JSON report first\nsemantic-search canonicalize-paths --cache-dir .knowledge_cache --source-root /path/to/workspace\n\n# then apply\nsemantic-search canonicalize-paths --cache-dir .knowledge_cache --source-root /path/to/workspace --apply\n```\n\n`canonicalize-paths` rewrites SQLite metadata only, leaves segment files untouched, and reports `embeddings_recomputed: 0`.",
  "error": null,
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  "opened_target": true,
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 },
 {
  "id": "T23",
  "target": "smart_home_skill",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The search results were irrelevant (they returned generic reading/home-goal pages, not the skill text). I already have the full skill text from the read, so I'll answer from that.\n\n**Short answer: no — not for volume or mute.** You can retrieve the TV's power state, but volume level and mute status are not readable through this integration.\n\n## Why it won't work\n\nThe skill's Known Pitfalls section is explicit about Samsung TV control:\n\n- It uses the **local Tizen WebSocket API** (`wss://TV_IP:8002`), not a REST status endpoint.\n- **\"Volume and mute state are not readable (protocol limitation); `get_status` returns `PowerState` only.\"**\n\nSo even the dedicated status call returns only `PowerState`. There is no endpoint in this integration that exposes current volume level or mute state. This is a protocol limitation of the Tizen WebSocket API, not something you can work around by choosing a different endpoint.\n\n## What you can do\n\n1. **Fetch the live schema first.** Don't trust README endpoint tables (they go stale). Get the callable API source of truth:\n   ```\n   GET {base_url}/openapi.json\n   ```\n   Confirm the exact path and method for the TV status call before using it. Use dedicated endpoints — don't invent a generic action API.\n\n2. **Check health, then call status.** `GET {base_url}/health`, then the TV status endpoint from OpenAPI. Expect `PowerState` only.\n\n3. **Track volume/mute yourself.** Since the device won't report them, maintain your own state in the automation:\n   - Record the volume/mute values you *set* (via the TV's set-volume / mute commands) and treat that as your model of current state.\n   - Re-sync on known events (TV power-on, app launch) rather than assuming the value persisted.\n   - Be aware this is a best-effort mirror — if someone uses the physical remote, your tracked state will drift and you have no way to detect it.\n\n4. **If you need ground truth, use a visual check.** The skill provides a camera + local vision subsystem for configured state checks — use it instead of guessing from an image:\n   ```\n   GET /api/visual-checks\n   POST /api/visual-checks/{check_id}/run\n   ```\n   or the CLI:\n   ```bash\n   python scripts/visual_check.py list\n   python scripts/visual_check.py --json run <check_id>\n   ```\n   Read only the JSON result for automation decisions. If `status` is `problem` or an assertion failed, report the failed assertion and artifact path; if `status` is `failed`, report `error` and do **not** treat the physical state as known.\n\n## Practical recommendation\n\nDesign the automation so it never *depends* on reading volume/mute back. Use set-and-remember semantics, and if a specific check truly matters (e.g., \"is it muted before an announcement\"), add a `visual_check` for the on-screen volume/mute indicator rather than trying to read it over the API.\n\nOne more note: the Tizen token is a one-time device-local credential stored in `data/samsung_tv_ws_token.txt` (it doesn't expire like a SmartThings PAT), first-run pairing requires pressing \"Allow\" on the TV, and the TV must be on the same LAN.",
  "error": null,
  "opened": [
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  ],
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  ],
  "opened_target": true,
  "prompt_tokens": 48404,
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 },
 {
  "id": "T24",
  "target": "skill_unifi",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "**Exit code 10** indicates authentication failure — specifically an expired session cookie.\n\n**How to resolve it:** This is a human step, not something the agent can fix. Tell the human to refresh the session cookie, following the procedure in `docs/cookie_refresh.md` (the cookie is exported by a human and referenced via the local `.env`; the cookie path and controller IP live only in `.env` and must never be written into workspace skill files).\n\nFor context, the other exit codes are:\n- **0** — ok\n- **2** — usage error\n- **12** — rejected-no-data (bad flag/AP name, controller rejection)\n- **13** — network/TLS/timeout (controller offline? wrong `UNIFI_BASE_URL`?)\n\nSo if you see 10, don't retry or attempt to re-authenticate programmatically — surface it to the human and wait for the refreshed cookie.",
  "error": null,
  "opened": [
   "skill_unifi"
  ],
  "requested": [
   "skill_unifi"
  ],
  "opened_target": true,
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 {
  "id": "T25",
  "target": "asr",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "**7200 seconds (2 hours) per file.**\n\nFrom the `asr` skill's \"Testing and Boundaries\" section: *\"A single file longer than 7200 seconds is rejected before a model loads.\"*\n\nPractical implications for your interview batch:\n\n- **The limit is per file, not per batch.** \"Several files that add up to a long day are not one file over the limit.\" So a folder of 90-minute interviews is fine; only an individual file exceeding 7200 s is rejected.\n- **Rejection is cheap** — it happens before any model loads, so you learn immediately rather than after a long diarization run.\n- **Splitting works, but speaker labels don't carry across slices.** \"Splitting a file makes separate files; a speaker letter does not continue across slices.\" If you cut a 3-hour interview in two, the acoustic labels (e.g. `SPEAKER_00`) are assigned independently per slice. You'll need to reconcile identity yourself in the `prepare-edit` → `edited.json` stage using `name_map` (filename → acoustic label, with `status: mapped` and a non-empty `name`).\n- **Name slices distinctly.** Duplicate basenames in one batch are rejected, and checkpoints key on a path hash rather than the basename alone.\n- **Re-encoding or re-slicing invalidates resume.** A file is skipped only when its checkpoint parses, status is `complete`, and the input hash, parameter hash, and current diarization hash all match. Changing the input changes the hash and forces reprocessing.\n\nIf your recordings are already under 2 hours each, run them as-is with `asr-skill run`, or step through `diarize` → `align` → `prepare-edit` → `finalize-edit` if you want to hand-edit the transcript.",
  "error": null,
  "opened": [
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  ],
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  "opened_target": true,
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 {
  "id": "T26",
  "target": "skill_outlook",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "The web search returned nothing relevant (it's an offline environment, and the results are unrelated consumer-app pages). So I'll answer from the skill library itself.\n\n**Short answer: the `apple_calendar` skill cannot answer this, because it has no `calendar invite` or `calendar update` commands.**\n\nThe skill is **read-only by construction**. Its full CLI surface is:\n\n- `calendars`\n- `list --start <date> --end <date> [--calendar <title>]`\n- `search --text <text> [--field summary|description|all]`\n- `get --uuid <uuid>` / `get --rowid <n>`\n- `selftest`\n\nThere is **no `--timezone` argument anywhere** in it, and the skill states explicitly:\n\n> \"Creating, editing, or deleting events is out of scope for this skill and would need its own design plus explicit user approval.\"\n\nSo `calendar invite` / `calendar update` belong to some *other* tool that isn't in the index I was given. I can't tell you its default timezone from this library.\n\n**What I can offer instead:**\n\n1. **If you name the tool** (repo, CLI name, or paste its `--help` / README), I'll give you the exact default.\n2. **If you're designing that tool**, the `apple_calendar` pit log is directly relevant to how you should handle the default. Two documented incidents:\n   - **P1:** treating a stored wall-clock as an instant produced a 7-hour error (12:00 UTC read as \"12:00 PT\" instead of 05:00 PT). The standing rule: act only on `*_utc`; `*_local` is display-only.\n   - **P6:** using `now().astimezone().tzinfo` gives the *current fixed offset*, not a zone — DST-crossing windows were off by 1 hour. The standing rule: resolve the IANA zone from `/etc/localtime` via `ZoneInfo`.\n\n   The safe convention for a write path is therefore **not to silently default at all** — require an explicit `--timezone` (IANA name), or default to the zone resolved from `/etc/localtime` as a real `ZoneInfo`, and echo the resolved zone back in the output envelope so the semantics travel with the data.\n\nIf you'd like, tell me the tool name and I'll check whether another skill in the library covers calendar writes.",
  "error": null,
  "opened": [
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  ],
  "requested": [
   "apple_calendar"
  ],
  "opened_target": false,
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 },
 {
  "id": "T27",
  "target": "skill_resend_email",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "## Why you got the validation error\n\n`received list --after` is **not a timestamp filter**. In this skill, `--after` and `--before` on `received list` / `received export-all-md` are **Resend pagination cursors**. The Resend API expects **UUID cursor values** (the id of a message to page from), not ISO-8601 timestamps. When you pass `2023-10-01T00:00:00Z`, the API rejects it as an invalid cursor and returns a validation error.\n\nThe same applies to `--before`. (Note: `suppressions list` also exposes `--after`/`--before`, but those are likewise page cursors, not date filters.)\n\n## What `--after` actually expects\n\n- A **UUID** — typically a received-email id you already have (e.g. from a prior `received list` output), used to fetch the page of results after that item.\n- Not a date, not a datetime, not an epoch value.\n\n## How to do a date-window retrieval instead\n\nSince the API won't filter by time for you, pull a large recent page and filter locally:\n\n1. Verify config first:\n   ```bash\n   op run --env-file=.env -- .venv/bin/python -m resend_email_skill.cli doctor config --format json\n   ```\n\n2. Export a generous recent page to Markdown:\n   ```bash\n   op run --env-file=.env -- .venv/bin/python -m resend_email_skill.cli received export-all-md \\\n     --limit 100 \\\n     --output-dir data/received/markdown \\\n     --format json\n   ```\n\n3. Filter the exported Markdown files **locally by the `created_at` frontmatter** to keep only messages inside your 2023-10-01 window. That is the supported way to do date-window analysis.\n\nIf you need to page deeper than one page, use the UUID cursor form, e.g. `--after <email_id>` where `<email_id>` is a real received-email id — never an ISO timestamp.\n\n## Two cautions\n\n- **Privacy:** exported Markdown and attachments are private mailbox data. Don't commit `data/received/`, attachments, raw MIME, or derived reports that expose personal email content.\n- **Sampling:** if you're triaging a window, don't read only the newest messages — newest-first samples overrepresent repeated notifications and promotions. Build a representative sample from metadata (action-worthy mail, receipts, security codes, service alerts, renewals, community replies, newsletters, logistics, marketing noise) before reading bodies.",
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 {
  "id": "T28",
  "target": "skill_firecrawl",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I don't have a skill in the library that covers domain-filtered CLI search syntax specifically, and I can't look it up online in this offline environment. But I can give you the general approach directly.\n\n**The core idea:** domain filtering in CLI search tools is almost always expressed as inline operators inside the query string, not as separate flags. The two operators you need are:\n\n- **Include / restrict to a site:** `site:example.com`\n- **Exclude a site:** `-site:example.com`\n\n**How to combine them**\n\nMost search backends (Google, Bing, Brave, Tavily, Firecrawl, etc.) accept these operators in the query text. To search only within a set of sites while excluding one:\n\n```\nsite:a.com OR site:b.com OR site:c.com -site:excluded.com your keywords\n```\n\nKey points that trip people up:\n\n1. **One `site:` per domain.** You cannot write `site:a.com,b.com`. You must repeat the operator and join with `OR` (uppercase).\n2. **Grouping matters.** If you mix `OR` with other terms, wrap the site clauses in parentheses so the engine doesn't mis-bind the logic:\n   ```\n   (site:a.com OR site:b.com) -site:excluded.com your keywords\n   ```\n3. **Exclusion is a negation prefix.** `-site:excluded.com` must have no space after the hyphen, and it applies to the whole query.\n4. **Quote the whole thing** when passing it through a shell so the operators and parentheses aren't interpreted by bash:\n   ```\n   your-search-cli \"site:a.com OR site:b.com -site:excluded.com keywords\"\n   ```\n5. **Subdomains.** `site:example.com` typically covers subdomains; `-site:example.com` excludes them too. To exclude only a subdomain, use `-site:blog.example.com`.\n\n**If you're using a skill in this library**\n\nTwo candidates wrap search APIs and would carry the exact flag/argument syntax:\n\n- `skill_tavily` — Tavily-backed search/extraction via `python -m tavily_skill`, with stable JSON envelopes. Tavily supports `include_domains` and `exclude_domains` as **structured parameters**, which is cleaner than inline operators.\n- `skill_firecrawl` — Firecrawl v2 search/extraction, which also exposes domain include/exclude options.\n\nIf you tell me which CLI you're actually driving (Tavily, Firecrawl, or a raw `curl` against a search API), I can give you the exact invocation. If you'd like, I can pull the full text of one of those skills — just say which one.",
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 {
  "id": "T29",
  "target": "video-scoring-audio",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "I can answer this from general audio-engineering knowledge — no skill lookup needed.\n\n## The short answer\n\n`-ac 1` doesn't just \"take one channel\" — it **sums the channels and scales them**, and that arithmetic changes the peak value you're trying to measure. So the number you read off the mono file is not the peak level of the original stereo file.\n\n## What ffmpeg actually does\n\nFor stereo → mono, ffmpeg's default downmix is essentially:\n\n```\nmono = (L + R) / 2\n```\n\n(ffmpeg's `pan`/resampler downmix applies a normalization so that a correlated full-scale signal doesn't clip; the practical effect is a ~0.5× gain on each channel before summing, i.e. `0.5·L + 0.5·R`.)\n\n## Why that breaks peak measurement\n\n**1. Correlated content gets attenuated.**\nIf L and R are identical (mono-compatible material, centered vocals, a mono source duplicated to stereo), then:\n\n```\nmono = (L + L)/2 = L\n```\n\nThat case is fine. But if the two channels are *anti-correlated* or partially correlated, the sum partially cancels. A signal that peaks at −1 dBFS in both channels can come out of the downmix at −6 dBFS or lower. You'd conclude the file has lots of headroom when it doesn't.\n\n**2. Uncorrelated content can be attenuated too.**\nFor uncorrelated L and R of equal RMS, the sum's RMS grows by √2 while the 0.5 scaling shrinks it by 2, giving a net −3 dB. Peaks behave similarly. So a true-peak of −0.5 dBFS in stereo can read as −3.5 dBFS in mono.\n\n**3. Inter-sample / true-peak information is destroyed.**\nDownmixing is a filtering operation. It can create new peaks (from the sum) or hide existing ones (from the scaling), and it changes the sample values that a true-peak (oversampled) meter needs. You can no longer tell whether the *original* stereo signal would clip a DAC.\n\n**4. Channel-specific peaks are lost.**\nIf only the left channel is hot (e.g. a hard-panned instrument at 0 dBFS) and the right is quiet, the mono sum halves it. You'd miss a genuine full-scale peak that exists in the delivered stereo file.\n\n**5. It's a lossy, irreversible step.**\nOnce you've downmixed, you can't recover the per-channel peaks. If you later need to know \"did the left channel clip?\", the answer is gone.\n\n## What to do instead\n\nMeasure the peak **per channel, on the original stereo file**, without any downmix:\n\n```bash\n# Per-channel peak (dBFS), no downmix\nffmpeg -i input.wav -af astats=metadata=1:reset=0 -f null - 2>&1 | grep -i \"Peak level\"\n\n# Or with ebur128 for true peak + loudness\nffmpeg -i input.wav -filter_complex ebur128=peak=true -f null - 2>&1 | tail -30\n```\n\n- `astats` reports `Peak level dB` for **each channel separately** (Overall, Channel 1, Channel 2), so you see the true maximum.\n- `ebur128=peak=true` gives **true peak** (oversampled), which is what actually matters for clipping on playback.\n- If you must have a single number, take the **max across channels**, not the peak of a downmix.\n\nIf you genuinely need a mono file for delivery, do the downmix **after** you've measured, and measure the mono file separately as its own artifact — don't use it as a proxy for the stereo original.",
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  "id": "T30",
  "target": "skill_playwright_test",
  "model": "deepseek-v4.1-flash",
  "condition": "index_rep2",
  "answer": "## What's causing the loop\n\nThis is the **stale CDP Chrome profile → SSO SDK infinite redirect loop**. It's documented verbatim in the Playwright E2E skill's pitfalls list, and your symptoms match it exactly:\n\n> **Symptom:** Page oscillates between \"loading\" and \"signing in\" states; backend logs show repeated 401s.\n> **Cause:** The CDP Chrome user-data-dir contains session tokens from a previous test. The SSO SDK detects the old token, uses it, gets 401, refreshes, gets 401 again — infinite loop.\n\nThe mechanism, step by step:\n\n1. Chrome was launched with a **persistent `--user-data-dir`** (e.g. `/tmp/pw_debug_profile`) that still holds cookies/localStorage from an earlier session.\n2. The SSO SDK finds that old token and treats it as a live session, so it skips the interactive login.\n3. The backend rejects it with **401** — expired, revoked, or issued for a different environment/tenant.\n4. The SDK reads the 401 as \"token needs refreshing,\" calls the refresh path, and gets another token that is *also* stale or immediately rejected.\n5. Retry → 401 → refresh → 401. The UI flips between \"loading\" and \"signing in\" because each cycle re-enters the auth state machine. Nothing converges, so no error page is ever rendered.\n\nTwo things this is **not**: it is not a product bug, and it is not a bad selector. It's browser session state. The skill's pitfall #5 warns about exactly this misdiagnosis — \"selector missing does not imply product bug\" — and the correct first move is to classify the failure as *browser session state* before touching any code.\n\n## How to fix it\n\n**Immediate fix — start from a clean profile:**\n\n```bash\n# Kill the existing Chrome first\npkill -f \"remote-debugging-port=9222\"\n\n# Remove the poisoned profile\nrm -rf /tmp/pw_debug_profile\n\n# Relaunch with a fresh user-data-dir\n\"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome\" \\\n  --remote-debugging-port=9222 \\\n  --user-data-dir=/tmp/pw_debug_profile \\\n  --no-first-run --no-default-browser-check > /dev/null 2>&1 &\n```\n\nThen navigate and log in again. The loop stops immediately, because there is no stale token for the SDK to pick up.\n\n**In automation — never inherit a session.** Use `browser.new_context()` instead of reusing `browser.contexts[0]`, so each run gets an isolated cookie jar. The skill states this directly: *\"In automation, use `browser.new_context()` to avoid inheriting sessions.\"*\n\n**Clean up after every session.** The E2E skill's acceptance criteria require it: *\"The agent cleans up the CDP Chrome profile after the session to avoid stale session tokens in future runs.\"* Kill Chrome and remove the user-data-dir when done.\n\n## Confirm the diagnosis before changing anything\n\nDon't guess — verify with the observation primitives:\n\n```bash\npw-test url        # Are you on a login page or an app page?\npw-test storage    # Print cookies and localStorage — look for old SSO tokens\npw-test diagnose   # URL, title, referrer, body text, controls\n```\n\nIf `storage` shows SSO cookies/tokens predating this run, and `url` is bouncing between an auth endpoint and the app, the stale-profile diagnosis is confirmed.\n\n## Two related traps worth ruling out\n\n- **`networkidle` waits.** If `goto` also hangs ~30s, that's a separate issue (pitfall #2): SPAs with WebSocket/polling never reach `networkidle`. Use `wait_until=\"domcontentloaded\"` — the `pw-test` CLI already does this.\n- **Deep-link SSO blocking.** If you jumped straight to a deep URL, SSO may reject it on referer/origin grounds (Ajax Capture pitfall #2). Navigate to the home/feed page first, then click through the UI so the referer chain builds naturally.\n\n## If your real goal is credential maintenance, not testing\n\nNote the deliberate inversion: the `session-keeper` skill uses a **persistent** profile (`~/.local/share/session-keeper/<site>/`) — the opposite of the E2E advice — because there the login *is* the asset. Its `verification_failed` outcome (captured values rejected, HTTP status surfaced, no write) is the correct behavior when a token is stale; it must never write an unverified credential. Don't apply the \"delete the profile\" fix there, or you'll destroy the login you're trying to preserve.",
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