[
 {
  "id": "T01",
  "target": "bestpractice_pdf_to_markdown",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "## Cause of the crash\n\nOn Apple Silicon, Docling's **RT-DETR V2 model** calls `torch.float64` during the **layout detection** phase. The **MPS backend does not support `torch.float64`**, so the entire PDF conversion crashes.\n\n## Workaround flag\n\nPass **`--device cpu`** to run Docling on CPU directly:\n\n```bash\ndocling input.pdf --to md --device cpu --output output_dir\n```\n\nFor text-only PDFs that don't need OCR, add `--no-ocr` to reduce extra model paths and false recognition:\n\n```bash\ndocling input.pdf --to md --device cpu --no-ocr --output output_dir\n```\n\n## Important notes\n\n- **Don't rely on `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:\n\n```bash\npython rules/skills/pdf_to_markdown_cli.py vlm-ocr input.pdf \\\n  --output output.md \\\n  --model qwen/qwen3.5-35b-a3b \\\n  --timeout 300 \\\n  --format json\n```\n\nThis requires LM Studio running locally (default `http://127.0.0.1:1234`) with a vision model loaded.",
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 {
  "id": "T02",
  "target": "mac_universal_clipboard",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Yes — the Keychain item you're looking for is **`handoff-own-encryption-key`**.\n\n## What it is\n\nThis is the Mac's local Handoff identity key. Deleting it forces macOS to regenerate a fresh Handoff encryption identity, which resets the local Continuity/Handoff state that can go stale (especially after macOS updates or on beta builds). This is the step to try **before** the last resort of signing out of iCloud.\n\n## How to delete it\n\n1. Open **Keychain Access** (Applications → Utilities → Keychain Access, or search with Spotlight).\n2. In the left sidebar, select the **login** keychain (and optionally **iCloud**, if present).\n3. In the search field, type `handoff-own-encryption-key`.\n4. Select the matching item and delete it (right-click → **Delete**, or press Delete).\n5. Enter your admin password / authenticate when prompted.\n6. **Restart Handoff** so the key regenerates. The cleanest way is to toggle Handoff off and on again in **System Settings → General → AirDrop & Handoff**, or simply restart the Mac.\n\n## Where this fits in the escalation order\n\nPer the skill, the recommended sequence when the fast fix (rewriting `ClipboardSharingEnabled` and restarting `useractivityd`/`sharingd`/`pboard`) doesn't work is:\n\n1. Toggle Handoff off/on in **System Settings → General → AirDrop & Handoff** (and on iOS/iPadOS in **Settings → General → AirPlay & Handoff**). — *You've already done this.*\n2. Boot the Mac into **Safe Mode** once, then restart normally. This clears system caches and has historically restored broken Continuity state after macOS updates.\n3. **Delete the Keychain item `handoff-own-encryption-key`**, then restart Handoff. ← *This is your next step.*\n4. **Sign out of / back into iCloud** — treat this as the last resort, since it can trigger iCloud Drive, Photos, and Keychain resync.\n\n## One important caveat\n\nIf you're running a **macOS beta build**, assume this may be an OS regression. The skill advises that after the first reset fails on a beta, you should avoid repeatedly applying heavier local changes when the symptoms match a known beta Continuity bug — in that case, waiting for a subsequent beta/update may be the more sensible path than continuing to escalate.\n\nSo: try deleting `handoff-own-encryption-key` and restarting Handoff. If that still doesn't fix it and you're on a beta, hold off on the iCloud sign-out and consider it a likely OS regression.",
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 {
  "id": "T03",
  "target": "openreview",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Based on the skill document, here's the specific fix:\n\n**Delete the cached token file and retry once.**\n\n```bash\nrm tmp/openreview_token.json\n```\n\nThen re-run your original command. The CLI will perform a fresh login and cache a new token.\n\n### Why this works\nThe CLI caches your authentication token to a temp file (e.g., `tmp/openreview_token.json`) to avoid repeated logins, since OpenReview enforces a **rate limit of 3 logins per time window**. When that cached token expires, the API returns **HTTP 403**. Removing the stale file forces the CLI to log in again and write a fresh token.\n\n### Important caveats\n- **Retry only once.** Because of the 3-logins-per-window limit, repeatedly deleting and retrying can itself trigger a rate-limit lockout. If a single retry doesn't clear the 403, wait for the window to reset rather than looping.\n- **Verify credentials if it persists.** If a fresh login still fails, check that `openreview_email` / `openreview_password` are set correctly in `.env` (or the `OPENREVIEW_EMAIL` / `OPENREVIEW_PASSWORD` environment variables), since a bad credential would also produce an auth failure.\n- **Confirm the token path.** The exact cache path may vary by setup; if `tmp/openreview_token.json` doesn't exist, look for the token file the CLI actually created (check the `tmp/` directory) and delete that one.\n\n### Quick recovery sequence\n```bash\n# 1. Remove the expired token\nrm tmp/openreview_token.json\n\n# 2. Re-run your query (example)\npython tools/openreview_cli.py papers ICLR.cc/2024/Conference -o papers.json\n```\n\nIf it succeeds, the CLI returns normal JSON (or `{\"status\":\"ok\",...}` when using `-o`).",
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  "target_pos": 0,
  "prompt_tokens": 1945,
  "completion_tokens": 399,
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 },
 {
  "id": "T04",
  "target": "workflow_bilibili_whisper_transcription",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Based on the workflow document, you should maintain a **2–3 second interval** between successive downloads.\n\nHere's the relevant guidance from the skill document:\n\n**Download phase best practices:**\n- **Single-threaded** downloading (one audio track at a time — do not parallelize downloads)\n- **2–3 second delay** between each download\n- Add **User-Agent / Referer headers**\n- Download **audio-only**\n- **Record failed IDs** for retry\n\n**Why this matters:**\nThe document explicitly notes that download concurrency should be single-threaded specifically to \"avoid triggering Bilibili anti-scraping mechanisms.\" If you hit a **352 error**, that's the signal that requests are being blocked — the recommended fixes are to add User-Agent/Referer headers, increase the delay, or manually obtain the video IDs.\n\n**Quick checklist for the download stage:**\n1. Extract video IDs first (watch for 352 errors from the Bilibili API)\n2. Download audio one by one, single-threaded\n3. Wait 2–3 seconds between each download\n4. Include proper headers\n5. Log any failed IDs (352 = blocked, 404 = deleted/invalid)\n6. Verify file integrity (check size; re-download if the `.m4a` is unplayable)\n\nNote that this delay applies to the **download** stage only. The **transcription** stage is different — it's CPU-intensive and should use multi-process concurrency (4–8 processes) to utilize multiple cores.",
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  "target_pos": 0,
  "prompt_tokens": 1983,
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 },
 {
  "id": "T05",
  "target": "playwright_ajax_capture",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The search results are irrelevant to your question (they're about physical/software reverse engineering in general, not internal API ID resolution). I'll answer from the skill document, which directly covers this exact scenario.\n\n# Getting the Real ID After a POST That Only Returns `creation_uuid`\n\nThis is a documented pitfall in the Playwright Ajax Capture workflow (**Pitfall #7**). Some internal APIs return an **async processing token** (`creation_uuid`) rather than the resource ID — the real ID is generated server-side after processing completes. Using `creation_uuid` as if it were the ID silently breaks downstream calls (e.g., a thread reply becomes a standalone message because `parent_message_id` ends up `None`).\n\n## The reliable pattern: POST → GET → match by content\n\nDon't trust the POST response for the ID. Instead:\n\n1. **POST** the resource (create the post/comment/message).\n2. **Immediately GET the list** (or the relevant collection) via the same internal API.\n3. **Find the just-created resource by content matching** — match on the body/text you just submitted (plus recency, e.g. the newest matching item).\n4. **Extract the numeric `id`** from that list record and use it for all subsequent actions.\n\nThis is exactly the pattern used by existing translation-bot projects: after sending a message, immediately fetch messages and locate the new message's numeric ID by content match.\n\n## Concrete steps\n\n```python\n# 1. Create the resource\nresp = requests.post(create_url, headers=headers, json=payload)\n# resp.json() may only contain {\"creation_uuid\": \"...\"} — do NOT use this as an ID\n\n# 2. Fetch the collection\nlisting = requests.get(list_url, headers=headers).json()\n\n# 3. Match by content (and take the newest match to avoid collisions)\ntarget_text = payload[\"body\"]  # or whatever field holds the content\nmatches = [item for item in listing[\"records\"] if item.get(\"body\") == target_text]\nnew_id = matches[0][\"id\"]  # newest-first ordering assumed; verify ordering\n\n# 4. Use the real ID\nrequests.post(reply_url, headers=headers, json={\"parent_message_id\": new_id, ...})\n```\n\n## Important caveats\n\n- **Verify list ordering.** If the list isn't newest-first, sort by `created_at`/timestamp before picking the match, or match on a unique field (e.g., a nonce you embedded in the content) to disambiguate.\n- **Confirm the ID is real.** The skill notes the resource ID is generated after server-side processing — so if the GET doesn't yet show your resource, add a short retry/poll loop (a few hundred ms) before matching.\n- **Don't infer the ID from `creation_uuid`.** Pitfall #9 warns that even when the API echoes `parent_message_id` correctly and `fetch_replies` finds the reply, the browser UI's thread panel won't show it if you replied to the wrong parent — because you used a bogus ID. Always confirm the parent's real ID via API first.\n- **When verifying in the UI**, open the thread panel for the *same* message you replied to (the one whose real ID you fetched), not a different one.\n\n## Acceptance check\n\nPer the skill's acceptance criteria: *\"If a newly created resource's ID is needed, POST then GET to confirm the ID (don't use `creation_uuid` as ID).\"* So the POST→GET→content-match loop is the sanctioned approach — build it into your CLI/verification script so every create operation resolves its real ID before any dependent call runs.",
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  "n_skills": 1,
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  "prompt_tokens": 34691,
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  "cost": 0.016116850000000002,
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 },
 {
  "id": "T06",
  "target": "workflow_watchdog",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The recommended default inspection wake-up interval is **about 30 minutes (1800 seconds)**.\n\nHere's how to apply it, per the Workflow Watchdog skill:\n\n**1. Right after dispatching**\nSet the wake-up immediately — don't wait for something to go wrong. In the Claude Code harness, use `ScheduleWakeup` with a 1800s delay. In other harnesses, use the equivalent timing mechanism (e.g., Process Launcher delayed execution).\n\n**2. On wake-up, check the task status and branch:**\n\n- **Genuinely busy** — new output has appeared and the agent keeps producing. Leave it alone; either wait or set another wake-up.\n- **Stuck in a loop** — no progress for a long time, the same step retrying repeatedly. Kill it (`TaskStop` in Claude Code), then proceed with whatever partial results exist, or switch methods and redo.\n\n**3. Make the judgment yourself**\nDeciding \"genuinely busy vs. stuck\" is your call — don't ask the user. Inspection and killing are technical orchestration decisions, consistent with the self-execution contract, and should be carried through to the end.\n\n**Key principle:** Treat \"set an inspection after dispatching\" as the default action, not a remediation you reach for only after things go wrong. The skill was generalized from a session where a dispatched subprocess got stuck and the AI waited in place, wasting the entire interval.\n\nOne note: 1800s is the *default* — you can adjust it to something more reasonable for the specific task if you have good reason (e.g., a task you expect to finish in a few minutes).",
  "error": null,
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  "sec": 6.2843403816223145,
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  "nudged": false
 },
 {
  "id": "T07",
  "target": "workflow_parallel_subagents",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The skill document gives a direct answer to this. Here's the reasoning, with the steps you can follow.\n\n## The core reason\n\nSetting `temperature: 0` \"for stability\" is a false economy. Many providers and models already have their own sampling strategies, and forcing determinism on long text payloads can actually **cause repetition** — the opposite of stability.\n\nFrom the skill's Notes section:\n\n> Do not default to setting `temperature: 0` for agents \"for stability.\" Many providers/models have their own sampling strategies; DS4 also does deterministic handling for protocol structures, but forcing determinism on long text payloads may cause repetition. Default to not setting temperature; let the provider/server use their own defaults. Only configure when you explicitly know a specific model needs fixed sampling.\n\n## Why this happens\n\n1. **Providers already handle determinism where it matters.** Some models (e.g., DS4) do deterministic handling for protocol structures on their own. You don't need to override that.\n2. **Greedy decoding on long text degrades.** With temperature 0, the model always picks the highest-probability token. On long generations this makes it prone to loops and repeated phrases, because there's no sampling diversity to escape a degenerate path.\n3. **You may fight the provider's own strategy.** Overriding a model's tuned sampling defaults can make behavior worse, not more predictable.\n\n## What to do instead\n\n1. **Default to not setting `temperature` at all.** Let the provider/server use their own defaults.\n2. **Only set it when you have a specific, known reason** — i.e., you explicitly know a particular model needs fixed sampling for your use case.\n3. **Don't confuse \"stability\" with \"determinism.\"** If your real goal is reliable subagent output, get it from:\n   - Specific, well-scoped prompts (the skill stresses prompt quality).\n   - File-first handoff with checkable artifacts and acceptance criteria.\n   - Cross-validation across agents rather than forcing identical sampling.\n\nSo the short answer: temperature 0 doesn't buy stability — it can cause repetition on long outputs, and it overrides sampling strategies the provider already tuned. Leave temperature unset unless you have a concrete, model-specific reason to fix it.",
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 {
  "id": "T08",
  "target": "bestpractice_markdown_html_conversion",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Based on the Markdown to HTML conversion best practices, the recommended minimum padding for the `body` or main container is **24px or more**.\n\nThis is covered in the skill document under **\"4. CSS Details for Mobile Adaptation → Spacing Management\"**, which states:\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\n**How to apply it directly:**\n\n```css\nbody {\n  padding: 24px;\n}\n```\n\nOr, if you're styling a main content container instead of the body:\n\n```css\n.main-container {\n  padding: 24px;\n}\n```\n\n**A couple of practical notes:**\n\n- **24px is the floor, not a ceiling.** On larger phones or tablets you can comfortably increase this (e.g., 32px) for a more breathable layout. Just don't go below 24px.\n- **Watch for double padding.** If you set padding on both `body` and an inner container, the values stack. Pick one place to apply it, or use a smaller value on the inner element.\n- **Consider box-sizing.** If you add padding to a container with a fixed `width: 100%`, use `box-sizing: border-box` so the padding doesn't cause horizontal overflow:\n\n```css\n.main-container {\n  box-sizing: border-box;\n  width: 100%;\n  padding: 24px;\n}\n```\n\nThat single rule is the key takeaway from the guidance: **24px minimum padding on the body or main container** to keep text from clinging to the screen edges on mobile.",
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  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 1105,
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  "sec": 6.296238899230957,
  "cost": 0.00037095,
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 {
  "id": "T09",
  "target": "codex_cli",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "# Disabling `web_search` for a Non-OpenAI Backend\n\n## The Problem\n\nCurrent Codex CLI builds declare a `web_search` tool definition on **every turn**. If your OpenAI-compatible backend doesn't implement that tool, the provider rejects the entire request with:\n\n```\nHTTP 400: the web_search tool is not supported\n```\n\n## The Fix\n\nSet `web_search` as a **bare top-level key** in your config file:\n\n```toml\nweb_search = \"disabled\"\n```\n\n### Critical details\n\n1. **Value type must be the string `\"disabled\"`** — not a boolean, not another string.\n   - On Codex CLI **0.156.1**, only the string unit variant `\"disabled\"` parses.\n   - `false`, `on`, `off`, and `enabled` are **all rejected** with the error:\n     ```\n     expected string only in 'web_search'\n     ```\n\n2. **Placement matters — keep it above every `[section]` header.**\n   TOML binds a bare key to the *preceding* table. If you put `web_search = \"disabled\"` after a `[section]` header, it becomes a key *inside* that table instead of a top-level key, and the setting won't take effect.\n\n   ✅ Correct:\n   ```toml\n   web_search = \"disabled\"\n\n   [some_section]\n   key = \"value\"\n   ```\n\n   ❌ Wrong (binds into the section):\n   ```toml\n   [some_section]\n   key = \"value\"\n\n   web_search = \"disabled\"\n   ```\n\n## Where to Put It\n\nYou can set it in either location:\n\n- **Base config** — `$CODEX_HOME/config.toml`\n- **The profile file the run loads** — profile files layer *over* the base config, so a profile-level setting will override the base.\n\n## Verification\n\nAfter editing, run a cheap one-shot to confirm the 400 is gone:\n\n```bash\ncodex exec --skip-git-repo-check --sandbox read-only --color never \\\n  -c approval_policy=\"never\" \\\n  -o /tmp/last_message.txt \\\n  \"Reply with the single word: ok\"\n```\n\nIf the run completes with exit code 0 and `/tmp/last_message.txt` contains `ok`, the `web_search` tool is no longer being declared and your backend is accepting the request.\n\n## Quick Reference\n\n| Item | Value |\n|---|---|\n| Key | `web_search` (bare, top-level) |\n| Value | `\"disabled\"` (string only) |\n| Rejected values | `false`, `on`, `off`, `enabled` |\n| Placement | Above all `[section]` headers |\n| Valid locations | Base config or the loaded profile file |",
  "error": null,
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  "prompt_tokens": 2484,
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  "sec": 8.352221012115479,
  "cost": 0.0007308,
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 },
 {
  "id": "T10",
  "target": "apple_photos",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "**50 pairs per delete manifest.**\n\nPer the `apple-photos` 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\nA few related constraints worth keeping in mind when you split a larger set:\n\n1. **Fresh review and authorization per plan** — each split manifest needs its own review and its own authorization token. Tokens are single-use, short-lived, and bound to a specific action, manifest, snapshot, count, expiry, nonce, and CLI major version.\n2. **Never reuse a token** and never retry an uncertain batch. If a batch ends in `partial` or `outcome_unknown`, treat it as a transaction failure and reconcile read-only.\n3. **Eligibility still applies per pair** — byte-different, single-resource still images only. Reject Live Photos, RAW+JPEG or edited compound assets, unavailable resources, and non-photo resources. Required thresholds: equal dimensions, RGB MAE ≤ 1%, luma MAE ≤ 1%, RGB P99 ≤ 5%. Byte-identical duplicates are unsupported (use Photos' built-in duplicate handling instead).\n4. **Evidence must be replayed** — `evidence compare-images` needs candidate and keeper PhotoKit local identifiers, and only a fully passing, byte-different report authorizes planning.\n5. **Stay at the machine during dispatch** — delete dispatch has no client deadline because `performChangesAndWait` may block on the macOS Photos confirmation window. If the process is interrupted, the outcome is unknown; do not retry.\n\nAlso note this is a **live-unverified alpha**: mutation commands should only be run against explicitly approved disposable test libraries, never a production System Photo Library.",
  "error": null,
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  "prompt_tokens": 2451,
  "completion_tokens": 382,
  "sec": 8.339643001556396,
  "cost": 0.00059685,
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  "nudged": false
 },
 {
  "id": "T11",
  "target": "colab",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "On CLI 0.7.4, an unrecognized `--gpu` value is **not rejected** — it is silently sent as **A100**.\n\nThat's the trap noted in the skill: \"On 0.7.4 an unrecognized `--gpu` value is sent as A100, not rejected.\"\n\nSo your typo doesn't abort with an invalid-argument error; the request goes out as an A100 allocation request. Practical consequences:\n\n- You may get an A100 (or a `400`/`403`/`503` HTTP class) rather than the card you meant to ask for.\n- Don't infer anything from the flag you passed. After allocation, probe the actual hardware and record it:\n  ```bash\n  nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader\n  ```\n  A successful accelerator request is not that line — the probed line is the evidence.\n- If the job imports Torch, also print `torch.cuda.get_device_capability()` after install, and rebuild any compiled binary if the compute capability differs from what you built for (e.g., don't run an `sm_80` binary on `sm_120`).\n- If you get a `400`, don't read it as a proven entitlement/quota denial — on 0.7.4 the accelerator `400` message doesn't read the response body, and the same `400` can be a rejected parameter or shape.\n\nFix: re-issue with a valid accelerator name (`T4`, `L4`, `G4`, `H100`, `A100`; TPUs `v5e1`, `v6e1`), and verify with the probe rather than trusting the flag.",
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  "target_pos": 0,
  "prompt_tokens": 5789,
  "completion_tokens": 373,
  "sec": 7.4892590045928955,
  "cost": 0.0010921499999999998,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T12",
  "target": "dataforseo",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "## What's happening\n\nError `40104` is not an authentication failure — your credentials are fine (which is why `balance` works). It means **the account is authenticated but not verified** in the DataForSEO panel.\n\nThis is documented as a known failure mode in the skill:\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 split behavior you're seeing is expected:\n\n| Endpoint | Works with unverified account? |\n|---|---|\n| `balance` (account endpoint) | ✅ Yes |\n| `keyword-suggestions`, `related-keywords`, `keywords-for-site` | ❌ No — blocked with `40104` |\n\nNote that the sandbox does **not** bypass this. Sandbox mode (`--base-url https://sandbox.dataforseo.com`) changes where requests go, but the verification gate is applied at the account level, so sandbox keyword calls are blocked too.\n\n## How to fix it\n\n1. **Log in to the DataForSEO panel** (the web dashboard at dataforseo.com) with the same account whose credentials you're using in `DATAFORSEO_AUTH_BASE64` / `DATAFORSEO_LOGIN` + `DATAFORSEO_PASSWORD`.\n2. **Complete account verification.** Look for a verification / activation prompt or banner in the dashboard (typically email/identity or billing verification). Follow the steps it presents.\n3. **Confirm verification status** in the panel — the account should show as verified/active rather than pending.\n4. **Re-run a small keyword query** to confirm the gate is lifted:\n\n```bash\n.venv/bin/dataforseo-skill keyword-suggestions \"agent harness\" --limit 3 --location-code 2840 --language-code en\n```\n\nKeep the limit small for this first check — the CLI uses Live endpoints, so exploratory calls cost money and you want a cheap confirmation.\n\n5. If it still returns `40104` after verification, re-check that the credentials in your environment belong to the *verified* account (a common cause is having two accounts, e.g. a sandbox/test login and a production login, and verifying the wrong one).\n\n## Things that are *not* the cause\n\n- **Bad Base64 token** — that would fail `balance` too.\n- **Sandbox vs. Live** — switching base URLs won't help; the block is account-level.\n- **`keyword_difficulty` vs `competition` confusion** — unrelated; those are just different metrics.\n\n## While you wait\n\nIf verification is slow, you can still do useful prep work that doesn't need keyword endpoints:\n\n- Assemble your **seed list** from first-party data (GSC top queries, high-reading GA4 pages, article titles, internal project names) — the skill explicitly recommends starting from first-party signals rather than blank keyword mining.\n- Write your **project context document** (site URL + goals) so recommendations are scored against your actual objective function once data flows.\n- Draft the **decision categories** you'll assign per opportunity (`refresh`, `cluster`, `new_article`, `ignore`).\n\nThat way, the moment verification clears, you can run the keyword commands and immediately merge the third-party estimates with your first-party evidence.",
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 {
  "id": "T13",
  "target": "firewalla",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "You need to add the `--execute` flag.\n\n**Why it's needed:** Live collection in the Firewalla skill is **dry-run by default**. Without `--execute`, the CLI won't actually query your physical Firewalla device — it just simulates the operation. You must explicitly opt in with `--execute` whenever you intend to hit the real device.\n\n**Your commands should look like this:**\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**A few related reminders from the skill:**\n\n- Run these from `adhoc_jobs/firewalla_skill/` and activate the environment first: `source .venv/bin/activate`.\n- The integration is **read-only** — `--execute` only queries the device; it never writes Redis, changes iptables, or alters Firewalla policies.\n- Keep the resulting artifacts in ignored local paths (like `reports/`). Don't paste them into public docs, issues, PRs, screenshots, or messages, since the JSON preserves real names, IPs, MACs, domains, alarms, and flow fields.\n- If you're not sure the connection works, start with `firewalla-skill health --execute` before running the full collections.\n\nNote that `--execute` is also required for other live commands like `snapshot --execute` and `dump-format --execute`.",
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 {
  "id": "T14",
  "target": "apple_calendar",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "You're hitting the decoy file — this is a documented pit (P2 in the skill's pit log).\n\n## The path you should copy from\n\n```\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\nThat's the real EventKit store. And you must copy the sidecars too, not just the main file:\n\n```\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb-wal\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb-shm\n```\n\n## Why `~/Library/Calendars/Calendar.sqlitedb` fails\n\nIt's a **0-byte decoy** on current macOS. It exists at that path but contains nothing, so:\n\n- Copying it succeeds silently (no error, no permission prompt).\n- Opening it with sqlite3 gives you an empty database — every query returns zero rows.\n- The failure mode is the dangerous kind: it looks like a successful run that legitimately found no events, so you can draw a wrong \"no such event\" conclusion instead of realizing you read the wrong file.\n\n## Steps to fix your script\n\n1. **Point at the group container**, not `~/Library/Calendars/`:\n   ```bash\n   SRC=\"$HOME/Library/Group Containers/group.com.apple.calendar\"\n   ```\n\n2. **Snapshot all three files** into a temp dir, preserving names (P4 — uncheckpointed writes live in `-wal`/`-shm`, so a main-file-only copy can read stale data):\n   ```bash\n   TMP=$(mktemp -d)\n   cp \"$SRC/Calendar.sqlitedb\"      \"$TMP/\"\n   cp \"$SRC/Calendar.sqlitedb-wal\"  \"$TMP/\" 2>/dev/null || true\n   cp \"$SRC/Calendar.sqlitedb-shm\"  \"$TMP/\" 2>/dev/null || true\n   ```\n\n3. **Open the copy read-only** so you never touch the live store:\n   ```bash\n   sqlite3 \"file:$TMP/Calendar.sqlitedb?mode=ro\" \"SELECT count(*) FROM CalendarItem;\"\n   ```\n   A non-zero count confirms you're on the real store.\n\n4. **Sanity-check the copy** before trusting results: if the count is 0, you're either on the decoy or the snapshot was taken mid-write — re-copy and re-run.\n\n## Two related traps to avoid while you're in there\n\n- **Don't retry AppleScript / pyobjc-EventKit** (P3). Both go through macOS TCC and are denied for this kind of process (`-1743: Not authorized to send Apple events to Calendar`). The sqlite snapshot is the only no-permission route.\n- **Don't read `start_date` as wall-clock** (P1). It's an absolute instant in seconds since `2001-01-01 00:00:00 UTC` (NSDate reference), not a local time. `start_tz` is only for rendering. Convert from the instant and show both forms when quoting a time.\n\nIf you'd rather not maintain the copy logic yourself, the skill ships a CLI that already does all of the above (snapshot all three files, read-only open, correct time semantics):\n\n```bash\npython3 scripts/apple-calendar selftest\npython3 scripts/apple-calendar list --start 2026-10-01 --end 2026-10-31\n```\n\n`selftest` explicitly asserts store identity and non-emptiness, so it will catch the decoy for you.",
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 {
  "id": "T15",
  "target": "skill_image_generation",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "# Infographic Workflow with GPT Image 2.5\n\n## The Core Rule\n\n**Do not prompt GPT Image 2.5 from text alone to generate an infographic.** GPT Image is **repaint-only, not fresh generation**. Asking it to produce an infographic from a text prompt with no input image yields unreliable results.\n\n## The Correct Workflow\n\n1. **Build a structure draft first** — produce a matplotlib chart/infographic draft locally. This establishes the layout, title, and annotation text.\n2. **Pass the draft as the input image** using the `-i` flag:\n   ```bash\n   generate-image -p \"Turn this chart into a polished infographic\" \\\n     -i output/draft.png \\\n     -o output/infographic.png \\\n     --model gpt-image-2.5-sunburst \\\n     --aspect-ratio 16:9\n   ```\n3. **Let the model repaint it** — GPT Image 2.5 understands the draft layout, preserves the title and annotation text, and improves the visual presentation.\n\n## Why This Works\n\nBecause the model is repaint-only, it needs an existing visual to work from. The matplotlib draft gives it:\n- The **layout** to preserve\n- The **text content** (titles, annotations) to keep intact\n- A concrete structure to enhance rather than invent from scratch\n\n## Supporting Tips\n\n- **Aspect ratio**: `--aspect-ratio 16:9` suits horizontal infographics.\n- **Timeout**: complex edits like redrawing a matplotlib chart into an infographic routinely need **300s or more**. Set an explicit timeout (e.g. `timeout=300000`, ~5 minutes). Without it, the command produces no output and exits on timeout.\n- **Model choice**: use `gpt-image-2.5-sunburst` (precision-first) for final assets, or `gpt-image-2.5-flare` (speed-first) for fast drafts.\n- **Final delivery format**: if the infographic is embedded in an external Markdown deliverable, it must be a PNG/JPG/WebP produced by a GPT Image model. A matplotlib draft is only an intermediate editable source — it must pass through the GPT Image model before being embedded.",
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 {
  "id": "T16",
  "target": "skill_intake",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Sync reads only two extensions:\n\n- **`.qta`** — Apple Voice Memos' native format (converted via `ffmpeg`)\n- **`.m4a`** — standard audio\n\nAnything else in the source folder is ignored. So if recordings are being skipped, the most likely causes are:\n\n1. **The files aren't `.qta` or `.m4a`.** For example, if `ffmpeg` is missing, `make-sample-audio` falls back to `.wav`, and sync ignores `.wav` entirely. Check `doctor` for `checks.ffmpeg_available: true`.\n2. **The files aren't in the configured source.** The default is `~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/`. If Voice Memos hasn't finished syncing, files won't be there yet.\n3. **The date filter excludes them.** Sync with `--date YYYYMMDD` only processes that day's recordings.\n\nTo diagnose, run a dry run and inspect the JSON `items` array:\n\n```bash\npython -m intake_skill sync --date YYYYMMDD --dry-run\n```\n\nConfirm the sources are under the Voice Memos directory and the destinations are under `data/YYYYMMDD/`. If a recording you expect is missing from `items`, verify its extension is `.qta` or `.m4a` and that it lives in the source path reported by `doctor`.",
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 {
  "id": "T17",
  "target": "watch_development",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The Developer Mode toggle only appears on the watch after it has established an initial pairing with your Mac. If you've never paired the watch with your Mac, the setting won't be there yet. Here's how to make it appear:\n\n## Steps to surface the Developer Mode toggle\n\n1. **Pair the watch with your Mac at least once.**\n   - Open **Device Hub** on the Mac (*Xcode > Open Developer Tool > Device Hub*, or the run-destination menu > *Manage Devices…*).\n   - Click **\"+\" → \"Pair Nearby Device\"**.\n   - Alternatively, you can initiate pairing from the watch itself via *Settings → Privacy & Security → Developer Mode* (the pairing control lives there).\n   - When prompted, a numeric pairing code appears on the watch face — confirm it on the watch to establish trust.\n\n   Note: The watch is reached **through its paired iPhone**, not directly. Make sure the iPhone is paired and trusted with the Mac (USB or same local network), and that Bluetooth and Wi-Fi are enabled on both the iPhone and the watch. The watch's magnetic charging cable provides power only — it is not a data link.\n\n2. **Restart the Apple Watch.**\n   - After pairing, reboot the watch. This is what causes watchOS to surface the Developer Mode setting.\n\n3. **Open Settings > Privacy & Security again.**\n   - The **Developer Mode** toggle should now be present. Toggle it on.\n\n4. **Restart the watch again when prompted.**\n   - watchOS will ask you to restart to apply the change. Do so.\n\n## Why this happens\n\nThe Developer Mode entry is gated on the watch having completed an initial pairing with the Mac. Until that pairing has happened (and the watch has rebooted afterward), watchOS simply doesn't display the option — it's not a bug or a missing feature.\n\n## After Developer Mode is on\n\nOnce Developer Mode is enabled, you'll still need to complete **DDI preparation** before the watch can accept installs. During that phase:\n- Keep the watch on its charger, unlocked and awake, near the paired iPhone.\n- Keep the iPhone connected to the Mac with Bluetooth/Wi-Fi active.\n- Watch for the state to move from `connected (no DDI)` to `connected` in `xcrun devicectl list devices`.\n\nIf you're building from the command line while preparation is still running, pass `-destination-timeout <seconds>` to `xcodebuild` so it doesn't abort with a \"Preparing <watch>\" timeout error.",
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 {
  "id": "T18",
  "target": "koyeb",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "You shouldn't rely on the standard CLI's `metrics get` command for a zero-traffic / scale-to-zero eligibility check because of how it handles missing data:\n\n**Why the native CLI is unsafe for this check**\n\n- The native CLI (`koyeb metrics get ... -o json`) **maps upstream nulls to 0**. That means a period with *no data reported* (null) becomes indistinguishable from a period that genuinely had *zero requests* (0).\n- For scale-to-zero decisions, that distinction is critical: a null could indicate missing/absent telemetry rather than confirmed inactivity, so treating it as \"0 traffic\" can produce a **false-positive zero-traffic classification** and let you scale down when you shouldn't.\n- The skill states this explicitly: *\"Native CLI maps upstream nulls to 0. Always use `scripts/request_metrics.py` when evaluating scale-to-zero eligibility or confirming inactivity.\"*\n\n**What to use instead**\n\nUse the lossless request metrics reader, which preserves the null-vs-0 distinction and per-series labels:\n\n```bash\npython3 scripts/request_metrics.py --service-id example-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 read-only exception to the launcher rule (it's run directly, not through `scripts/koyeb_env.py`), precisely so it can preserve raw null request telemetry.\n\n**Practical notes for your automation**\n\n- Use `scripts/request_metrics.py` for the zero-traffic classification; reserve the native `metrics get` command for quick, non-authoritative inspection only.\n- The reader supports a three-state classification (per the metrics reference), so you can distinguish \"confirmed zero\", \"has traffic\", and \"no data / unknown\" rather than collapsing everything to 0.\n- Remember that sleep also requires autoscaling down to zero (`--min-scale 0`) on an eligible instance type, and that control-plane CLI queries (`describe`, `list`) do **not** wake instances — but inbound HTTP data traffic does. So don't poll public HTTP endpoints while waiting for sleep, as that resets the idle timer and would invalidate your zero-traffic check.\n\nFor deeper detail on the null-vs-0 semantics and the three-state classification, consult `references/metrics.md`.",
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  "prompt_tokens": 3044,
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 {
  "id": "T19",
  "target": "narwal",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "# Determining Docked/Charging State from `narwal-local snapshot`\n\n## Why those fields are `null`\n\nThis is expected behavior, not a bug. Per the skill's telemetry rules:\n\n> **`is_docked` and `charging_state` are always `null`.** Raw codes are in `dock_raw` / `charging_status_raw` and vary by firmware.\n\nThe CLI deliberately does not interpret these fields because the raw codes differ across firmware versions. So you should **not** try to read dock/charge state directly from `is_docked` or `charging_state` — they will never be populated.\n\n## The correct method: use the battery trend\n\nThe skill is explicit about the workaround:\n\n> To tell whether it is docked or charging, use the **battery trend** (rising across a `watch` window or between snapshots), not these fields.\n\n### Option A — Bounded `watch` window (recommended)\n\nRun a live telemetry window and compare `battery_percent` across samples:\n\n```bash\nnarwal-local watch --duration 20\n```\n\n- Each broadcast is one JSONL line.\n- If `battery_percent` is **rising** across the window, the robot is charging (and therefore on the dock).\n- If it is flat or falling, it is not charging.\n\nThis is the only way to get \"a stretch of time\" — there is no background daemon, so nothing exists outside the window.\n\n### Option B — Two snapshots separated in time\n\n```bash\nnarwal-local snapshot\n# wait a few minutes\nnarwal-local snapshot\n```\n\nCompare `data.base_query.battery_percent` between the two runs. A rising value indicates charging.\n\n## Important caveats\n\n1. **A single snapshot cannot answer this.** The CLI is stateless — each invocation sends one query and disconnects, keeping no history. You must observe a trend, not a single reading.\n2. **Do not trust `dock_raw` / `charging_status_raw` blindly.** Those raw codes vary by firmware, so they are not a reliable cross-model signal on their own.\n3. **Quiet telemetry is not proof of sleep or disconnection.** If a `watch` window records nothing, that does not establish that the robot is asleep, offline, or not docked — report the observation rather than guessing its cause.\n4. **A `no_broadcast` warning** in a snapshot means that short window saw no broadcast — **not** that the robot is asleep or offline.\n\n## Quick reference\n\n| Goal | Command | What to look at |\n|---|---|---|\n| Is it charging/docked? | `narwal-local watch --duration 20` | `battery_percent` trend (rising = charging) |\n| Same, without streaming | Two `narwal-local snapshot` runs spaced apart | `data.base_query.battery_percent` delta |\n| Raw (unreliable) codes | `narwal-local snapshot` | `dock_raw`, `charging_status_raw` — firmware-dependent, don't rely on alone |\n\n**Bottom line:** ignore `is_docked` and `charging_state` (always `null` by design), and infer dock/charge state from a **rising battery trend** observed across a `watch` window or between two snapshots.",
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 {
  "id": "T20",
  "target": "skill_opencode_data",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Subagent fan-out sessions are missing because `export` **skips them by default**. Per the skill's output contract:\n\n> Subagent fan-out sessions are skipped unless `--include-subagent` is set.\n\n## Why they're skipped\n\n- `export` writes one markdown file per session that has **at least one user turn**.\n- Subagent fan-out sessions are filtered out of the default run, so they never get a file in the output directory even though they exist in the source database.\n- Note this is a deliberate default, not a bug — it keeps the export focused on top-level sessions.\n\n## How to include them\n\nAdd the `--include-subagent` flag to your export command:\n\n```bash\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent\n```\n\nYou can combine it with the other export options as needed:\n\n```bash\n# Preview first (no files written)\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent --dry-run\n\n# Scoped by time window\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent --since 30d\n```\n\n## Things to check if they're still missing\n\n1. **User-turn requirement** — a session with no user turn is never exported, subagent or not. A fan-out session that only contains assistant/tool output won't produce a file.\n2. **`--since` window** — if you passed `--since`, sessions older than the window are excluded regardless of the subagent flag.\n3. **Wrong database** — confirm the source DB actually contains those sessions by running `stats --json` first, and use `--main` / `--archive` / `--old-archive` if the sessions live in a different database than the default.\n4. **Reuse an existing archive** — before re-exporting, check whether the workspace already maintains a daily-exported session archive; read from it first and only run `export` to force-refresh a session newer than the archive's last run. Delete any temporary output afterward so you don't leave a duplicate copy.\n\n## Reminder\n\nExported markdown contains prompt and message bodies and is **private** — never commit it, and don't paste session titles or content into public logs.",
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 {
  "id": "T21",
  "target": "speaker_notes",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The strict upper limit is **22 words**.\n\nFrom the speaker notes contract, under \"Easy to Say\":\n\n> Each sentence must be easy to say in one breath. Keep ordinary sentences between 12-20 words. **Hard gate**: no ordinary spoken sentence may exceed 22 words.\n\nA few practical notes on applying that gate:\n\n- **It's a hard gate, not a target.** The working range is 12–20 words; 22 is the ceiling you must never cross for an ordinary spoken sentence.\n- **\"Ordinary spoken sentence\" is the scope.** The limit applies to normal podium prose, not to quoted material or slide text.\n- **Length isn't the only check.** A sentence can sit under 22 words and still fail the \"Single Payload\" rule if it nests a condition, an exception, and a consequence. Split at the logical turn instead.\n- **Verify mechanically.** The contract's Rewrite QA step 4 requires running mechanical gates — sentence length, total spoken word count, slide-note alignment, forbidden phrases, and factual qualifiers — after the prose is assembled, rather than trusting a fluent draft.",
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  "target_pos": 0,
  "prompt_tokens": 3443,
  "completion_tokens": 231,
  "sec": 6.524711847305298,
  "cost": 0.00065505,
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 {
  "id": "T22",
  "target": "skill_semantic_search",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The option is `--source-root`.\n\n- **With the option:** relative entries in the file list are resolved against the directory you pass to `--source-root`.\n- **Without the option:** relative entries resolve against the CLI's **current working directory** — *not* the directory the file list was generated in.\n\nThis distinction matters because the CLI canonicalizes every file-list entry to an absolute path before cache access. If the list was generated elsewhere (e.g., with `find` from a workspace root) and you run the CLI from a different directory, the resolved paths won't match any cached identity, and the query will **silently return an empty result with no error**.\n\nTo avoid that, either:\n\n1. Generate the file list with absolute paths, or\n2. Pass `--source-root` pointing at the directory the relative entries were generated from, e.g.:\n\n```bash\nsemantic-search query \\\n  --file-list tmp/files.txt \\\n  --source-root /path/to/workspace \\\n  --cache-dir .knowledge_cache \\\n  --query \"natural-language question\" \\\n  --top-k 10\n```\n\nIf you already have a v2 cache with mismatched relative/duplicate identities, you can repair it with `canonicalize-paths` (dry run first, then `--apply`), which rewrites SQLite metadata only and reports `embeddings_recomputed: 0`.",
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 {
  "id": "T23",
  "target": "smart_home_skill",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "No — you can't retrieve volume level or mute status from the Samsung TV integration's status endpoint. This is a protocol limitation, not a configuration problem.\n\n**Why:** Samsung TV control in this skill uses the local Tizen WebSocket API (`wss://TV_IP:8002`). The Tizen protocol does not expose readable volume or mute state. Per the skill's Known Pitfalls:\n\n> Volume and mute state are not readable (protocol limitation); `get_status` returns `PowerState` only.\n\nSo calling the status endpoint (`get_status`) will return only `PowerState` — you'll get on/off, but no volume level and no mute flag.\n\n**What you can do instead:**\n\n1. **Track state yourself.** Since the protocol is write-only for volume/mute, maintain your own state in the automation (store the last value you set). Treat it as best-effort, not authoritative — it will drift if anyone uses the physical remote or another app.\n\n2. **Set volume/mute blindly.** You can still *send* volume and mute commands; you just can't read them back. Design the automation to issue absolute set commands rather than relative ones (e.g., \"set volume to 10\" instead of \"volume up\"), so you don't depend on knowing the current value.\n\n3. **Don't try to infer it from a camera.** A visual check of the TV screen is not a reliable substitute for reading volume/mute state, and the skill reserves `visual_check` for configured camera-based state checks with deterministic assertions — not free-form guessing at on-screen UI.\n\n**If you need real readback**, you'd need a different control path (e.g., a SmartThings PAT-based integration, if your TV supports it), which is a separate integration from this Tizen WebSocket one. Note the skill also mentions the Tizen token is a one-time device-local credential stored in `data/samsung_tv_ws_token.txt` that doesn't expire like a SmartThings PAT — so these are genuinely two different mechanisms.\n\n**Bottom line:** `get_status` → `PowerState` only. No volume, no mute.",
  "error": null,
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 },
 {
  "id": "T24",
  "target": "skill_unifi",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "**Exit code 10** indicates an authentication failure — specifically an expired session cookie.\n\n**How to resolve it:** This is a **human step**, not something the agent can fix. The human needs to refresh the session cookie by following the procedure in `docs/cookie_refresh.md` (the cookie is exported by a human and stored via the local `.env`). Once the human refreshes the cookie, re-run the command.\n\nA few related notes from the skill's output contract, in case you see other codes:\n\n- **0** — ok\n- **2** — usage error (bad flags)\n- **10** — auth (cookie expired → human refreshes per `docs/cookie_refresh.md`)\n- **12** — rejected-no-data (bad flag/AP name, or controller rejection)\n- **13** — network/TLS/timeout (controller offline? wrong `UNIFI_BASE_URL`?)\n\nSo if you're getting **10**, don't retry blindly or try to re-authenticate programmatically — the tool is read-only by construction and has no login flow. Tell the human to refresh the cookie, then re-run.",
  "error": null,
  "n_skills": 1,
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  "prompt_tokens": 1405,
  "completion_tokens": 236,
  "sec": 4.996870279312134,
  "cost": 0.00035234999999999997,
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  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T25",
  "target": "asr",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "**7200 seconds (2 hours) per single file.**\n\nPer the ASR skill's testing and boundaries section: \"A single file longer than 7200 seconds is rejected before a model loads.\"\n\nA few related details worth knowing 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 you can process many long interviews in one run as long as each individual file is ≤ 7200 s.\n- **Splitting has a caveat.** If you split a long recording into slices, each slice becomes a separate file, and \"a speaker letter does not continue across slices\" — so speaker labels (A, B, C…) will restart/not carry over between slices. You'd need to reconcile speaker identities across the split pieces yourself.\n- **Duplicate basenames are rejected.** If you split files, make sure the resulting filenames are unique within a batch, since \"duplicate basenames in one batch are rejected\" (checkpoints use a path hash, not just the basename).\n- **Empty results are fine.** If a file yields no speech, that's recorded as a completed empty output (`empty_speech=true`) and won't be retried forever — useful if some interview files turn out to be silent.\n\nSo: keep each file at or under 2 hours, and if you must split, plan for speaker-label reconciliation across the slices.",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2508,
  "completion_tokens": 302,
  "sec": 5.0017149448394775,
  "cost": 0.0005574,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T26",
  "target": "skill_outlook",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The default timezone is **UTC** for both `calendar invite` and `calendar update`.\n\nDetails from the skill reference:\n\n- **`calendar invite`**: `--start`/`--end` are wall-clock times interpreted in the `--timezone` zone; the default is `UTC`.\n- **`calendar update`**: same rule — `--timezone` defaults to **UTC** and does **not** inherit the event's existing timezone.\n\nKey cautions:\n\n1. **Never hand-convert local time to UTC.** Pass wall-clock times (no UTC offset allowed — offset-bearing values are rejected) and let the CLI resolve the zone.\n2. **For `calendar update`, be especially careful.** `calendar get` often returns Windows zone names (e.g. `Pacific Standard Time`). If you pass a `get` wall-clock time back without an explicit `--timezone`, a US-Pacific meeting will be rewritten 7–8 hours off. Always pass an explicit `--timezone` when updating times.\n3. **Always cross-check the echoed `start_utc`/`end_utc`** in the JSON output against the intended local time (e.g. \"10/1 6pm PT\" must yield `start_utc: 2026-10-02T01:00:00Z`), and verify with `calendar get` after creation.\n\nSo if you want a non-UTC event, you must explicitly supply `--timezone` (IANA name like `America/Los_Angeles`, or an alias like `PT`/`PST`/`PDT`/`Pacific`/`US/Pacific`, `MT`, `CT`, `ET`, or the Windows name `Pacific Standard Time`).",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 18031,
  "completion_tokens": 725,
  "sec": 8.941719055175781,
  "cost": 0.01313965,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T27",
  "target": "skill_resend_email",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "The validation error happens because `--after` (and `--before`) on `received list` / `received export-all-md` are **Resend pagination cursors, not timestamp filters**. They expect **UUID cursor values**, not ISO 8601 timestamps. Passing `2023-10-01T00:00:00Z` sends a non-UUID string to the API, which rejects it with a validation error.\n\n## What `--after` actually expects\n\n- A **UUID cursor** identifying a position in the paginated result set (typically the `id` of a message you already retrieved).\n- It is used to page through results, not to select a date range.\n\n## How to do a time-window analysis instead\n\nSince the API can't filter by timestamp, export a recent page and filter locally by the `created_at` frontmatter in the exported Markdown files:\n\n1. **Verify config first:**\n\n```bash\nop run --env-file=.env -- .venv/bin/python -m resend_email_skill.cli doctor config --format json\n```\n\n2. **Export a sufficiently large recent page to Markdown:**\n\n```bash\nop 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 locally** by the `created_at` frontmatter in the exported Markdown files to isolate your target window (e.g., messages on/after 2023-10-01).\n\n## Notes and cautions\n\n- Don't pass ISO timestamps to `--after`/`--before` — the API expects UUID cursors and will return a validation error.\n- When sampling for analysis, don't read only the newest messages; newest-first samples overrepresent repeated notifications and promotions. Build your sample from metadata first, then read bodies.\n- Treat exported Markdown and attachments as private mailbox data. Don't commit `data/received/`, attachments, raw MIME, or derived reports that expose personal email content.",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 2596,
  "completion_tokens": 441,
  "sec": 6.691490888595581,
  "cost": 0.000654,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T28",
  "target": "skill_firecrawl",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "You can't do it with the two domain-filter flags at the same time — Firecrawl rejects that combination. Here's the constraint and the workaround.\n\n## The constraint\n\n`--include-domain` and `--exclude-domain` are **mutually exclusive**. Passing both is a usage error (exit 2). This is a Firecrawl API limitation, not a CLI quirk.\n\nSo this will fail:\n\n```bash\n# ❌ Rejected — cannot combine include and exclude domain filters\npython -m firecrawl_skill search \"agent framework\" \\\n  --include-domain github.com \\\n  --include-domain docs.anthropic.com \\\n  --exclude-domain reddit.com\n```\n\n## The workaround: use the `-site:` operator in the query text\n\nTo both target certain sites and exclude another, put the exclusion inside the query string using the `-site:` operator, and keep the inclusion via `--include-domain`:\n\n```bash\npython -m firecrawl_skill search \"agent framework -site:reddit.com\" \\\n  --include-domain github.com \\\n  --include-domain docs.anthropic.com\n```\n\nThe `-site:` operator handles the exclusion, while `--include-domain` (repeatable) handles the targeting. This is the documented approach for exactly this case.\n\n## Notes\n\n- `--include-domain` is repeatable, so list each site you want to focus on as a separate flag.\n- If you only need to exclude (no targeting), just use `--exclude-domain` alone — repeatable for multiple sites.\n- If you only need to target, use `--include-domain` alone.\n- The same mutual-exclusion rule applies to `--time-range` vs. `--start-date`/`--end-date` — use one or the other.\n\n## Reading the results\n\nRemember that `search` writes the full payload to a file and prints only a status object to stdout. After the call, open the file at `output_path` from the status JSON and inspect `data.results[*].raw_content` (markdown by default), along with the URLs and titles, to confirm the domain filtering worked as intended.",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 7024,
  "completion_tokens": 449,
  "sec": 6.31582498550415,
  "cost": 0.001323,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T29",
  "target": "video-scoring-audio",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "Because the downmix changes the number you're measuring — it can make a non-clipping file look like it clips.\n\n**The mechanism:** for correlated stereo (the usual case for a voice-over, where a mono mix is written identically to both channels), summing the two channels adds about **+3 dB**. So a mix whose true peak is **-1.5 dBFS** measures **1.19** after `-ac 1` — above 1.0, and reported as clipping when it isn't.\n\n**What to do instead:**\n\n1. **Measure peak over every channel, before any downmix.** Decode to float and take the max of the absolute value across all channels:\n   ```python\n   xc, sr = load(\"out.wav\")          # keep the channels\n   print(float(np.max(np.abs(xc))))  # peak must stay below 1.0\n   ```\n   Only downmix to mono *after* that, and only for the envelope and spectral-centroid calculations, which don't care about absolute level.\n\n2. **Cross-check with an independent meter.** `ffmpeg -af ebur128=peak=true -f null -` gives you integrated loudness and true peak without a downmix in the path.\n\nThe general rule: peak is a per-channel property, so it has to be measured per channel. Any processing step you insert between the file and the measurement is part of the measurement, and a downmix is a gain change you didn't intend.",
  "error": null,
  "n_skills": 1,
  "target_pos": 0,
  "prompt_tokens": 5725,
  "completion_tokens": 311,
  "sec": 5.87820291519165,
  "cost": 0.00104535,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T30",
  "target": "skill_playwright_test",
  "model": "deepseek-v4.1-flash",
  "condition": "full_1",
  "answer": "This is **Pitfall #1** in the Playwright E2E Test Skill: **a stale CDP Chrome profile causing an SSO SDK infinite redirect loop.**\n\n## What's causing it\n\nThe CDP Chrome `user-data-dir` contains **session tokens from a previous test run**. Here's the cycle:\n\n1. The SSO SDK detects the old (stale) token and tries to use it.\n2. The backend rejects it with a **401**.\n3. The SDK attempts to refresh the token → gets **401** again.\n4. It retries → **401** again → and so on, forever.\n\nThat's why you see the page oscillating between \"loading\" and \"signing in\" while the backend logs a continuous stream of 401s. The old token is being reused, rejected, refreshed, and rejected in an endless loop.\n\n## How to fix it\n\n**For manual/CDP debugging sessions:**\nAlways start Chrome with a fresh profile. Remove the old one first:\n\n```bash\nrm -rf /tmp/pw_debug_profile\n```\n\nThen start Chrome with a clean user-data-dir:\n\n```bash\nchrome --remote-debugging-port=9222 --user-data-dir=/tmp/pw_debug_profile\n```\n\n**For automation (Playwright test scripts):**\nUse a fresh browser context so you don't inherit any prior session state:\n\n```python\nbrowser.new_context()\n```\n\n## Why this matters\n\nA clean profile avoids stale SSO session tokens entirely. This is also why the skill's methodology step 1 says to **start CDP Chrome with a fresh user-data-dir** — and why step 9 (cleanup) says to **kill Chrome and remove the user-data-dir** after the session, so future runs don't inherit stale tokens and hit this same loop.\n\n## Quick diagnostic note\n\nIf you're unsure whether it's this pitfall or something else (e.g., a wrong redirect URI or an auth provider error page), run `pw-test diagnose` before changing any code or adding timeouts — the skill's Pitfall #5 warns that a missing selector or stuck state doesn't automatically mean a product bug. But the specific signature here — oscillation between \"loading\"/\"signing in\" plus repeated 401s — is the textbook stale-profile symptom.",
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  "prompt_tokens": 2700,
  "completion_tokens": 479,
  "sec": 5.029874086380005,
  "cost": 0.0006924,
  "web_tools": [],
  "n_calls": 1,
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  "nudged": false
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]