[ { "id": "T01", "target": "bestpractice_pdf_to_markdown", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the `bestpractice_pdf_to_markdown` skill document, here's the answer:\n\n## Cause of the crash\n\nOn Apple Silicon macOS, Docling's **RT-DETR V2 model** (used during layout detection) calls **`torch.float64`**, which the **MPS backend does not support**. This causes the entire PDF conversion to crash during the layout detection phase.\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, you can also 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 caveats from the skill\n\n- **Don't rely on `PYTORCH_ENABLE_MPS_FALLBACK=1` as the first option.** In practice, the fallback may still not take effect with certain version combinations; `--device cpu` directly 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 (via the CLI's `vlm-ocr` subcommand).\n\nThe skill also notes that the same `DocumentConverter()` instance can be reused across multiple files, and the first call downloads ~1 GB of model weights to the HuggingFace cache.", "error": null, "n_skills": 139, "target_pos": 115, "prompt_tokens": 336477, "completion_tokens": 323, "sec": 8.062883138656616, "cost": 0.050665350000000005, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T02", "target": "mac_universal_clipboard", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Yes — the item is **`handoff-own-encryption-key`** in your login Keychain. Deleting it forces macOS to regenerate the Mac's local Handoff identity key on the next Handoff/Continuity activity, which is the standard escalation step before an iCloud sign-out.\n\n## Steps\n\n1. **Open Keychain Access** (Applications → Utilities → Keychain Access), or run it from Spotlight.\n2. Select the **login** keychain in the left sidebar (not System, not iCloud).\n3. In the search field, type `handoff-own-encryption-key`.\n4. If it appears, right-click it → **Delete** (confirm). If it doesn't appear, try searching just `handoff` — the item may be named slightly differently on your macOS version, or it may not exist yet (in which case there's nothing to reset here and you should move to the next escalation step).\n5. **Restart Handoff**: toggle Handoff off and back on in **System Settings → General → AirDrop & Handoff**.\n6. Test both directions: iPhone/iPad → Mac, and Mac → iPhone/iPad.\n\n## Why this is the right step here\n\nYour symptom pattern — iPhone and iPad still sync with each other, but anything involving the Mac fails — points at stale Mac-side Continuity state rather than an Apple ID or iOS-side problem. The Mac's Universal Clipboard depends on three layers:\n\n- `pboard` — local pasteboard state\n- `useractivityd` — Handoff/Continuity activity state, including the `ClipboardSharingEnabled` preference and shared pasteboard blobs\n- `sharingd` — nearby-device discovery and the sharing transport\n\nSince you've already done restarts and the Handoff toggle, the remaining cheap local reset is the Handoff identity key. Deleting it is far less disruptive than an iCloud sign-out, which can trigger resyncs of iCloud Drive, Photos, and Keychain.\n\n## If you want to try the daemon reset first (or again)\n\nBefore or alongside the Keychain deletion, you can force the three daemons to restart with a clean preference:\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 reappear (launchd restarts them). Then test copy/paste both ways.\n\n## Escalation order if the Keychain deletion doesn't fix it\n\n1. **Safe Mode boot** once, then restart normally — this clears system caches and has historically restored broken Continuity state after macOS updates.\n2. **Delete `handoff-own-encryption-key`** (the step above) if you haven't already.\n3. **iCloud sign-out/sign-in** — last resort, because of the resync side effects.\n\nOne caveat: if you're on a **macOS beta build**, treat this as a possible OS regression after the first reset fails. Don't keep applying heavier local changes when the symptoms match a known beta Continuity bug — report it and wait for a fix rather than repeatedly resetting your identity key or signing out of iCloud.", "error": null, "n_skills": 139, "target_pos": 82, "prompt_tokens": 336491, "completion_tokens": 756, "sec": 12.337419033050537, "cost": 0.05092724999999999, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T03", "target": "openreview", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the OpenReview skill documentation, here's the specific step to resolve an HTTP 403 error caused by an expired token:\n\n**Delete the cached token file and retry once.**\n\nThe skill's \"Known Limitations\" section states:\n\n> **Login rate limit.** At most 3 logins per time window. The CLI already does token caching; normal use will not trigger it. If a 403 occurs due to an expired token, delete `tmp/openreview_token.json` and retry once.\n\nSo the concrete steps are:\n\n1. Delete the cached token:\n ```bash\n rm tmp/openreview_token.json\n ```\n2. Re-run your query (the CLI will perform a fresh login and write a new token to the cache).\n\nA few important caveats from the same skill:\n\n- **Retry only once.** The login rate limit is 3 logins per time window, so don't loop on repeated retries — that will make things worse.\n- **Make sure credentials are actually configured.** The CLI reads `openreview_email` / `openreview_password` from `.env` (or the `OPENREVIEW_EMAIL` / `OPENREVIEW_PASSWORD` environment variables). If those are missing or wrong, deleting the token won't help — you'll just get another auth failure.\n- **403 isn't always an expired token.** The skill frames the delete-and-retry as the fix specifically for the expired-token case. If a fresh login still returns 403, the problem is more likely your credentials or account access, not the cache.\n\nIf you're unsure whether the token is the issue, you can first confirm the CLI is picking up your credentials correctly before deleting anything.", "error": null, "n_skills": 139, "target_pos": 64, "prompt_tokens": 336476, "completion_tokens": 346, "sec": 10.410863876342773, "cost": 0.050679, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T04", "target": "workflow_bilibili_whisper_transcription", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the skill documents provided, the answer is **2–3 seconds between successive downloads**.\n\nFrom the `workflow_bilibili_whisper_transcription` skill:\n\n> **Single-threaded download** → Download audio one by one, 2-3 second interval between each, to avoid triggering anti-scraping\n\nAnd in the \"Best Practices\" section:\n\n> **Download phase:** Single-threaded + 2-3 second delay + User-Agent headers + audio-only download + record failed IDs\n\nThe key decisions table in that same skill also confirms:\n\n| Decision Point | Choice | Reason |\n|----------------|--------|--------|\n| Download concurrency | Single-threaded | Avoid triggering Bilibili anti-scraping mechanisms |\n\nSo the full recommended pattern is:\n\n1. **Single-threaded** — do not parallelize downloads.\n2. **2–3 second delay** between each download.\n3. **Add User-Agent/Referer headers** (the skill notes that missing headers can cause `HTTP 352` errors, which are a sign of being blocked).\n4. **Audio-only download** to reduce load.\n5. **Record failed IDs** so you can retry them later rather than hammering the API.\n\nA couple of related notes from the same skill that are worth keeping in mind:\n\n- If you still hit **352 errors** (request blocked), the skill's remedy is to add User-Agent/Referer headers, increase the delay further, or manually obtain the video IDs.\n- The `download_and_transcribe` skill adds that Bilibili can return **HTTP 412** for generic extractors even on valid links, and that a playurl 412 with valid cookies can mean the risk control is blocking the client fingerprint rather than the cookies — in which case the recovery route is to drive the HTML5 playurl API with `curl` instead of yt-dlp.\n\nSo: **2–3 seconds, single-threaded, with proper headers** is the documented interval.", "error": null, "n_skills": 139, "target_pos": 23, "prompt_tokens": 336470, "completion_tokens": 416, "sec": 11.110040187835693, "cost": 0.0507201, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T05", "target": "playwright_ajax_capture", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The search results are generic reverse-engineering material and don't add anything to the specific API problem. The answer is fully covered by the Playwright Ajax Capture skill's documented pitfall #7, which I already have. Here's the complete answer:\n\n# Getting the Real ID After a POST That Returns Only `creation_uuid`\n\nThis is a known, documented failure mode (Playwright Ajax Capture skill, pitfall #7). Here's the reliable approach.\n\n## Why it happens\n\nSome internal APIs return an **async processing token** (`creation_uuid`) rather than the resource ID. The resource ID is generated server-side *after* processing completes, so it simply doesn't exist yet at response time. If you then pass that non-existent `id` as a parameter in a follow-up request (e.g., `parent_message_id`), the parameter silently becomes `None` and the request behaves unexpectedly — a thread reply becomes a standalone message, etc.\n\n## The reliable fix: POST, then GET and match by content\n\nDo **not** use the response's `id` directly after POST. Instead:\n\n1. **POST** the resource.\n2. **GET the list** (or the relevant collection) immediately after.\n3. **Find the just-created resource by content match** — match on the content you submitted (body text, title, etc.), not on the `creation_uuid`.\n4. Use the numeric/real ID you found for any subsequent action.\n\nThis is the pattern used by existing projects (e.g., translation bots): after sending a message, immediately fetch messages and find the new message's numeric ID by content matching.\n\n## Concrete checklist\n\n- [ ] After POST, issue a GET against the collection endpoint.\n- [ ] Match the new resource by its **content** (the text/title you sent), since `creation_uuid` is not the resource ID.\n- [ ] Extract the real `id` from the GET response.\n- [ ] Only then perform the dependent action (reply, update, delete, thread-link).\n- [ ] If you need to verify in a UI, make sure you open the panel for the **same** message you replied to — a wrong parent ID makes a thread reply invisible even though the API returned success.\n\n## Related traps to watch for\n\n- **Wrong parent ID makes a thread reply invisible**: the API can return `202` and echo `parent_message_id` correctly, `fetch_replies` can find the reply, yet the browser UI's thread panel won't show it — because you sent it to the wrong parent. Confirm the parent's real ID via API first; don't infer it from the POST response's `creation_uuid`.\n- **Don't guess SPA deep-link URL patterns** to capture the \"view single post\" ajax. If the list endpoint already returns full records (including body), just replay that endpoint with plain HTTP — no Playwright capture needed. Guessing URL patterns can 404 and poison the SPA router state.\n- **CSRF tokens can rotate** on page refresh; if a mutation starts returning 403, re-export cookies + CSRF via CDP.\n\n## If you're capturing the flow with Playwright\n\nUse CDP-level `page.on(\"request\")` / `page.on(\"response\")` (not `window.__captured` monkey-patching, which is lost on page refresh). Then replay the POST + GET sequence with plain `requests` using the exported HttpOnly cookies (via `context.cookies()`, not `document.cookie`) and the CSRF token for mutations.\n\nThe short version: **treat the POST response as an acknowledgment, not as the source of the ID. Always follow with a GET and match by content.**", "error": null, "n_skills": 139, "target_pos": 84, "prompt_tokens": 698284, "completion_tokens": 1483, "sec": 12.541000366210938, "cost": 0.1156324, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T06", "target": "workflow_watchdog", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The recommended default wake-up interval is **about 30 minutes (1800 seconds)**.\n\nThis comes from the `workflow_watchdog` skill, which covers exactly this situation — inspecting long-running background sub-agent tasks after dispatch.\n\n## The recommended pattern\n\nAfter dispatching a task, set a reasonable inspection wake-up, **default about 30 minutes (1800s)**. Use `ScheduleWakeup` in the Claude Code harness; in other harnesses use the corresponding timing mechanism (e.g., Process Launcher delayed execution).\n\n## What to do on wake-up\n\nCheck the task status and handle two cases:\n\n1. **Genuinely busy**: new output appears, the agent keeps producing. Leave it alone; wait or set another wake-up.\n2. **Stuck in a loop**: no progress for a long time, the same step retries repeatedly. Kill it (use `TaskStop` in Claude Code), and proceed with the partial results, or switch methods and redo.\n\nThe \"genuinely busy vs stuck\" judgment is yours to make; do not ask the user. This aligns with the self-execution contract: inspection and kill are technical orchestration decisions to be carried through to the end.\n\n## Why this matters\n\nThe skill was generalized from a session where a dispatched subprocess got stuck and the AI waited in place, wasting the entire interval. The lesson: **make \"set an inspection after dispatching\" the default action, not a remediation after things go wrong.**\n\n## A note on your specific setup\n\nSince you mention dispatching *several* long-running tasks, two related points from the skill ecosystem are worth keeping in mind:\n\n- **Don't wait in place.** The whole point of the wake-up is to avoid blocking on a task that may be stuck. Set the wake-up immediately after dispatch.\n- **If you're using Process Launcher for the timing**, note that a scheduled job marked `completed` only means its launched child process exited with code 0 — it does **not** certify that the downstream agent actually executed the inspection. So verify the real task state (logs, output files, session timeline) rather than trusting the scheduler's success status alone.\n\nIf your tasks are expected to run much longer or shorter than the typical case, you can adjust the interval — but 30 minutes is the documented default starting point.", "error": null, "n_skills": 139, "target_pos": 81, "prompt_tokens": 336470, "completion_tokens": 479, "sec": 6.588557004928589, "cost": 0.050757899999999995, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T07", "target": "workflow_parallel_subagents", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The short answer: because forcing determinism on long text payloads can cause repetition, and because many providers/models already have their own sampling strategies that you'd be overriding for no real benefit.\n\nHere's the reasoning, drawn from the parallel subagent workflow guidance:\n\n**1. It can actively hurt long text output.**\nThe guidance is explicit: \"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.\"\n\nSo the failure mode is the opposite of what you're trying to prevent. You set temperature 0 hoping for stable, reliable output, and instead you get degenerate repetition — the model loops on the same phrase or structure. That's a real quality regression, not a stability win.\n\n**2. The \"stability\" you want is usually already handled elsewhere.**\nDeterminism for structured/protocol output (tool-call schemas, JSON envelopes, etc.) is typically enforced by the provider or server layer, not by temperature. DS4, for example, \"does deterministic handling for protocol structures.\" So the parts that genuinely need to be deterministic are already deterministic, and temperature 0 doesn't add anything there.\n\n**3. You'd be overriding provider defaults you don't understand.**\nDifferent providers and models have tuned sampling strategies. Blindly pinning temperature to 0 discards that tuning. The default recommendation is to **not set temperature at all** and let the provider/server use their own defaults.\n\n**4. Only configure it when you have specific knowledge.**\nThe rule is narrow: \"Only configure when you explicitly know a specific model needs fixed sampling.\" That's a deliberate, model-specific decision — not a blanket default applied to every subagent.\n\n**Practical takeaway:** Leave temperature unset by default. If you observe a specific model producing unstable output that you can trace to sampling, then set it deliberately for that model — and watch for repetition as the side effect. Don't reach for temperature 0 as a generic \"make it more reliable\" knob, because on long-form generation it tends to trade one problem for a worse one.", "error": null, "n_skills": 139, "target_pos": 124, "prompt_tokens": 336456, "completion_tokens": 453, "sec": 8.061717987060547, "cost": 0.0507402, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T08", "target": "bestpractice_markdown_html_conversion", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The recommended minimum padding is **24px or more**.\n\nFrom the Markdown-to-HTML best practices:\n\n> **Spacing Management**: 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 apply at least `padding: 24px` to the `body` or your main content container. If you want a bit more breathing room on larger phones, going slightly above 24px (e.g., 24–32px) is fine, but 24px is the stated floor.", "error": null, "n_skills": 139, "target_pos": 121, "prompt_tokens": 336481, "completion_tokens": 124, "sec": 4.58321213722229, "cost": 0.050546549999999996, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T09", "target": "codex_cli", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the Codex CLI skill document, here's the exact fix:\n\n## The Problem\n\nCurrent Codex builds declare a `web_search` tool on every turn by default. OpenAI-compatible backends that don't implement it fail the whole run with HTTP 400 `the web_search tool is not supported`.\n\n## The Fix\n\nSet `web_search` as a **bare top-level key** in your base config (or the profile file the run loads):\n\n```toml\nweb_search = \"disabled\"\n```\n\n## Critical Details\n\n**1. The value must be a string, not a boolean.**\n\nOn Codex CLI 0.156.1, only the string `\"disabled\"` parses. These are all **rejected** with `expected string only in 'web_search'`:\n- `false`\n- `on`\n- `off`\n- `enabled`\n\n**2. Placement matters — keep it above every `[section]` header.**\n\nTOML binds a bare key to the preceding table. If you place `web_search = \"disabled\"` after a `[section]` header, it will be interpreted as a key *inside* that table rather than a top-level key. So put it at the top of the file:\n\n```toml\nweb_search = \"disabled\"\n\n[some_section]\n# ...\n```\n\n**3. Profile files layer over the base config.**\n\nIf your run loads a profile file, you can set it there instead — profile files layer over the base config, so the profile value wins.\n\n## Verification\n\nAfter upgrading Codex, re-verify flags with `codex exec --help` plus a cheap rejection probe, since this CLI churns flags across minor versions (0.144.6 → 0.156.1 removed `--full-auto` and `--ask-for-approval`).\n\n## Why this happens\n\nThe `web_search` tool is declared by default in current builds. When your OpenAI-compatible backend doesn't implement it, the provider rejects the entire request with HTTP 400 rather than ignoring the unknown tool. Disabling it as a bare top-level string key removes the tool declaration from the request entirely.", "error": null, "n_skills": 139, "target_pos": 12, "prompt_tokens": 336481, "completion_tokens": 445, "sec": 5.9550461769104, "cost": 0.05073915, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T10", "target": "apple_photos", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The maximum is **50 pairs** per delete manifest.\n\nFrom the Apple Photos skill's hard boundaries:\n\n> **Delete Batch Bound**: 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\nSo if your evidence set exceeds 50 pixel-verified pairs, you must split it into multiple independent plans — each requiring its own fresh review and authorization (and its own single-use, manifest-bound token).", "error": null, "n_skills": 139, "target_pos": 105, "prompt_tokens": 336461, "completion_tokens": 103, "sec": 4.063473701477051, "cost": 0.05053095, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T11", "target": "colab", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the Colab skill document, here's what happens:\n\n**On CLI 0.7.4, an unrecognized `--gpu` value is sent as A100, not rejected.**\n\nFrom the skill's \"Real path, probed GPU, external deadline\" section:\n\n> On 0.7.4 an unrecognized `--gpu` value is sent as A100, not rejected.\n\nSo instead of aborting with an invalid-argument error, the CLI silently falls back to requesting an **A100** accelerator. This is a silent failure mode — you won't get an error telling you the value was wrong; you'll just get an A100 (or an allocation attempt for one) rather than whatever you intended.\n\n### Why this matters and what to do\n\n1. **Don't trust the flag you passed — probe the actual GPU.** The skill is explicit that a successful accelerator request is not proof of what you got. After allocation, print and keep:\n ```bash\n nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader\n ```\n Record the probed line, not the `--gpu` value you asked for.\n\n2. **Related trap — the `400` message is a guess.** The skill also notes that on 0.7.4, `session.new` (and the same branch in `colab run`) turns any accelerator `400` into one message and does not read the response body. The words \"quota or entitlement\" in that message are the CLI's guess; the same `400` can be a rejected parameter or shape. So don't over-interpret error text either.\n\n3. **A fallback must record the probed GPU, not the flag.** If you retry with a corrected value, verify via `nvidia-smi` that you actually landed on the intended card.\n\nIn short: a typo'd `--gpu` on 0.7.4 doesn't fail loudly — it quietly becomes an A100 request, so always verify the real accelerator with `nvidia-smi` after allocation.", "error": null, "n_skills": 139, "target_pos": 35, "prompt_tokens": 336491, "completion_tokens": 442, "sec": 5.92919397354126, "cost": 0.050738849999999995, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T12", "target": "dataforseo", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the DataForSEO skill documentation, this is a known and documented failure mode.\n\n## What causes it\n\nError `40104` means **the account is authenticated but not verified**. The DataForSEO API distinguishes between two states:\n\n- **Authenticated** — your Base64 login/password (or `DATAFORSEO_LOGIN` + `DATAFORSEO_PASSWORD`) is valid, so account/balance endpoints work.\n- **Verified** — the account has completed DataForSEO's account verification process.\n\nKeyword data endpoints (keyword suggestions, related keywords, keywords-for-site, etc.) require a **verified** account. Until verification is complete, those endpoints are blocked — and this block applies **even to the sandbox** (`https://sandbox.dataforseo.com`), which is why your sandbox attempt also returns `40104`.\n\nThis is exactly why you see the split behavior: `balance` succeeds (it only needs authentication), while keyword queries fail (they need verification).\n\n## How to fix it\n\n1. **Log in to the DataForSEO panel** (the DataForSEO dashboard/console) with the account whose credentials you're using.\n2. **Complete the account verification** there. Verification is a user-side action in the DataForSEO panel — it is not something the CLI can do, and there is no flag or environment variable that bypasses it.\n3. Once verification is confirmed, **re-run the keyword command**. No credential change is needed; the same Base64 token will start working for keyword endpoints.\n\n## What not to do\n\n- Don't rotate or regenerate the API token — the token is fine; the account state is the problem.\n- Don't switch to the sandbox as a workaround — the sandbox is blocked by the same verification gate.\n- Don't assume the CLI is misconfigured — `balance` working is positive evidence that authentication is correct.\n\n## Quick confirmation\n\nYou can verify the diagnosis by checking that `balance` still returns successfully while a keyword call returns `40104`:\n\n```bash\n.venv/bin/dataforseo-skill balance\n.venv/bin/dataforseo-skill keyword-suggestions \"agent harness\" --limit 3\n```\n\nIf that's the pattern you see, it's the verification gate, and the fix is entirely on the DataForSEO panel side.", "error": null, "n_skills": 139, "target_pos": 135, "prompt_tokens": 336481, "completion_tokens": 486, "sec": 7.197346925735474, "cost": 0.050763749999999996, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T13", "target": "firewalla", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The flag you need is **`--execute`**.\n\n## Why it's needed\n\nThe Firewalla CLI is **dry-run by default**. Live collection is intentionally gated so that an agent (or a person) can't accidentally hit the physical device just by running a command. From the skill:\n\n> Live collection is dry-run by default; add `--execute` only when you intend to query the user's Firewalla.\n\nSo without `--execute`, commands like `devices` and `alarms` will parse/validate and print what they *would* do, but never actually open the SSH connection to your Firewalla. That's exactly the symptom you're describing — the command \"runs\" but nothing is queried.\n\n## What to run\n\nAdd `--execute` to the collection commands:\n\n```bash\nfirewalla-skill health --execute\n\nfirewalla-skill devices --execute --all --json --output reports/devices_all_latest.json\n\nfirewalla-skill alarms --execute --since-days 3 --include-archive --all --json --output reports/alarms_last3d_latest.json\n```\n\n## A few things to keep in mind\n\n1. **Run from the right place and environment.** The skill expects you to work from `adhoc_jobs/firewalla_skill/` with the project venv activated:\n ```bash\n cd adhoc_jobs/firewalla_skill/\n source .venv/bin/activate\n ```\n The CLI command is `firewalla-skill`.\n\n2. **`--execute` is only for the live-collection commands.** The local analysis commands (`device-summary`, `cluster`, `attribute`, `active-devices`, `summary`) operate on already-collected JSON artifacts and don't need it. `dump-format` does take `--execute` for local schema inspection.\n\n3. **The integration is read-only.** Even with `--execute`, it only queries the device — it never writes Redis, changes iptables, alters Firewalla policies, or edits service files.\n\n4. **Keep the output private.** The JSON preserves real names, IPs, MACs, domains, alarms, and flow fields, so keep live artifacts in ignored local paths (e.g. `reports/`) and don't paste them into public docs, issues, or messages.\n\n5. **If it still doesn't connect**, check your local config — the CLI needs a git-ignored `.firewalla.local.json` with your SSH alias (e.g. `{\"ssh_alias\": \"firewalla\"}`), or the `FIREWALLA_SSH_ALIAS` / `FIREWALLA_HOST` + `FIREWALLA_SSH_USER` + `FIREWALLA_SSH_KEY` environment variables. A missing or wrong SSH target would also make it look like nothing is being queried.\n\nA good first check is `firewalla-skill health --execute` — if that succeeds, your connection and credentials are fine and the other collection commands will work too.", "error": null, "n_skills": 139, "target_pos": 76, "prompt_tokens": 336483, "completion_tokens": 648, "sec": 8.573999166488647, "cost": 0.05086125, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T14", "target": "apple_calendar", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The file you're copying is a decoy. Here's what's actually going on.\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. Copy it **along with its sidecars**:\n\n- `Calendar.sqlitedb`\n- `Calendar.sqlitedb-wal`\n- `Calendar.sqlitedb-shm`\n\n## Why `~/Library/Calendars/Calendar.sqlitedb` fails\n\nOn current macOS, `~/Library/Calendars/Calendar.sqlitedb` is a **0-byte file**. It exists, so your script finds it and opens it without error — but there are no tables and no rows, so every query returns empty. This is the classic silent failure: no exception, no error message, just zero results, which makes it look like a query bug rather than a wrong-path bug.\n\n## Two more things that will bite you\n\n**1. You must copy the `-wal` and `-shm` sidecars too.** SQLite keeps uncheckpointed writes in the write-ahead log. If you copy only the main `.sqlitedb` file, you can read stale data — or miss recent events entirely. Copy all three files together, preserving their names, into a temp directory, then open the copy read-only (`mode=ro`). Never open the live store directly.\n\n**2. Don't try the AppleScript / EventKit route as a workaround.** Both go through macOS TCC and are denied for a background process tree (`-1743: Not authorized to send Apple events to Calendar`). The sqlite snapshot is the only no-permission path.\n\n## Quick verification\n\nAfter copying, confirm you're on the real store before trusting any result:\n\n```bash\n# The decoy is 0 bytes; the real one is not\nls -l ~/Library/Calendars/Calendar.sqlitedb\nls -l ~/Library/Group\\ Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\nIf the group-container file is non-empty and your queries still come back empty, the problem is elsewhere (time-window semantics, not the path).\n\n## One time-semantics trap worth knowing now\n\nOnce you're reading the right file, `start_date` / `end_date` are **absolute instants** (seconds since 2001-01-01 UTC), not wall-clock values — `start_tz` is only for rendering. And all-day events are stored at UTC midnight, so a naive single-instant window will either miss the all-day event on the first day or pull in the one after your `--end`. Match timed events by instant and all-day events by their UTC date.", "error": null, "n_skills": 139, "target_pos": 75, "prompt_tokens": 336490, "completion_tokens": 580, "sec": 9.322167158126831, "cost": 0.0508215, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T15", "target": "skill_image_generation", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the skill documents, here's the answer:\n\n## The Workflow: Structure Draft First, Then Repaint\n\nInstead of prompting GPT Image 2.5 from text alone, you should **first produce a structure draft (e.g., with matplotlib), then pass it as the `-i` input image** for the model to repaint.\n\nFrom the `skill_image_generation` document:\n\n> **GPT Image is repaint-only, not fresh generation**: when building infographics with GPT Image 2.5, first produce a matplotlib structure draft and pass it as the `-i` input image. The model understands the draft layout, preserves title and annotation text, and improves the visual presentation. Asking it to generate an infographic from a text prompt alone (no input image) produces unreliable results. `--aspect-ratio 16:9` suits horizontal infographics.\n\n### Concrete steps\n\n1. **Generate a structure draft** with matplotlib (or a similar deterministic tool) — this establishes the layout, title, and annotation text.\n2. **Pass that draft as an input image** using the repeatable `-i` flag:\n ```bash\n generate-image -p \"Redraw this chart as a polished infographic, preserving all titles and labels\" \\\n -i draft_chart.png \\\n -o output/infographic.png \\\n --model gpt-image-2.5-sunburst \\\n --aspect-ratio 16:9\n ```\n3. **Use `gpt-image-2.5-sunburst`** (precision-first) for final assets, or `gpt-image-2.5-flare` (speed-first) for drafts.\n\n### Why this matters\n\n- **Text-prompt-only generation is unreliable** for infographics — the model can't reliably invent a coherent layout and correct text from scratch.\n- **The model is \"repaint-only\"**: it's designed to understand and improve an existing draft's layout, preserving title and annotation text while upgrading the visual presentation.\n- This also aligns with the broader rule that **exact text, numbers, and quantitative charts must never be generated as pixels** — they should come from deterministic sources (matplotlib, DOM/SVG, etc.), with the image model only handling the visual treatment.\n\n### Related caveats worth knowing\n\n- **Set an explicit timeout** (e.g., `timeout=300000`, ~5 minutes). The default 120s bash timeout is too short for GPT Image 2.5, and complex edits routinely need 300s+.\n- **For CJK text**, matplotlib can't render Chinese with Arial on macOS — locate a system CJK font (`STHeiti`, `Heiti SC`, `PingFang HK`) and pass it via `FontProperties(fname=...)` per text element.\n- **For batches**, run 4–6 concurrent jobs with `ThreadPoolExecutor` (per-task timeout ~420s) rather than serially, since GPT Image 2.5 has high per-call latency.", "error": null, "n_skills": 139, "target_pos": 30, "prompt_tokens": 336467, "completion_tokens": 628, "sec": 7.235354900360107, "cost": 0.05084685, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T16", "target": "skill_intake", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The sync step reads only **`.m4a`** and **`.qta`** files.\n\nFrom the Intake Skill's artifact contract:\n\n> Sync reads only `.qta` and `.m4a` files. It writes normalized audio files under:\n> ```\n> data/YYYYMMDD/YYYYMMDD_HHMM_watch.m4a\n> ```\n\nSo anything else in the source folder is ignored by design. A few practical consequences worth checking, since they're the usual cause of \"skipped\" recordings:\n\n1. **`.wav` files are silently ignored.** This is the most common trap. If `make-sample-audio` reports a `.wav` output instead of `.m4a`, that's a signal `ffmpeg` is missing or broken — and sync will skip those files. Fix `ffmpeg` and regenerate.\n\n2. **`.qta` conversion depends on `ffmpeg`.** `.qta` files are read, but they need `ffmpeg` to convert. If `ffmpeg` is unavailable, `.qta` conversion fails. Check `doctor` for `checks.ffmpeg_available: true`.\n\n3. **The source path itself may be wrong.** The standard Voice Memos source is:\n ```\n ~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/\n ```\n If recordings aren't syncing from iCloud yet, they won't be in that folder at all. Run `doctor` and check `checks.source_exists` and `checks.source_is_voice_memos_default`, then `sync --dry-run` to see the `items` array.\n\n**To diagnose your specific case:**\n\n```bash\npython -m intake_skill doctor\npython -m intake_skill sync --date YYYYMMDD --dry-run\n```\n\nInspect the dry-run JSON `items` array — it shows exactly which source files were picked up and where they'd land. If a recording you expect is missing from `items`, check its extension first (`.m4a`/`.qta` only), then confirm it actually exists in the Voice Memos container.\n\nOne caveat: don't work around this by pointing `--source` at a different directory or adding microphone recording — the skill explicitly scopes itself to Voice Memos files that Apple has already synced to the Mac. If the files are genuinely `.m4a`/`.qta` and present but still skipped, that's a different problem (likely the source path or iCloud sync), not an extension issue.", "error": null, "n_skills": 139, "target_pos": 8, "prompt_tokens": 336465, "completion_tokens": 538, "sec": 7.475330114364624, "cost": 0.05079255, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T17", "target": "watch_development", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The Developer Mode toggle is hidden until the watch has been paired with your Mac at least once. Here's the exact sequence to make it appear:\n\n## Steps to reveal Developer Mode\n\n1. **Pair the watch with your Mac via Device Hub.**\n - In recent Xcode releases, the old \"Devices and Simulators\" window is replaced by **Device Hub**. Open it via *Xcode > Open Developer Tool > Device Hub*, or the run-destination menu > *Manage Devices…*.\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 Note: the watch's magnetic charging cable provides **power only** — it establishes no data link. The Mac reaches the watch entirely **through the paired iPhone**, so the iPhone must be paired/trusted with the Mac (USB or same local network), and both iPhone and watch need Bluetooth and Wi-Fi enabled.\n\n2. **Restart the Apple Watch.**\n - After the initial pairing, reboot the watch. This is the step that actually surfaces the setting.\n\n3. **Open Settings > Privacy & Security on the watch.**\n - The **Developer Mode** toggle will now be present. Toggle it on.\n\n4. **Restart again when prompted.**\n - watchOS will ask you to restart to apply the change. Confirm and let it reboot.\n\n## If it's still missing\n\n- Make sure the pairing in step 1 actually completed (the watch should appear in Device Hub and in `xcrun devicectl list devices`).\n- Reboot the watch once more — the toggle reliably appears only after the post-pairing restart.\n\n## What comes next (so you're not surprised)\n\nOnce Developer Mode is on, before the watch can accept app installs, Xcode must mount the Developer Disk Image (DDI) and copy debug symbols. During that preparation:\n- Keep the watch **on its charger, unlocked, and awake**.\n- Keep it **near the paired iPhone**, with the iPhone connected to the Mac and Bluetooth/Wi-Fi active.\n- In `xcrun devicectl list devices`, the watch transitions from `connected (no DDI)` to `connected` when preparation finishes.\n\nIf you build while preparation is still running, `xcodebuild` may abort with `Timed out waiting for all destinations … error: Preparing `. Pass `-destination-timeout ` to give it enough time.\n\nOne more thing to keep in mind: if your app uses HealthKit, a wildcard provisioning profile won't work — you'll need an explicit App ID and a profile with the HealthKit capability, and the first-launch HealthKit permission prompt must be granted manually on the watch (CLI automation can't dismiss it).", "error": null, "n_skills": 139, "target_pos": 66, "prompt_tokens": 336476, "completion_tokens": 581, "sec": 7.159901857376099, "cost": 0.05082, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T18", "target": "koyeb", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The standard CLI's `metrics get` command maps upstream nulls to `0`, so it cannot distinguish \"no traffic\" from \"no data reported.\" For scale-to-zero eligibility checks you need the lossless reader instead.\n\nFrom the Koyeb skill:\n\n> **Rule**: Native CLI maps upstream nulls to 0. Always use `scripts/request_metrics.py` when evaluating scale-to-zero eligibility or confirming inactivity.\n\nThe two commands side by side:\n\n```bash\n# Native CLI metrics — chained JSON frames; maps raw nulls to 0\npython3 scripts/koyeb_env.py -- metrics get --service example-app/example-service \\\n --start 2026-01-01T00:00:00Z --end 2026-01-01T01:00:00Z -o json\n\n# Lossless request metrics reader — preserves null vs 0 and per-series labels\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\nWhy this matters for your automation:\n\n- **Null vs. 0 is the whole signal.** A genuine zero-traffic window and a window where the metrics backend returned nothing both render as `0` through the native CLI. If your automation treats `0` as \"safe to scale down,\" a telemetry gap will look identical to real inactivity and could trigger a scale-down on bad evidence.\n- **`scripts/request_metrics.py` preserves the distinction** and also keeps per-series labels, so you can classify the window into three states rather than two (the skill's `references/metrics.md` covers the three-state classification).\n- **The reader is the documented exception** to the launcher rule: all Koyeb CLI commands must go through `scripts/koyeb_env.py` to keep credentials out of `argv`, but `request_metrics.py` is called directly as a narrow read-only exception precisely so it can preserve raw null request telemetry.\n\nOne more thing to build into the automation: control-plane queries (`describe`, `list`) do not wake instances, but **inbound HTTP data traffic does**. So do not poll a public HTTP endpoint while waiting for the idle timer to expire — polling resets the idle timer and your zero-traffic window will never close. Also confirm `--min-scale 0` is set on an eligible instance type, since sleep requires autoscaling down to zero.", "error": null, "n_skills": 139, "target_pos": 27, "prompt_tokens": 336486, "completion_tokens": 529, "sec": 7.3495941162109375, "cost": 0.050790300000000003, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T19", "target": "narwal", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "# Determining Docked/Charging State from `narwal-local`\n\nShort answer: **you can't read it directly from a single snapshot.** `is_docked` and `charging_state` are always `null` by design in this build — the raw codes live in `dock_raw` and `charging_status_raw`, and those codes vary by firmware, so the tool refuses to interpret them. The documented way to answer the question is to infer it from the **battery trend over time**, not from a status bit.\n\n## Why the fields are null\n\nFrom the skill's telemetry guidelines:\n\n> **Docked and Charging State:** `is_docked` and `charging_state` are always null. Raw values are in `dock_raw` and `charging_status_raw`.\n\nAnd the core rule:\n\n> **`is_docked` and `charging_state` are always `null`.** Raw codes are in `dock_raw` / `charging_status_raw` and vary by firmware. To tell whether it is docked or charging, use the **battery trend** (rising across a `watch` window or between snapshots), not these fields.\n\nSo this is a product boundary, not a bug. Don't try to decode `dock_raw` yourself — the skill explicitly says the codes vary by firmware and the tool won't interpret them.\n\n## The method: watch the battery trend\n\nUse a bounded `watch` window and compare `battery_percent` across samples:\n\n```bash\nnarwal-local watch --duration 60\n```\n\n`watch` emits one JSONL line per broadcast. Read `data.base_query.battery_percent` from the first and last records:\n\n- **Rising** across the window → the robot is on the dock and charging.\n- **Flat at/near 100%** → likely docked and charged (or idle on the dock).\n- **Falling** → running on battery, i.e. not charging.\n\nIf you only need a coarse check, two `snapshot` calls spaced apart work too — the skill says \"rising across a `watch` window **or between snapshots**.\"\n\n## Practical caveats\n\n1. **A single snapshot cannot answer \"why did it stop\" or \"is it docked right now.\"** The CLI is stateless — each invocation sends one query and disconnects, keeping no history. The skill is explicit: *\"You cannot answer 'why did it stop before I got here' from a single snapshot.\"*\n\n2. **Don't confuse this with `mode`.** Mode `5` (`cleaning_alt`) is not proof of being stuck or of any dock state; it may be a normal clean. To judge stuck, watch `map/display_map` `position.raw_x/raw_y` over time.\n\n3. **Absent telemetry is `null`, never `0`.** An empty fault list means unknown health, not healthy — same principle applies here. Don't read `null` as \"not docked.\"\n\n4. **`task.station_bag_dry_total_seconds` is a station drying timer, not area.** If you're trying to infer dock activity from task fields, don't use that one for area (area is `task.area_m2`).\n\n5. **Coordinates are `not_calibrated`** — use them only to see whether it moved, never for absolute position.\n\n## If you need a definitive answer\n\nThe skill's own guidance is that the robot is the authority and the tool has no history. For \"was it docked at 3pm yesterday\" or \"did it finish charging,\" use the **Narwal App's task history** — the CLI cannot answer it. The tool's stated boundaries include:\n\n> **No history.** Cannot explain a past stop.\n> **No stuck verdict.** Stuck is inferred from movement, not a status bit.\n\nSo: run `narwal-local watch --duration 60`, compare `battery_percent` at the start and end, and treat a rising value as \"docked and charging.\" That's the intended, documented path.", "error": null, "n_skills": 139, "target_pos": 118, "prompt_tokens": 336484, "completion_tokens": 847, "sec": 8.41349482536316, "cost": 0.0509808, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T20", "target": "skill_opencode_data", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Subagent fan-out sessions are skipped by default in the OpenCode export. Here's why and how to include them.\n\n## Why they're missing\n\nThe `export` command in the OpenCode data skill 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 deliberately excluded from that default set — they're filtered out unless you explicitly opt in.\n\nFrom the skill's 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. It returns scanned and exported counts (and the written file paths under `--json`). Each file begins with a YAML frontmatter block carrying `source: opencode` and a `date` field, followed by `## User` and `## Assistant` sections. **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 the two conditions that keep a session out of the output directory are:\n\n1. It has no user turn (nothing to export), or\n2. It is a subagent fan-out session and you did not pass `--include-subagent`.\n\n## How to include them\n\nAdd the `--include-subagent` flag to the 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, for example a bounded window:\n\n```bash\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --since 30d --include-subagent\n```\n\nAnd preview first with `--dry-run` if you want to see the scanned/exported counts before writing:\n\n```bash\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent --dry-run\n```\n\n## Related notes\n\n- **Selector expansion is a separate mechanism.** The `plan`/`apply` maintenance commands expand descendants through the session `parent_id` chain by default (keeping root sessions and subagent children together), and `--no-expand` disables that. That's for archive/compaction selectors, not for `export` — don't confuse the two. For `export`, the relevant switch is `--include-subagent`.\n- **Treat the output as private.** Exported markdown contains prompt and message bodies (including subagent content once included) and must never be committed. Keep it in an ignored runtime directory.\n- **Reuse an existing archive first.** Before running `export` just to look up a past session, check whether the workspace already maintains a daily-exported session archive and read from it; only run `export` to force-refresh a session newer than the archive's last run, and delete the temporary output afterward.", "error": null, "n_skills": 139, "target_pos": 94, "prompt_tokens": 336470, "completion_tokens": 605, "sec": 6.751055002212524, "cost": 0.0508335, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T21", "target": "speaker_notes", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The strict upper limit is **22 words**.\n\nFrom the speaker notes contract (`speaker_notes.md`), under \"Spoken Language & Podium Voice\":\n\n- **Easy to Say**: 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\nSo the target range is 12–20 words, and 22 words is the hard ceiling that must not be crossed.", "error": null, "n_skills": 139, "target_pos": 122, "prompt_tokens": 336468, "completion_tokens": 98, "sec": 4.01640510559082, "cost": 0.050529, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T22", "target": "skill_semantic_search", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The option is `--source-root`.\n\nFrom the semantic-search skill:\n\n> The CLI canonicalizes every file-list entry to an absolute path before cache access. Relative entries resolve against `--source-root` when provided, otherwise against the CLI's current working directory — not the directory the file list was generated in.\n\nSo:\n\n- **With the option**: `semantic-search query --file-list tmp/files.txt --source-root /path/to/workspace --cache-dir .knowledge_cache --query \"...\"` — relative entries resolve against `/path/to/workspace`.\n- **Without the option**: relative entries resolve against the CLI's **current working directory** (the directory you run the command from), *not* the directory where the file list was generated.\n\nThis matters because 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. The recommended fixes are to generate the list with absolute paths, or pass `--source-root` pointing at the directory the relative entries were generated from.", "error": null, "n_skills": 139, "target_pos": 79, "prompt_tokens": 336491, "completion_tokens": 237, "sec": 5.243854761123657, "cost": 0.05061585, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T23", "target": "smart_home_skill", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The search results aren't relevant to your question — they returned generic Samsung Galaxy AI and browser DevTools content, not smart home integration documentation. I'll answer from the skill material directly.\n\n**No — you cannot retrieve volume level or mute status from the smart home integration's status endpoint.**\n\nThis is a documented protocol limitation of the Samsung TV integration, not a configuration issue. From the Smart Home Skill's known pitfalls:\n\n> Samsung TV control uses the local Tizen WebSocket API (wss://TV_IP:8002). The token is a one-time device-local credential stored in `data/samsung_tv_ws_token.txt`; it does not expire like a SmartThings PAT. First-run pairing requires pressing \"Allow\" on the TV screen. The TV must be on the same LAN. **Volume and mute state are not readable (protocol limitation); `get_status` returns `PowerState` only.**\n\nSo `get_status` gives you `PowerState` and nothing on the audio side. There is no endpoint in this integration that reports current volume or mute state.\n\n## What to do instead\n\n**1. Verify the contract yourself before building on it.**\nPer the skill's required runtime checks, always confirm against the live schema rather than a README table (those go stale):\n\n```bash\ncurl -s {base_url}/health\ncurl -s {base_url}/openapi.json\n```\n\nFind the Samsung TV status endpoint in `openapi.json` and read its response schema. You'll see `PowerState` as the only reported field — that's your authoritative confirmation that volume/mute aren't exposed.\n\n**2. Maintain your own shadow state (recommended for automation).**\nSince the integration can *set* volume/mute but not *read* them, track it yourself:\n- Initialize to a known value (e.g., a default you set at startup).\n- Update it every time your automation issues a volume/mute command.\n- Treat it as best-effort — it drifts if anyone uses the physical remote or the TV's own UI.\n\n**3. Design around the limitation rather than fighting it.**\n- Keep a local shadow variable for volume/mute.\n- Re-assert your desired state (set volume/mute) at the start of any scenario where it matters, instead of read-then-adjust.\n- Log every set so drift is at least auditable.\n\n**4. If you genuinely need real readback**, you'd have to go outside this integration — e.g., query the TV's Tizen WebSocket API directly on port 8002 and check whether your specific firmware exposes a volume query. That's firmware-dependent and not something the smart home skill provides, so verify it against your actual TV before relying on it.\n\nIf you tell me the specific automation you're building (e.g., \"lower volume when the doorbell rings\"), I can suggest a concrete pattern that doesn't depend on reading state back.", "error": null, "n_skills": 139, "target_pos": 7, "prompt_tokens": 685777, "completion_tokens": 1175, "sec": 14.667427062988281, "cost": 0.11357155000000001, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T24", "target": "skill_unifi", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the `skill_unifi` document, here's the answer:\n\n## Exit Code for Authentication Failure\n\n**Exit code `10`** indicates an authentication failure.\n\nFrom the output contract section:\n\n> Exit codes: **0** ok / **2** usage / **10** auth (cookie expired → tell the human to refresh, see docs/cookie_refresh.md; it is a human step) / **12** rejected-no-data (bad flag/AP name, controller rejection) / **13** network/TLS/timeout (controller offline? wrong `UNIFI_BASE_URL`?)\n\n## How to Resolve It\n\nWhen you hit exit code `10` (expired cookie):\n\n1. **Tell the human to refresh the cookie** — this is explicitly a **human step**, not something the agent should attempt to automate.\n2. **Point them to `docs/cookie_refresh.md`** in the repo, which documents the cookie refresh procedure.\n3. The session cookie is exported by a human (per the setup section: \"the session cookie is exported by a human — see `docs/cookie_refresh.md`\").\n\n## Important Context\n\n- The tool is **read-only by construction** — it has no POST/PUT/DELETE operations, so you cannot \"fix\" the auth by writing anything through the CLI.\n- Private handles (controller IP, cookie path) live only in the local `.env`; never write them into workspace skill files.\n- Don't confuse exit `10` (auth) with exit `13` (network/TLS/timeout, which suggests the controller is offline or `UNIFI_BASE_URL` is wrong) — they have different remedies.\n\nSo the resolution is: **surface the expired-cookie condition to the human and have them re-export the session cookie following `docs/cookie_refresh.md`** — the agent should not try to refresh credentials itself.", "error": null, "n_skills": 139, "target_pos": 99, "prompt_tokens": 336468, "completion_tokens": 391, "sec": 6.274847030639648, "cost": 0.0507048, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T25", "target": "asr", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Based on the ASR skill documentation, the maximum duration allowed for a single audio file is **7200 seconds (2 hours)**.\n\nFrom the `asr` skill's \"Testing and Boundaries\" section:\n\n> A single file longer than 7200 seconds is rejected before a model loads. Splitting a file makes separate files; a speaker letter does not continue across slices. Several files that add up to a long day are not one file over the limit.\n\nA few practical implications for your interview batch:\n\n- **The limit is per file, not per batch.** You can process many recordings totaling far more than 2 hours in one run — the 7200-second cap applies to each individual file.\n- **The rejection happens before any model loads**, so an over-length file fails fast rather than wasting compute.\n- **Splitting is the workaround, but it has a caveat:** if you split a long interview into slices, speaker labels do not carry across the slices. Each slice is treated as a separate file, so a speaker letter (e.g., \"A\") in one slice is not guaranteed to refer to the same person in the next. For speaker-attributed transcripts of long interviews, plan for a post-merge step to reconcile speaker identities across slices.\n\nIf your interviews are under 2 hours each, you're within the limit and can run them directly. If any exceed it, split them and account for the speaker-label discontinuity when assembling the final transcript.", "error": null, "n_skills": 139, "target_pos": 67, "prompt_tokens": 336471, "completion_tokens": 299, "sec": 5.560927152633667, "cost": 0.05065005, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T26", "target": "skill_outlook", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The default timezone is **UTC**.\n\nPer the Outlook skill document, both `calendar invite` and `calendar update` treat `--start`/`--end` as wall-clock times interpreted in the `--timezone` zone, and **the default is `UTC`** when no `--timezone` argument is provided.\n\nKey details:\n\n- **`calendar invite`**: `--start`/`--end` are wall-clock times (offset-bearing values are rejected). The zone accepts IANA names (`America/Los_Angeles`) or case-insensitive aliases (`PT`/`PST`/`PDT`/`Pacific`/`US/Pacific` → `America/Los_Angeles`, `MT` → `America/Denver`, `CT` → `America/Chicago`, `ET` → `America/New_York`, plus the Windows name `Pacific Standard Time` → `America/Los_Angeles`). **Default: `UTC`.**\n\n- **`calendar update`**: Same rule — `--start`/`--end` are wall-clock times in `--timezone`, **which defaults to UTC and does not inherit the event's existing timezone**.\n\nThe skill flags this as a real trap: `calendar get` often returns Windows zone names (e.g. `Pacific Standard Time`), so never pass a `get` wall-clock time back without an explicit `--timezone`, or a US-Pacific meeting will be rewritten 7–8 hours off. Always cross-check the echoed `start_utc`/`end_utc` before trusting the result.", "error": null, "n_skills": 139, "target_pos": 56, "prompt_tokens": 682446, "completion_tokens": 647, "sec": 9.108788013458252, "cost": 0.1127551, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T27", "target": "skill_resend_email", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The validation error happens because `--after` (and `--before`) on the Resend `received list` / `received export-all-md` commands are **pagination cursors, not timestamp filters**. They expect opaque UUID cursor values from the Resend API, not ISO 8601 timestamps. Passing `2023-10-01T00:00:00Z` fails validation because the API rejects a non-UUID value.\n\nThis is documented explicitly in the Resend Email skill:\n\n> `received list/export-all-md --after` and `--before` are Resend pagination cursors, not timestamp filters. Do not pass ISO timestamps to them; the API expects UUID cursor values and will return a validation error. For date-window analysis, export recent messages first and filter Markdown files by `created_at` locally.\n\n## What `--after` actually expects\n\nA cursor UUID — the ID of the last item from a previous page, used to fetch the next page. It is a \"give me records after this record\" pointer, not \"give me records after this date.\"\n\n## How to get emails from a specific time window\n\nUse the documented workaround: export a sufficiently large recent page, then filter locally by the `created_at` frontmatter.\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\nThen filter the exported Markdown files by their `created_at` frontmatter field to isolate your target window.\n\n## Practical notes\n\n- **Pick a `--limit` large enough** to cover your window. Since you can't filter server-side by date, you need to pull enough records that your target dates are included, then narrow locally.\n- **`--limit` is the real lever here**, not `--after`. Use it to bound how much you pull.\n- **Treat the exports as private mailbox data.** Do not commit `data/received/`, attachments, raw MIME, or derived reports that expose personal email content.\n- If you genuinely need cursor-based paging (e.g., more than one page), you'd take the ID of the last record from a page and pass it as `--after` — but that's for pagination, not date filtering.\n\nSo: the flag name is misleading. `--after` = \"after this record ID,\" not \"after this date.\" For date windows, export and filter locally.", "error": null, "n_skills": 139, "target_pos": 69, "prompt_tokens": 336495, "completion_tokens": 531, "sec": 12.431686878204346, "cost": 0.05079285, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T28", "target": "skill_firecrawl", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "# Domain Filtering in the Search CLIs\n\nThe short answer: **you can't combine include and exclude domain filters in one call with the Firecrawl CLI, but you can with the Tavily CLI.** Here's the breakdown.\n\n## Firecrawl CLI — mutually exclusive\n\nFirecrawl's API does not allow both filters at once. From the skill:\n\n> Include and exclude domain filters cannot be combined (Firecrawl constraint). To both target and exclude, use the `-site:` operator inside the query text.\n\nSo with Firecrawl you have two options:\n\n**Option A — include only:**\n```bash\npython -m firecrawl_skill search \"agent framework\" \\\n --include-domain github.com \\\n --include-domain docs.anthropic.com\n```\n\n**Option B — exclude only:**\n```bash\npython -m firecrawl_skill search \"agent framework\" \\\n --exclude-domain pinterest.com\n```\n\n**Option C — both, via the query string** (the workaround):\n```bash\npython -m firecrawl_skill search \"agent framework site:github.com -site:pinterest.com\"\n```\n\nNote: `--include-domain` and `--exclude-domain` are mutually exclusive flags — passing both is a usage error (exit 2).\n\n## Tavily CLI — both flags supported\n\nTavily exposes separate repeatable flags for each direction, so you can combine them directly:\n\n```bash\npython -m tavily_skill search \"agent framework\" \\\n --include-domain github.com \\\n --include-domain docs.anthropic.com \\\n --exclude-domain pinterest.com\n```\n\nBoth `--include-domain` and `--exclude-domain` are repeatable, so you can list multiple domains on each side.\n\n## Practical notes\n\n- **Repeat the flag per domain** — don't comma-separate. `--include-domain a.com --include-domain b.com`, not `--include-domain a.com,b.com`.\n- **Firecrawl's `-site:` workaround** relies on the search backend honoring the operator inside the query text; it's less reliable than a native filter, so verify the returned URLs actually respect it.\n- **Tavily is the better choice** when you genuinely need both directions enforced at the API level. The skill notes Tavily and Firecrawl run in parallel, and recommends Firecrawl for new tasks generally — but this is one case where Tavily's flag set is the cleaner fit.\n- **Both CLIs write full payloads to a file** and return only a status object on stdout. Read the `output_path` from the status JSON to inspect results; don't expect the full result set inline.\n\nIf you tell me the specific sites you want to include and exclude, I can give you the exact command.", "error": null, "n_skills": 139, "target_pos": 106, "prompt_tokens": 336470, "completion_tokens": 579, "sec": 7.228940010070801, "cost": 0.05081789999999999, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T29", "target": "video-scoring-audio", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "Because ffmpeg's downmix **sums the channels**, which can raise the measured peak above the true peak of the original file — producing a false \"clipping\" report.\n\nFrom the `video-scoring-audio` skill:\n\n> Do not let ffmpeg downmix with `-ac 1` before measuring peak. For correlated stereo (a mono mix written to both channels, the usual case for a voice-over) it sums the channels at about **+3 dB**: a mix with a true peak of **-1.5 dBFS** measured **1.19** and was reported as clipping.\n\n## Why this happens\n\n- A mono mix written identically to both L and R channels is **correlated** stereo. Summing correlated channels adds amplitude (~+3 dB), so the downmixed signal can exceed 1.0 even though neither original channel clips.\n- The peak you then measure is a property of the *downmix*, not of the delivered file. You end up \"fixing\" a clipping problem that doesn't exist — or worse, shipping a quieter mix than necessary to satisfy a phantom constraint.\n\n## What to do instead\n\n1. **Measure peak over every channel, before any downmix.** The skill's helper keeps channels separate and takes the max across them:\n ```python\n xc, sr = load(\"out.wav\") # keep the channels\n print(float(np.max(np.abs(xc)))) # peak over all channels, no downmix\n ```\n Only downmix to mono *after* this, and only for the RMS envelope / spectral centroid, which are not peak-sensitive.\n\n2. **Cross-check with an independent meter** that reports true peak directly:\n ```bash\n ffmpeg -af ebur128=peak=true -f null - -i out.wav\n ```\n This gives integrated loudness and true peak without a lossy channel fold-down.\n\n3. **If you genuinely need a mono deliverable**, do the downmix deliberately and re-measure the *downmixed* file as its own artifact — don't reuse the stereo measurement, and don't assume the stereo peak carries over.\n\nThe general principle from the skill: measure the artifact you actually ship, on the channels it actually has. A downmix is a transformation, and measuring through it silently changes what you're measuring.", "error": null, "n_skills": 139, "target_pos": 129, "prompt_tokens": 336465, "completion_tokens": 486, "sec": 6.476057052612305, "cost": 0.050761350000000004, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false }, { "id": "T30", "target": "skill_playwright_test", "model": "deepseek-v4.1-flash", "condition": "full_all", "answer": "The infinite loop is caused by a **stale CDP Chrome profile** — the browser's user-data-dir contains session tokens left over from a previous test run.\n\n## Why it happens\n\nThe SSO SDK finds the old token in the profile, tries to use it, gets a 401 from the backend, attempts to refresh, gets another 401, and repeats — forever. The backend logs show the continuous 401s because each refresh attempt fails the same way.\n\nThis is documented as a known pitfall in the Playwright E2E test skill:\n\n> **Symptom:** Page oscillates between \"loading\" and \"signing in\" states; backend logs show repeated 401s.\n>\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\n## The fix\n\nAlways start with a **fresh user-data-dir** before launching CDP Chrome:\n\n```bash\nrm -rf /tmp/pw_debug_profile\n```\n\nThen launch Chrome pointing at that clean directory:\n\n```bash\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\n```\n\nIn automation, use `browser.new_context()` instead of reusing a persistent profile, so no session state is inherited between runs.\n\n## How to confirm it's this cause\n\n1. Check whether the profile directory existed before you started (i.e., you didn't `rm -rf` it).\n2. Kill Chrome, delete the profile directory, relaunch, and re-run the flow. If the oscillation stops, the stale token was the cause.\n3. If it persists on a genuinely fresh profile, the problem is elsewhere (auth configuration, redirect URI, or backend state) — then run `pw-test diagnose` to classify the failure before changing any code.\n\n## Related trap worth knowing\n\nA related failure mode in the same family: if you're driving the flow with Playwright and the page never reaches `networkidle`, don't switch to `wait_until=\"networkidle\"` — SPAs with persistent connections (WebSocket, polling, analytics) never go idle. Use `wait_until=\"domcontentloaded\"` instead. But that produces a *timeout*, not the loading/signing-in oscillation you're describing, so the stale profile remains the primary suspect here.", "error": null, "n_skills": 139, "target_pos": 60, "prompt_tokens": 336484, "completion_tokens": 536, "sec": 5.573625087738037, "cost": 0.0507942, "web_tools": [], "n_calls": 1, "finish_reason": "stop", "nudged": false } ]