[
 {
  "id": "T01",
  "target": "bestpractice_pdf_to_markdown",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Cause:** On Apple Silicon, Docling’s RT-DETR V2 layout model calls `torch.float64` during inference. The MPS backend does not support `float64`, so the process crashes.\n\n**Work-around:** Force the CPU device with the `--device cpu` flag (and optionally `--no-ocr` for text-only PDFs):\n\n```bash\ndocling input.pdf --to md --device cpu --no-ocr --output output_dir\n```\n\nThis bypasses the MPS backend entirely and lets the conversion complete.",
  "error": null,
  "n_skills": 139,
  "target_pos": 115,
  "prompt_tokens": 318141,
  "completion_tokens": 105,
  "sec": 18.366816997528076,
  "cost": 0.39793875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T02",
  "target": "mac_universal_clipboard",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Yes.** The Keychain item that stores the Mac’s local Handoff identity key is named:\n\n**`handoff-own-encryption-key`**\n\n### How to delete it safely\n\n1. Open **Keychain Access** (Spotlight → type “Keychain Access”).\n2. In the search field at the top-right, paste exactly:\n   ```\n   handoff-own-encryption-key\n   ```\n3. You should see one or more items with that name (usually under the “login” keychain).\n4. Select the item(s) and press **Delete** (or right-click → Delete).\n5. Restart the Continuity daemons (or just restart your Mac):\n\n   ```bash\n   killall useractivityd sharingd pboard 2>/dev/null\n   ```\n\n6. Test Universal Clipboard again (copy on iPhone/iPad → paste on Mac, and vice-versa).\n\n### Important notes\n\n- This is a **local reset only** — it does **not** affect your iCloud account or other devices.\n- If the item does not appear, the problem is likely elsewhere (e.g., the iPhone side, network, or a deeper macOS beta bug). In that case the next step is usually a Safe Mode boot or iCloud sign-out.\n- Deleting this item is a common last-resort step before signing out of iCloud, and it is safe to do.\n\nIf the item is missing or the reset still doesn’t work, let me know and we can move to the next troubleshooting layer.",
  "error": null,
  "n_skills": 139,
  "target_pos": 82,
  "prompt_tokens": 318153,
  "completion_tokens": 310,
  "sec": 29.60939884185791,
  "cost": 0.39846625,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T03",
  "target": "openreview",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**To resolve the HTTP 403 error from an expired authentication token in the OpenReview CLI:**\n\n1. Delete the cached token file:  \n   `rm tmp/openreview_token.json`\n\n2. Re-run your original query (or any OpenReview CLI command). The tool will automatically re-authenticate using the credentials from your `.env` file (`openreview_email` / `openreview_password` or the corresponding environment variables).\n\nThis forces a fresh login and clears the stale token. The CLI handles the re-authentication automatically on the next call.",
  "error": null,
  "n_skills": 139,
  "target_pos": 64,
  "prompt_tokens": 318141,
  "completion_tokens": 109,
  "sec": 26.031336069107056,
  "cost": 0.39794875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T04",
  "target": "workflow_bilibili_whisper_transcription",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**2–3 seconds** between successive downloads.\n\nThis is the conservative interval recommended in the skill documentation for Bilibili audio downloads. The guidance is to keep the process single-threaded and insert a 2–3 second pause between each request to stay under the platform’s anti-scraping thresholds.",
  "error": null,
  "n_skills": 139,
  "target_pos": 23,
  "prompt_tokens": 318135,
  "completion_tokens": 61,
  "sec": 26.302656173706055,
  "cost": 0.39782125,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T05",
  "target": "playwright_ajax_capture",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Use a follow-up GET/list after the POST, then match by content.**\n\n### Why the response lacks an ID\nMany internal APIs (especially those using async processing or optimistic creation) return a temporary token like `creation_uuid` instead of the final resource ID. The ID is only assigned after server-side processing completes. Relying on the POST response for the ID is unreliable.\n\n### Reliable pattern\n1. **POST** the resource (e.g., create a message/post).\n2. **Immediately GET** the list or search endpoint that returns the newly created item.\n3. **Match by content** (e.g., the exact text you just posted, or a unique combination of fields like `content` + `timestamp` + `author`).\n4. Extract the real numeric/string ID from the matched item.\n\n### Example flow (Circle-style internal API)\n```python\n# 1. Create the resource\nresp = requests.post(\n    \"https://target-domain.com/internal_api/messages\",\n    json={\"content\": \"Hello world\", \"parent_message_id\": None},\n    headers={\"Cookie\": cookie_header, \"X-CSRF-Token\": csrf_token}\n)\n# resp may contain creation_uuid but no usable id\n\n# 2. Fetch recent messages (or the thread)\nmessages = requests.get(\n    \"https://target-domain.com/internal_api/messages?limit=20\",\n    headers={\"Cookie\": cookie_header}\n).json()\n\n# 3. Find the newly created item by content match\nnew_msg = next(\n    (m for m in messages if m[\"content\"] == \"Hello world\"),\n    None\n)\n\nif new_msg:\n    real_id = new_msg[\"id\"]          # ← use this for subsequent calls\n    print(f\"Created with real ID: {real_id}\")\n```\n\n### Key safeguards\n- **Match on stable, unique content** — the exact text you posted, or a combination of `content` + `created_at` + `author_id`.\n- **Limit the search window** — fetch only the last N items (e.g., `limit=20`) to keep the match fast and deterministic.\n- **Handle duplicates** — if multiple items could match, add a secondary discriminator (timestamp within a few seconds, or a client-generated nonce you included in the POST).\n- **Do not reuse `creation_uuid`** as the ID for follow-up actions (e.g., `parent_message_id`). It will usually be ignored or cause the action to behave unexpectedly (thread reply becoming a standalone message).\n\n### When the list endpoint already returns full data\nIf your `list` (or `get`) endpoint already includes the full record (including body), you often don't need a separate \"view single item\" call at all — just use the list result.\n\nThis pattern was validated while reverse-engineering Circle's internal message endpoints and is the standard way to obtain the authoritative ID after an async or token-based creation.",
  "error": null,
  "n_skills": 139,
  "target_pos": 84,
  "prompt_tokens": 318151,
  "completion_tokens": 592,
  "sec": 32.2550630569458,
  "cost": 0.39916874999999996,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T06",
  "target": "workflow_watchdog",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Recommended default wake-up interval: 30 minutes (1800 seconds).**\n\nThis is the standard interval suggested in the `workflow_watchdog.md` skill for inspecting long-running background sub-agent tasks. The skill recommends setting a wake-up (via `ScheduleWakeup` or an equivalent timing mechanism) after dispatching a task, with 30 minutes as the default.\n\n### Why 30 minutes?\n- It provides a reasonable balance between staying informed about progress and avoiding excessive interruptions.\n- The skill explicitly states: \"After dispatching a task, set a reasonable inspection wake-up, default about 30 minutes (1800s).\"\n\n### How to use it\nWhen you dispatch a background task, schedule a follow-up inspection at this interval. On wake-up, check the task status:\n- If it's genuinely busy (new output, ongoing progress), leave it alone or set another wake-up.\n- If it's stuck in a loop (no progress, repeated retries), kill it and proceed with partial results or switch methods.\n\nThis prevents waiting indefinitely on stalled tasks while keeping the main workflow responsive.",
  "error": null,
  "n_skills": 139,
  "target_pos": 81,
  "prompt_tokens": 318134,
  "completion_tokens": 216,
  "sec": 25.86881709098816,
  "cost": 0.3982075,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T07",
  "target": "workflow_parallel_subagents",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Do not default to `temperature: 0` on subagents.**\n\nThe reason is that many providers and models already apply their own sampling strategies, and forcing determinism on long text payloads can cause repetition or brittle output. DS4, for example, already does deterministic handling for protocol structures, but applying a hard `temperature: 0` on extended text generation often leads to repetitive or low-quality results.\n\n**Default behavior:** Do not set temperature unless you explicitly know a specific model needs fixed sampling. Let the provider/server use its own defaults.\n\nThis is documented in the `workflow_parallel_subagents.md` file under the \"Notes\" section.",
  "error": null,
  "n_skills": 139,
  "target_pos": 124,
  "prompt_tokens": 318120,
  "completion_tokens": 129,
  "sec": 6.706252098083496,
  "cost": 0.3979725,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T08",
  "target": "bestpractice_markdown_html_conversion",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Recommended minimum padding: 24 px (or more).**\n\nApply this to the `body` or the main content container (e.g., a `<main>` or `.content` wrapper) to prevent text from hugging the edges on mobile devices. This is the standard guidance for Markdown-to-HTML conversions to maintain readability and a professional appearance across screen sizes.",
  "error": null,
  "n_skills": 139,
  "target_pos": 121,
  "prompt_tokens": 318145,
  "completion_tokens": 72,
  "sec": 22.06341290473938,
  "cost": 0.39786125,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T09",
  "target": "codex_cli",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "To disable the `web_search` tool in the Codex CLI configuration, set the bare top-level key `web_search` to the string `\"disabled\"` (not a boolean).\n\nIn your `config.toml` (or the profile file the run loads), add:\n\n```toml\nweb_search = \"disabled\"\n```\n\nThis must be a bare top-level key above any `[section]` header. The value must be the string `\"disabled\"`; other forms such as `false`, `off`, or `0` are rejected by the CLI parser.",
  "error": null,
  "n_skills": 139,
  "target_pos": 12,
  "prompt_tokens": 318146,
  "completion_tokens": 109,
  "sec": 6.217447757720947,
  "cost": 0.397955,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T10",
  "target": "apple_photos",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**50 pairs.**\n\nAccording to the skill documentation for the Apple Photos tool, each delete manifest is limited to a maximum of 50 pairs. If you have a larger set of evidence, you must split it into independent plans and obtain fresh review and authorization for each batch.",
  "error": null,
  "n_skills": 139,
  "target_pos": 105,
  "prompt_tokens": 318126,
  "completion_tokens": 54,
  "sec": 20.267942905426025,
  "cost": 0.3977925,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T11",
  "target": "colab",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Answer:** The CLI silently accepts the unrecognized `--gpu` value and falls back to requesting an A100.\n\nIn version 0.7.4, the argument parser does not reject unknown GPU identifiers. Instead, it treats the invalid string as a request for an A100 and proceeds with that accelerator. This is a known trap documented in the skill: an unrecognized `--gpu` value is sent as A100, not rejected.",
  "error": null,
  "n_skills": 139,
  "target_pos": 35,
  "prompt_tokens": 318151,
  "completion_tokens": 86,
  "sec": 20.181233167648315,
  "cost": 0.39790375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T12",
  "target": "dataforseo",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Cause:** Error 40104 means your DataForSEO account is authenticated but **not yet verified**. The account/balance endpoint works because it only checks the key, but keyword endpoints (including sandbox) are blocked until you complete verification in the DataForSEO panel.\n\n**Fix:** Log in to your DataForSEO dashboard and finish the account verification process (usually email/phone confirmation or profile details). Once verified, the 40104 error disappears and keyword queries succeed.",
  "error": null,
  "n_skills": 139,
  "target_pos": 135,
  "prompt_tokens": 318145,
  "completion_tokens": 94,
  "sec": 21.038803815841675,
  "cost": 0.39791624999999997,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T13",
  "target": "firewalla",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "To actually query your physical Firewalla device, you must add the `--execute` flag to your commands.\n\n**Example:**\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**Why it's needed:**  \nThe Firewalla CLI defaults to **dry-run mode** (no network calls). Without `--execute`, the commands only simulate the operation and do not connect to your device. The `--execute` flag is the explicit trigger that authorizes the CLI to make live API calls to your Firewalla.",
  "error": null,
  "n_skills": 139,
  "target_pos": 76,
  "prompt_tokens": 318147,
  "completion_tokens": 146,
  "sec": 5.843160629272461,
  "cost": 0.39804875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T14",
  "target": "apple_calendar",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Copy from the group container, not the decoy.**\n\nThe correct path is:\n\n```text\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\n(plus its `-wal` and `-shm` sidecars if they exist).\n\n### Why the file under `~/Library/Calendars` fails\n\n`~/Library/Calendars/Calendar.sqlitedb` is a **0-byte decoy** on current macOS. It exists only as a placeholder or legacy artifact. Copying it gives you an empty database, which is why your queries return nothing.\n\nThe real EventKit store lives in the group container:\n\n```text\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\nThis is the file the Calendar app and EventKit actually use.\n\n### Snapshot procedure (what works)\n\n1. Copy the three files together (main + WAL + SHM) to a temp directory.\n2. Open the copy read-only with `sqlite3`.\n3. Query it.\n\nThe CLI in the `apple-photos` skill (and the same pattern in the `apple-calendar` skill) does exactly this:\n\n```bash\n# Example from the apple-calendar skill\npython3 scripts/apple-calendar list --start 2026-10-01 --end 2026-10-31\n```\n\nIt snapshots the group-container database (including WAL/SHM) and queries the copy, so you never touch the live store.\n\n### Quick verification\n\n```bash\nls -l ~/Library/Calendars/Calendar.sqlitedb          # should be 0 bytes\nls -l ~/Library/Group\\ Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\nIf the group-container file is the only one with real size, that’s the one to copy.\n\nUse the group-container path and you’ll stop getting empty results.",
  "error": null,
  "n_skills": 139,
  "target_pos": 75,
  "prompt_tokens": 318153,
  "completion_tokens": 372,
  "sec": 24.541821002960205,
  "cost": 0.39862125,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T15",
  "target": "skill_image_generation",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Use a two-stage workflow: first generate a matplotlib structure draft, then pass that draft as the `-i` input image to GPT Image 2.5.**\n\n### Recommended Workflow\n1. **Create the structure draft**  \n   Use Python + matplotlib (or a similar plotting library) to produce a clean, accurate layout with the correct title, axes, labels, data points, and annotations. Save it as a PNG or JPG.\n\n2. **Feed the draft to GPT Image 2.5**  \n   Run the image-generation command with the draft as the input image:\n   ```bash\n   generate-image \\\n     -p \"Redraw this chart as a clean, modern infographic with the same data and layout, using a professional color palette and clear typography\" \\\n     -i structure_draft.png \\\n     -o final_infographic.png \\\n     --model gpt-image-2.5-sunburst \\\n     --quality high\n   ```\n   (You can also use the `-i` flag multiple times if you have additional reference images.)\n\n### Why This Is Required\n- **GPT Image 2.5 is a repaint / refinement model, not a fresh-generation model.** When asked to create an infographic from a text prompt alone, it frequently produces unreliable layouts, incorrect numbers, misaligned elements, or hallucinated data.\n- **The structure draft supplies the exact geometry and data.** The model understands the draft’s layout, preserves the title and annotations, and improves only the visual presentation (colors, typography, styling). This is the only reliable way to get accurate quantitative charts.\n- **Text-only prompts are explicitly unreliable for this use case.** The skill documentation states that asking GPT Image 2.5 to generate an infographic from a text prompt alone produces unreliable results; the draft-first approach is the documented, tested method.\n\n### Additional Tips\n- Use `--aspect-ratio 16:9` (or another suitable ratio) for horizontal infographics.\n- For rapid iteration, start with `gpt-image-2.5-flare --quality low` to test the visual direction, then switch to `gpt-image-2.5-sunburst --quality high` (or `max`) for the final asset.\n- Keep the draft simple and accurate—let the image model handle aesthetics, not data correctness.\n\nThis draft-then-refine pattern is the only workflow that consistently produces accurate, publication-ready infographics with GPT Image 2.5.",
  "error": null,
  "n_skills": 139,
  "target_pos": 30,
  "prompt_tokens": 318132,
  "completion_tokens": 495,
  "sec": 10.845186710357666,
  "cost": 0.3989025,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T16",
  "target": "skill_intake",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "The sync step only processes files with the extensions `.m4a` and `.qta`. Any other extension (including `.wav`, `.mp3`, `.aac`, or `.m4a` files that have been renamed) is ignored.",
  "error": null,
  "n_skills": 139,
  "target_pos": 8,
  "prompt_tokens": 318130,
  "completion_tokens": 49,
  "sec": 20.357859134674072,
  "cost": 0.397785,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T17",
  "target": "watch_development",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**To make the Developer Mode toggle appear on your Apple Watch:**\n\n1. **Pair the watch with your Mac** (this is the most common missing step).  \n   Open **Xcode → Open Developer Tool → Device Hub** (or **Xcode → Settings → Devices** on older Xcode versions) and click **+ → Pair Nearby Device**. Follow the on-screen pairing code on the watch.\n\n2. **Restart the Apple Watch** after pairing.  \n   The Developer Mode entry only appears after the watch has been paired at least once and then rebooted.\n\n3. Once the watch restarts, go to **Settings → Privacy & Security** on the watch.  \n   The **Developer Mode** toggle should now be visible. Turn it on and restart the watch again when prompted.\n\n### Additional notes\n- The watch must be unlocked and awake, on its charger, and near the paired iPhone during the initial pairing and DDI preparation.\n- If the toggle still does not appear after the restart, ensure the paired iPhone is connected to the Mac (USB or same Wi-Fi) and that Bluetooth + Wi-Fi are enabled on both devices.\n- Developer Mode is required before you can install development builds on the physical watch.\n\nAfter these steps the option should be present.",
  "error": null,
  "n_skills": 139,
  "target_pos": 66,
  "prompt_tokens": 318141,
  "completion_tokens": 255,
  "sec": 23.061068058013916,
  "cost": 0.39831375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T18",
  "target": "koyeb",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Use `scripts/request_metrics.py` (the lossless reader) instead of the native Koyeb CLI `metrics get`.**\n\n### Why the standard CLI is unsuitable\n\nThe native Koyeb CLI (`koyeb metrics get`) maps upstream `null` values to `0`. This silently turns “no data” into “zero traffic,” which is exactly the wrong signal for a scale-to-zero decision. A zero-traffic check must distinguish three states:\n\n- `null` (no data / unknown)  \n- `0` (confirmed zero requests)  \n- `> 0` (traffic present)\n\nThe CLI collapses the first two, so you cannot trust its output for this purpose.\n\n### What to use instead\n\nRun the dedicated lossless reader:\n\n```bash\npython3 scripts/request_metrics.py \\\n  --service-id <service-id> \\\n  --start <ISO> --end <ISO> --step 1h\n```\n\nIt preserves the distinction between `null` and `0` and returns per-series labels, so you can correctly classify the service as eligible for scale-to-zero only when every series shows confirmed zeros (not missing data).\n\n### Quick reference\n\n| Tool | Behavior on null | Safe for zero-traffic check? |\n|------|------------------|------------------------------|\n| `koyeb metrics get` | Maps null → 0 | No |\n| `scripts/request_metrics.py` | Preserves null vs 0 | Yes |\n\nUse the script, not the CLI, when the decision depends on the difference between “no data” and “zero traffic.”",
  "error": null,
  "n_skills": 139,
  "target_pos": 27,
  "prompt_tokens": 318150,
  "completion_tokens": 317,
  "sec": 6.670154809951782,
  "cost": 0.39848,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T19",
  "target": "narwal",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Answer:** You cannot determine docking or charging status from `is_docked` or `charging_state` — they are always `null` in the current CLI output. Use the **battery trend** instead.\n\n### How to check with the CLI\n\nRun a short `watch` window (or two `snapshot` calls a few minutes apart) and look at the battery percentage:\n\n```bash\nnarwal-local watch --duration 30\n```\n\n**Interpretation:**\n\n- **Battery rising** (e.g., 45% → 52% → 61%) → the robot is docked and charging.\n- **Battery stable or falling** while in a cleaning mode → it is not charging (or the dock is not supplying power).\n- **Battery at 100%** with no change over time → it is docked but may have finished charging.\n\n### Why the fields are null\n\nThe CLI deliberately emits `is_docked` and `charging_state` as `null` because the raw values (`dock_raw`, `charging_status_raw`) vary by firmware and are not reliable indicators. The battery trend is the only consistent signal the tool provides.\n\n### Quick one-shot alternative\n\nIf you just need a single check:\n\n```bash\nnarwal-local snapshot\n```\n\nThen compare the `battery_percent` value against a second run a few minutes later. Rising = charging.\n\nThis matches the documented behavior in the skill: the robot is the authority, and the battery trend is the practical way to determine charging status when the dedicated fields are unavailable.",
  "error": null,
  "n_skills": 139,
  "target_pos": 118,
  "prompt_tokens": 318148,
  "completion_tokens": 306,
  "sec": 7.7427520751953125,
  "cost": 0.39845,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T20",
  "target": "skill_opencode_data",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Subagent fan-out sessions are excluded by default** in the OpenCode export workflow.\n\n### Why they are missing\nThe export logic (`export_sessions.py`) deliberately skips sessions that have a non-empty `parentID` (i.e., sessions that were spawned as subagents or forked children of another session). This is the intended behavior for the standard export, so the output directory only contains top-level / root sessions.\n\n### How to include them\nUse the `--include-subagent` flag when running the export:\n\n```bash\npython export_sessions.py --include-subagent\n```\n\nThis tells the exporter to also process sessions that have a `parentID`, so the fan-out subagent sessions will appear in the output directory alongside the main sessions.\n\n### Quick verification\nAfter running with the flag, check whether the previously missing session IDs now have corresponding `.md` files in the export directory. If you only want a subset of subagent sessions, you can still filter by date, source, or other criteria in the same command.",
  "error": null,
  "n_skills": 139,
  "target_pos": 94,
  "prompt_tokens": 318134,
  "completion_tokens": 206,
  "sec": 5.516624212265015,
  "cost": 0.3981825,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T21",
  "target": "speaker_notes",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**22 words.**\n\nThis is a hard gate in the speaker notes workflow: ordinary spoken sentences must stay between 12–20 words, and **no ordinary spoken sentence may exceed 22 words**. (Longer sentences are only acceptable in rare, deliberate cases such as a quoted passage or a carefully constructed rhetorical device.)",
  "error": null,
  "n_skills": 139,
  "target_pos": 122,
  "prompt_tokens": 318133,
  "completion_tokens": 63,
  "sec": 4.304979085922241,
  "cost": 0.39782375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T22",
  "target": "skill_semantic_search",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "Use the `--source-root` option to define the base directory for resolving relative paths in your file list.\n\nIf you omit `--source-root`, the tool resolves relative paths against the CLI's current working directory.",
  "error": null,
  "n_skills": 139,
  "target_pos": 79,
  "prompt_tokens": 318155,
  "completion_tokens": 41,
  "sec": 3.5089778900146484,
  "cost": 0.39779625,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T23",
  "target": "smart_home_skill",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**No, you cannot retrieve volume level or mute status** through the Smart Home Skill's status endpoint.\n\n### Why\nThe Samsung TV integration (via the local Tizen WebSocket API) only exposes `PowerState`. Volume and mute are explicitly **not readable** due to a protocol limitation. The skill documentation states this directly:\n\n> \"Volume and mute state are not readable (protocol limitation); `get_status` returns `PowerState` only.\"\n\n### What you *can* do\n- Use the dedicated TV control endpoints (if present in your deployment's OpenAPI) for commands like volume up/down or mute toggle.\n- For power state only, call the status endpoint and read `PowerState`.\n\nIf you need volume/mute feedback, you would have to implement it outside this skill (e.g., via another API or by tracking your own commands), as the current integration does not provide it.\n\nCheck your running service's `/openapi.json` for the exact available TV endpoints before coding.",
  "error": null,
  "n_skills": 139,
  "target_pos": 7,
  "prompt_tokens": 318138,
  "completion_tokens": 199,
  "sec": 8.049607038497925,
  "cost": 0.39816999999999997,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T24",
  "target": "skill_unifi",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Exit code 10** (`EXIT_TRANSPORT`) indicates an authentication failure caused by an expired cookie.\n\n**Resolution:**  \nTell the human to refresh the session cookie (see `docs/cookie_refresh.md` in the repo). This is a human step; the CLI itself cannot refresh it.",
  "error": null,
  "n_skills": 139,
  "target_pos": 99,
  "prompt_tokens": 318133,
  "completion_tokens": 59,
  "sec": 20.88021492958069,
  "cost": 0.39781375,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T25",
  "target": "asr",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**7200 seconds (2 hours).**\n\nThe pipeline explicitly rejects any single file longer than 7200 seconds before loading the model. If you have a longer recording, split it into separate files first—each file is treated independently, and speaker labels do not carry across splits.",
  "error": null,
  "n_skills": 139,
  "target_pos": 67,
  "prompt_tokens": 318135,
  "completion_tokens": 56,
  "sec": 6.529650926589966,
  "cost": 0.39780875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T26",
  "target": "skill_outlook",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Default timezone: UTC**\n\nWhen `--timezone` is omitted, the `calendar invite` and `calendar update` commands treat the provided `--start` and `--end` values as wall-clock times in **UTC**.\n\nThis is explicitly documented in the skill file:\n\n> `--start`/`--end` are wall-clock times (no UTC offset allowed; offset-bearing values are rejected) interpreted in the `--timezone` zone. 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`); the default is `UTC`.\n\nSo if you run:\n\n```bash\n.venv/bin/python -m gdocs calendar invite \\\n  --to user@example.com \\\n  --subject \"Meeting\" \\\n  --start 2026-05-20T10:00:00 \\\n  --end 2026-05-20T10:30:00\n```\n\nThe event will be created with a start time of `2026-05-20T10:00:00Z` (UTC).",
  "error": null,
  "n_skills": 139,
  "target_pos": 56,
  "prompt_tokens": 318134,
  "completion_tokens": 279,
  "sec": 6.940746068954468,
  "cost": 0.398365,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T27",
  "target": "skill_resend_email",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "The `--after` flag does **not** accept an ISO timestamp like `2023-10-01T00:00:00Z`. It expects a **UUID cursor** (the ID of a specific email record) to paginate through results.\n\n**Why the error happened:** The API interprets the timestamp string as an invalid cursor value, triggering a validation error.\n\n**Correct usage:** Use `--after` with a real email ID from a previous `list` response to fetch the next page of results. For filtering by date, you must export the messages first and then filter the local Markdown files by their `created_at` frontmatter.",
  "error": null,
  "n_skills": 139,
  "target_pos": 69,
  "prompt_tokens": 318158,
  "completion_tokens": 129,
  "sec": 19.229671239852905,
  "cost": 0.39802,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T28",
  "target": "skill_firecrawl",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "To restrict a search to specific domains while excluding another, use the `--include-domain` and `--exclude-domain` flags together in a single command. These flags are repeatable, so you can list multiple domains to include and one (or more) to exclude.\n\nExample command:\n\n```bash\npython -m tavily_skill search \"agent framework\" \\\n  --include-domain github.com \\\n  --include-domain docs.anthropic.com \\\n  --exclude-domain example.com\n```\n\nThis will return results only from the listed include domains and will filter out any results from the excluded domain.",
  "error": null,
  "n_skills": 139,
  "target_pos": 106,
  "prompt_tokens": 318135,
  "completion_tokens": 115,
  "sec": 6.24717903137207,
  "cost": 0.39795625,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T29",
  "target": "video-scoring-audio",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Because a naive downmix can inflate the measured peak by ~3 dB and make a perfectly clean mix look like it is clipping.**\n\n### What actually happens\n\nWhen you run\n\n```bash\nffmpeg -i mix.wav -ac 1 -f f32le - | ...\n```\n\nffmpeg sums the two channels and then divides by 2 (the default `pan`/`amerge` behavior).  \nIf the left and right channels are **correlated** (the usual case for a mono mix written to both channels, or for a centered voice-over), the sum is **+6 dB** before the divide-by-2, so the net result is **+3 dB**.\n\nA mix whose true peak is –1.5 dBFS can therefore be reported as **+1.5 dBFS** after the downmix, triggering a false “clipping” warning.\n\n### Why this matters for the pipeline\n\nThe skill’s measurement contract is:\n\n> “Peak must stay below 1.0 (no clipping).”\n\nIf the measurement itself is wrong, you will either:\n\n- reject a perfectly good cue, or  \n- accept a cue that really is clipping because you trusted the inflated number.\n\n### Correct way to measure peak\n\nMeasure **before** any down-mix, across every channel:\n\n```python\npeak = float(np.max(np.abs(xc)))   # xc is the multi-channel buffer\n```\n\nor, if you must work in mono for other reasons, use a proper down-mix that preserves head-room (e.g. `pan=mono|c0=0.707*c0+0.707*c1` or the EBU-recommended –3 dB sum) and still verify the original stereo peak.\n\n### Rule of thumb\n\nNever let ffmpeg’s automatic `-ac 1` be the last step before you read the peak.  \nMeasure the original multi-channel file; only then decide whether the cue is safe to ship.",
  "error": null,
  "n_skills": 139,
  "target_pos": 129,
  "prompt_tokens": 318127,
  "completion_tokens": 396,
  "sec": 11.691774845123291,
  "cost": 0.39864875,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T30",
  "target": "skill_playwright_test",
  "model": "grok-4-fast",
  "condition": "full_all",
  "answer": "**Stale CDP Chrome profile with leftover SSO session tokens.**\n\nThe browser profile (`--user-data-dir`) still contains valid but expired or invalid SSO tokens from a previous run. The SSO SDK detects the token, attempts to use it, receives a 401, tries to refresh, receives another 401, and loops forever.\n\n### Why this happens\n- The profile was reused across sessions without clearing cookies/storage.\n- The SSO flow never fully completes or the token becomes invalid, but the SDK keeps retrying the same bad token.\n- This is especially common when using Playwright's `connect_over_cdp` or persistent contexts without proper isolation.\n\n### Fix\n1. **Kill the current Chrome instance** and delete the profile directory:\n   ```bash\n   pkill -f \"remote-debugging-port=9222\" || true\n   rm -rf /tmp/pw_debug_profile   # or whatever path you're using\n   ```\n\n2. **Restart Chrome with a fresh profile**:\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\n3. **Re-login manually** in the new profile.\n\n4. **For future runs**, always start with a clean profile when debugging auth flows:\n   ```bash\n   rm -rf /tmp/pw_<purpose>_profile\n   ```\n\n### Prevention\n- Use unique `--user-data-dir` paths per debugging session (e.g., include a timestamp or purpose).\n- For automated tests, prefer `browser.new_context()` (isolated context) over reusing the same persistent profile.\n- If using Playwright Python, consider `context.clear_cookies()` + `context.clear_storage()` between test runs, though a fresh profile is more reliable for SSO debugging.\n\nThis is the exact pattern that produces the \"loading ↔ signing in\" oscillation with repeated 401s.",
  "error": null,
  "n_skills": 139,
  "target_pos": 60,
  "prompt_tokens": 318147,
  "completion_tokens": 405,
  "sec": 12.500279664993286,
  "cost": 0.39869625000000003,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 }
]