[
 {
  "id": "T01",
  "target": "bestpractice_pdf_to_markdown",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Based on the `bestpractice_pdf_to_markdown` reference, here is the cause and the workaround:\n\n### 1. Cause of the Crash\nDuring the layout detection phase, Docling's **RT-DETR V2** model calls **`torch.float64`**, which the Apple Silicon **MPS (Metal Performance Shaders) backend does not support**. This unsupported operation causes the entire PDF conversion process to crash.\n\n---\n\n### 2. Workaround Flag\nTo avoid the crash, run Docling directly on the CPU using the flag:\n\n```bash\n--device cpu\n```\n\n#### Example Usage:\n```bash\ndocling input.pdf --to md --device cpu --output output_dir\n```\n\n*(Optional)* For text-only PDFs that do not require OCR, you can also add **`--no-ocr`** to avoid unnecessary model paths and false recognition:\n```bash\ndocling input.pdf --to md --device cpu --no-ocr --output output_dir\n```\n\n> **Note:** Relying on `PYTORCH_ENABLE_MPS_FALLBACK=1` is not recommended, as fallback behavior may fail depending on PyTorch/macOS version combinations. Using `--device cpu` directly is the most deterministic solution.",
  "error": null,
  "n_skills": 139,
  "target_pos": 115,
  "prompt_tokens": 349905,
  "completion_tokens": 637,
  "sec": 11.235535860061646,
  "cost": 0.2648175,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T02",
  "target": "mac_universal_clipboard",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Yes. You can delete the Keychain item named:\n\n```text\nhandoff-own-encryption-key\n```\n\nDeleting this item resets the Mac's local Handoff identity key without requiring you to sign out and back into iCloud (which avoids triggering full resyncs of iCloud Drive, Photos, and Keychain).\n\n### Steps to Reset:\n\n1. **Delete the Keychain Item**:\n   - Open **Keychain Access** (`/Applications/Utilities/Keychain Access.app`).\n   - In the search bar, search for:\n     ```text\n     handoff-own-encryption-key\n     ```\n   - Delete the matching item(s).\n\n2. **Restart the Handoff Daemons**:\n   After deleting the key, restart the Mac's local clipboard and continuity processes by running the following command in Terminal:\n   ```bash\n   killall useractivityd sharingd pboard\n   ```\n   *(launchd will automatically relaunch these processes and regenerate the state).*\n\n3. **Test Synchronization**:\n   - Test copying on your iPhone/iPad and pasting on your Mac, and vice-versa.\n\n*(Note: If this still does not resolve the issue, booting into **Safe Mode** once and restarting normally clears underlying system caches before resorting to a full iCloud sign-out).*",
  "error": null,
  "n_skills": 139,
  "target_pos": 82,
  "prompt_tokens": 349918,
  "completion_tokens": 642,
  "sec": 17.59416389465332,
  "cost": 0.264846,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T03",
  "target": "openreview",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "To resolve an HTTP 403 error caused by an expired token, take the following steps:\n\n1. **Delete the cached token file:**\n   ```bash\n   rm tmp/openreview_token.json\n   ```\n2. **Re-run your query:**\n   Execute your `tools/openreview_cli.py` command again. The tool will authenticate freshly using the credentials in your `.env` / environment variables and save a new token.\n\n*(Note: OpenReview enforces a rate limit of at most 3 logins per time window, so delete the file and retry once rather than repeatedly.)*",
  "error": null,
  "n_skills": 139,
  "target_pos": 64,
  "prompt_tokens": 349907,
  "completion_tokens": 454,
  "sec": 11.709712028503418,
  "cost": 0.26413275,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T04",
  "target": "workflow_bilibili_whisper_transcription",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "When downloading audio tracks from Bilibili for batch transcription, you should maintain a **2–3 second interval** between each successive download (and keep the download queue **single-threaded**) to avoid triggering anti-scraping mechanisms and request blocks (such as HTTP 352 errors).",
  "error": null,
  "n_skills": 139,
  "target_pos": 23,
  "prompt_tokens": 349899,
  "completion_tokens": 266,
  "sec": 15.695868253707886,
  "cost": 0.26342175,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T05",
  "target": "playwright_ajax_capture",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "When reverse-engineering internal APIs, this is a common architectural pattern. Here is why it happens and how to reliably obtain the real resource ID:\n\n---\n\n### Root Cause\n\nMany internal APIs decouple the request ingestion from actual resource persistence. \n- The `creation_uuid` is merely a temporary client-tracking or asynchronous task token (often returning an HTTP `202 Accepted` or `200 OK` indicating the action was enqueued).\n- The canonical database ID (often a numeric ID or standard resource identifier) is generated only after server-side processing completes.\n- If you attempt to access `response.json().get(\"id\")`, it evaluates to `None`. Passing `None` to subsequent calls (e.g., as a `parent_message_id`) causes requests to behave unpredictably—such as creating a detached, standalone item instead of a nested thread reply.\n\n---\n\n### Recommended Solution: The POST-then-GET Pattern\n\nInstead of relying on the immediate `POST` response for the ID, use a **fetch-and-match** strategy:\n\n1. **Submit the `POST` request** to create the resource. Do not inspect or use the body for an `id`.\n2. **Issue an immediate `GET` request** to the list endpoint that displays those resources (e.g., `/internal_api/.../messages` or `/feed`).\n3. **Locate the record by content matching**:\n   - Filter the list to find the item whose content matches what was just posted.\n   - To prevent collisions with duplicate or common text, cross-reference with the timestamp or inject a unique, discrete marker if allowable.\n4. **Extract the real ID** from the matched object.\n5. **Proceed with downstream calls** (e.g., thread replies, updates, deletes) using the verified ID.\n\n---\n\n### Example Implementation\n\n```python\nimport time\nimport requests\n\n# 1. Post the resource\npost_payload = {\n    \"body\": \"Unique message content here\",\n    # other fields...\n}\npost_resp = requests.post(create_url, json=post_payload, headers=headers)\n# Note: post_resp contains only creation_uuid; do NOT use post_resp.json().get(\"id\")\n\n# Optional: brief pause if server processing is asynchronous\ntime.sleep(0.5)\n\n# 2. Fetch the latest items from the list endpoint\nlist_resp = requests.get(list_url, headers=headers)\nitems = list_resp.json().get(\"records\", [])\n\n# 3. Find the newly created item by matching content\nreal_id = None\nfor item in items:\n    if item.get(\"body\") == post_payload[\"body\"]:\n        real_id = item.get(\"id\")\n        break\n\nif not real_id:\n    raise RuntimeError(\"Failed to resolve real ID for newly created resource.\")\n\n# 4. Use the real ID in subsequent calls\nreply_payload = {\n    \"parent_message_id\": real_id,\n    \"body\": \"This is a reply to the confirmed parent ID\"\n}\nrequests.post(reply_url, json=reply_payload, headers=headers)\n```\n\n---\n\n### Key Caveats\n\n- **Never substitute `creation_uuid` for `id`**: Upstream endpoints typically reject UUID tokens when expecting entity IDs or silently discard them, treating the field as unset.\n- **Watch out for caching/pagination**: Ensure the `GET` list request fetches the most recent records (e.g., page 1, sorted descending, or with cache-busting headers if applicable).",
  "error": null,
  "n_skills": 139,
  "target_pos": 84,
  "prompt_tokens": 349925,
  "completion_tokens": 1459,
  "sec": 14.489839792251587,
  "cost": 0.26791499999999996,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": true
 },
 {
  "id": "T06",
  "target": "workflow_watchdog",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Based on the **Workflow Watchdog** guidelines, the recommended default wake-up interval is **about 30 minutes (1,800 seconds)**.\n\n### Recommended Actions on Wake-Up:\n1. **Set the inspection timer:** Use `ScheduleWakeup` (in the Claude Code harness) or a corresponding timing mechanism such as Process Launcher delayed execution in other harnesses.\n2. **Evaluate task state:**\n   - **Genuinely busy:** New output is being produced and progress is active. Leave it running and either wait or set another wake-up timer.\n   - **Stuck in a loop:** No progress has occurred for an extended period or steps are repeating. Terminate the task and proceed with partial results or switch strategies.",
  "error": null,
  "n_skills": 139,
  "target_pos": 81,
  "prompt_tokens": 349902,
  "completion_tokens": 568,
  "sec": 11.527875661849976,
  "cost": 0.2645565,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T07",
  "target": "workflow_parallel_subagents",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "According to the **Parallel Subagent Workflow** (`workflow_parallel_subagents`), you should not default to setting `temperature: 0` for subagents for several reasons:\n\n1. **Risk of Repetition on Long Payloads:** Forcing strict determinism (`temperature: 0`) on long text outputs frequently causes models to fall into degenerative repetition loops.\n2. **Provider-Specific Defaults:** Many models and API providers already implement their own tuned, optimal sampling strategies out of the box.\n3. **Built-in Determinism Where It Counts:** Certain model setups (such as DS4) already handle protocol and structured outputs deterministically where needed, making a global temperature override unnecessary.\n\n**Best Practice:** Leave the temperature parameter unset so the underlying provider or server applies its own defaults. Only set `temperature: 0` when you explicitly know that a specific model and task require fixed, deterministic sampling.",
  "error": null,
  "n_skills": 139,
  "target_pos": 124,
  "prompt_tokens": 349886,
  "completion_tokens": 586,
  "sec": 15.22140383720398,
  "cost": 0.264612,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T08",
  "target": "bestpractice_markdown_html_conversion",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "The recommended minimum padding is **24px or more** on the HTML `body` or main container to prevent text from hugging or clinging to the screen edges on mobile devices.",
  "error": null,
  "n_skills": 139,
  "target_pos": 121,
  "prompt_tokens": 349909,
  "completion_tokens": 256,
  "sec": 7.764941930770874,
  "cost": 0.26339175000000004,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T09",
  "target": "codex_cli",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "To disable `web_search` and resolve the HTTP 400 error, configure your Codex configuration file (`config.toml` or the specific profile file being loaded) with the following specifications:\n\n### 1. Exact Setting & Placement\nAdd the key as a **bare top-level key** placed **above every `[section]` header** in the file (since TOML associates bare keys with any preceding table header):\n\n```toml\nweb_search = \"disabled\"\n\n# Place any [sections] below this line\n```\n\n### 2. Value Type\n* **Required Type**: A **string unit variant** (`\"disabled\"`).\n* **Do NOT use booleans or alternate strings**: Values such as `false`, `true`, `\"off\"`, `\"on\"`, or `\"enabled\"` will be rejected with `expected string only in 'web_search'`. Only the exact string `\"disabled\"` is parsed.",
  "error": null,
  "n_skills": 139,
  "target_pos": 12,
  "prompt_tokens": 349914,
  "completion_tokens": 695,
  "sec": 8.667104005813599,
  "cost": 0.26504174999999996,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T10",
  "target": "apple_photos",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Based on the Apple Photos skill specification, the maximum allowed in a single delete manifest is **50 pairs** (under the **Delete Batch Bound** rule). \n\nLarger evidence sets must be split into independent plans, with fresh review and authorization required for each batch.",
  "error": null,
  "n_skills": 139,
  "target_pos": 105,
  "prompt_tokens": 349890,
  "completion_tokens": 185,
  "sec": 5.853680849075317,
  "cost": 0.26311124999999996,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T11",
  "target": "colab",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "On version 0.7.4 of the Google Colab CLI, an unrecognized `--gpu` value is **sent as `A100`** rather than being rejected.",
  "error": null,
  "n_skills": 139,
  "target_pos": 35,
  "prompt_tokens": 349916,
  "completion_tokens": 262,
  "sec": 5.2954020500183105,
  "cost": 0.26341950000000003,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T12",
  "target": "dataforseo",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "### Cause\n\nError code `40104` occurs when your account is authenticated but **not verified**. \n\nBecause your credentials are valid, basic account endpoints like `balance` succeed. However, DataForSEO blocks keyword data endpoints—including the sandbox environment—until account verification is completed.\n\n---\n\n### How to Fix\n\n1. **Log in to the DataForSEO dashboard** via your web browser.\n2. **Complete the required account verification** (check for any pending email, phone, or identity verification steps in your account settings or onboarding notifications).\n3. Once the account shows as verified in the dashboard, retry your CLI keyword queries (e.g., `dataforseo-skill keyword-suggestions ...`).",
  "error": null,
  "n_skills": 139,
  "target_pos": 135,
  "prompt_tokens": 349912,
  "completion_tokens": 492,
  "sec": 7.734241724014282,
  "cost": 0.264279,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T13",
  "target": "firewalla",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "To query your physical Firewalla device, you need to include the **`--execute`** flag.\n\n### Why It Is Needed\n\nBy default, live collection commands in `firewalla-skill` run in **dry-run mode** as a safety precaution. The tool will not make live network requests to query the physical Firewalla hardware unless you explicitly pass `--execute` to signal your intent to connect.\n\n### Example Commands\n\nWhen running collection from the project directory (`adhoc_jobs/firewalla_skill/`) with the virtual environment activated, structure your commands like this:\n\n```bash\n# Health check\nfirewalla-skill health --execute\n\n# Collect all devices\nfirewalla-skill devices --execute --all --json --output reports/devices_all_latest.json\n\n# Collect alarms from the last 3 days\nfirewalla-skill alarms --execute --since-days 3 --include-archive --all --json --output reports/alarms_last3d_latest.json\n```",
  "error": null,
  "n_skills": 139,
  "target_pos": 76,
  "prompt_tokens": 349909,
  "completion_tokens": 529,
  "sec": 6.285636901855469,
  "cost": 0.2644155,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T14",
  "target": "apple_calendar",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "### Why `~/Library/Calendars/Calendar.sqlitedb` Failed\n\nOn modern versions of macOS, **`~/Library/Calendars/Calendar.sqlitedb` is a 0-byte decoy file**. It contains no tables or data, so any script querying it will silently return empty results.\n\n---\n\n### The Real Path to Copy From\n\nThe actual EventKit SQLite database resides inside the Calendar group container:\n\n```text\n~/Library/Group Containers/group.com.apple.calendar/Calendar.sqlitedb\n```\n\n---\n\n### Essential Steps for Snapshotting and Querying\n\nTo properly query the database without data corruption or missing recent events:\n\n1. **Copy the WAL and SHM sidecars too**:  \n   macOS runs SQLite in WAL (Write-Ahead Logging) mode. Uncheckpointed writes reside in the `-wal` and `-shm` sidecars. If you only copy the `.sqlitedb` file, you will read stale data. You must copy all three files:\n   - `Calendar.sqlitedb`\n   - `Calendar.sqlitedb-wal` (if present)\n   - `Calendar.sqlitedb-shm` (if present)\n\n2. **Snapshot to a temporary location**:  \n   Never open or query the live database directly. Copy the files into a temporary directory first:\n\n   ```bash\n   SRC=\"$HOME/Library/Group Containers/group.com.apple.calendar\"\n   DEST=\"/tmp/calendar_snapshot\"\n\n   mkdir -p \"$DEST\"\n   cp \"$SRC\"/Calendar.sqlitedb* \"$DEST\"/\n   ```\n\n3. **Open in read-only mode**:  \n   Query the snapshot with SQLite in read-only mode:\n\n   ```bash\n   sqlite3 \"file:$DEST/Calendar.sqlitedb?mode=ro\" \"SELECT rowid, summary FROM CalendarItem LIMIT 10;\"\n   ```\n\n---\n\n### Key Schema Gotchas to Keep in Mind\n\n- **Reference Epoch**: Timestamps (`start_date`, `end_date`) are stored as seconds since **`2001-01-01 00:00:00 UTC`** (the Apple CoreFoundation / `NSDate` reference epoch), **not** standard Unix epoch (1970).\n- **Absolute Instants vs. Local Time**: `start_date` is stored as an absolute UTC instant. The `start_tz` column stores the original timezone string for display purposes only.\n- **All-Day Events**: All-day items have `all_day = 1`, timezone `_float`, and their date is stored at UTC midnight of the event date.",
  "error": null,
  "n_skills": 139,
  "target_pos": 75,
  "prompt_tokens": 349918,
  "completion_tokens": 1045,
  "sec": 9.400414943695068,
  "cost": 0.26635725000000005,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T15",
  "target": "skill_image_generation",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "When generating infographics with GPT Image 2.5, you should use an **image-to-image repaint/redraw workflow** rather than generating from a text prompt alone.\n\n---\n\n### The Recommended Workflow\n\n1. **Create a Structural Draft First:**\n   * Generate an exact layout or chart structure ahead of time using code (e.g., Python + `matplotlib`) and save it as a clean image (PNG).\n2. **Pass the Draft as an Input Image:**\n   * Feed that draft into GPT Image 2.5 using the `-i` input flag (e.g., `generate-image -p \"...\" -i draft.png -o infographic.png --model gpt-image-2.5-sunburst`).\n   * (Optional) Use `--aspect-ratio 16:9` if you need a standard horizontal infographic layout.\n3. **Instruct the Model to Restyle/Repaint:**\n   * Prompt the model to follow the draft's visual layout, preserve the titles and annotation text, and polish the aesthetic presentation.\n4. **Set an Extended Timeout:**\n   * Complex image redraws (like converting a matplotlib chart into an infographic) routinely take several minutes. Set an explicit command timeout of 300 seconds (5 minutes) or more to prevent premature timeout exits.\n\n---\n\n### Why This Workflow Is Necessary\n\n* **Avoids Hallucination and Layout Breakdown:** Generating complex infographics or quantitative charts from a text prompt alone produces unreliable results, distorted layouts, and hallucinated or garbled numerical data.\n* **Leverages Repainting Strengths:** GPT Image 2.5 functions most reliably as a repainting/restyling engine for structural graphics. When given a concrete input image, the model can interpret the existing spatial layout, preserve critical text labels and numbers, and focus its generative capabilities on upgrading visual styling and finish.",
  "error": null,
  "n_skills": 139,
  "target_pos": 30,
  "prompt_tokens": 349895,
  "completion_tokens": 977,
  "sec": 12.375592947006226,
  "cost": 0.266085,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T16",
  "target": "skill_intake",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "The sync step in the Intake Skill reads only two file extensions:\n\n- **`.m4a`**\n- **`.qta`**\n\nAny other formats (such as `.wav` or `.mp3`) are ignored by the sync process. \n\n*(Note: If any `.qta` files are present, `ffmpeg` must be installed and available on your system to convert them during sync.)*",
  "error": null,
  "n_skills": 139,
  "target_pos": 8,
  "prompt_tokens": 349894,
  "completion_tokens": 388,
  "sec": 6.6513190269470215,
  "cost": 0.2638755,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T17",
  "target": "watch_development",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "If the **Developer Mode** toggle is missing under **Settings > Privacy & Security** on your Apple Watch, it means the watch has not yet established an initial developer pairing with a Mac running Xcode, or watchOS has not surfaced the toggle yet.\n\nFollow these steps to make the option appear:\n\n---\n\n### Step 1: Ensure Prerequisites and Connection\n1. **Connect the paired iPhone to your Mac**:\n   * The Apple Watch does not have a direct USB data link to the Mac (the magnetic charger provides power only). The Mac communicates with the watch strictly **through the paired iPhone**.\n   * Connect and trust the paired iPhone with the Mac via USB or over the same Wi-Fi network.\n   * Ensure **Bluetooth and Wi-Fi** are enabled on both the iPhone and the Apple Watch.\n\n### Step 2: Pair the Watch with the Mac via Device Hub\nXcode does not auto-discover the watch for developer use without explicit pairing:\n1. Open Xcode on your Mac.\n2. Open **Device Hub** via:\n   * **Xcode > Open Developer Tool > Device Hub**, or\n   * The run-destination dropdown in the Xcode toolbar > **Manage Devices…**\n3. In Device Hub, click **\"+\"** and select **\"Pair Nearby Device\"**.\n4. When prompted, a numeric pairing code will appear on the Apple Watch display. Confirm the code on the watch to establish trust.\n\n### Step 3: Restart the Apple Watch\n1. Power off the Apple Watch completely and turn it back on.\n2. Rebooting after establishing the initial pairing in Device Hub is required for watchOS to reveal the hidden developer menu entry.\n\n### Step 4: Enable Developer Mode\n1. On the watch, navigate to **Settings > Privacy & Security**.\n2. Scroll to the bottom; the **Developer Mode** toggle will now be visible.\n3. Toggle **Developer Mode** on.\n4. When prompted by watchOS, restart the Apple Watch to apply the change.\n5. After the watch restarts, unlock it and tap **Turn On** (and enter your passcode) to confirm.",
  "error": null,
  "n_skills": 139,
  "target_pos": 66,
  "prompt_tokens": 349905,
  "completion_tokens": 951,
  "sec": 9.28747296333313,
  "cost": 0.265995,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T18",
  "target": "koyeb",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "### Why You Shouldn't Rely on the Standard CLI Metrics\n\nThe native Koyeb CLI command (`koyeb metrics get`) **maps upstream `null` values to `0`**. \n\nWhen automating scale-to-zero or inactivity verification, this creates a critical blind spot:\n- A raw `null` indicates missing data, an unpolled/unreported window, or no telemetry.\n- A genuine `0` represents an active observation confirming zero requests.\n\nBecause the standard CLI flattens `null`s into `0`, it cannot reliably distinguish between a genuine zero-traffic period and missing/unreported telemetry. Relying on it could cause automation to falsely classify missing metrics as zero traffic.\n\n---\n\n### What to Use Instead\n\nUse the lossless request metrics reader script: **`scripts/request_metrics.py`**.\n\nThis script preserves the raw `null` vs. `0` semantics and per-series labels from upstream telemetry, enabling true three-state classification and accurate scale-to-zero eligibility checks. (Note: `scripts/request_metrics.py` is an explicit, narrow read-only exception to the standard `scripts/koyeb_env.py` launcher wrapper specifically to preserve this raw null request telemetry).\n\n#### Command Usage\n\n```bash\npython3 scripts/request_metrics.py --service-id <example-service-id> \\\n  --start 2026-01-01T00:00:00Z --end 2026-01-01T01:00:00Z --step 1h\n```",
  "error": null,
  "n_skills": 139,
  "target_pos": 27,
  "prompt_tokens": 349916,
  "completion_tokens": 1022,
  "sec": 11.973247051239014,
  "cost": 0.26626950000000005,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T19",
  "target": "narwal",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "In `narwal-local`, **`is_docked` and `charging_state` are always emitted as `null` by design** (the underlying raw protocol values in `dock_raw` and `charging_status_raw` vary by firmware and cannot be universally trusted).\n\nTo reliably determine whether the robot is docked and charging, use the **battery trend** and operational state across observations rather than relying on status boolean fields.\n\n---\n\n### Recommended Methods\n\n#### Method 1: Track Battery Trend via `watch` (Best for Real-Time Check)\nRun a bounded `watch` command to stream live broadcast telemetry:\n\n```bash\nnarwal-local watch --duration 60\n```\n*(Note: `--duration SECONDS` is required, maximum 300 seconds.)*\n\n- Inspect the `battery_percent` values across the streamed JSONL records.\n- If `battery_percent` is **increasing** over the observation window (or steady at 100% while idle in `standby` mode), the robot is docked and charging.\n\n---\n\n#### Method 2: Compare Snapshots Over Time\nIf you cannot run `watch`:\n\n1. Run `narwal-local snapshot` and record `battery_percent` and `base_query.mode`.\n2. Wait a few minutes and run `narwal-local snapshot` again.\n3. If `battery_percent` rises between snapshots, it is actively charging on the dock.\n\n---\n\n### Supporting Signals\n\n- **Operating Mode (`base_query.mode.semantic`):** When docked, the robot will typically be in `standby` (idle) with `null` task metrics (`task.area_m2: null`, `task.elapsed_seconds: null`), rather than an active cleaning mode.\n- **Dock-Only Commands:** If you need to verify readiness to start a clean, note that `narwal-local start` or `clean` calls `clean/start_clean`, which the robot will only accept when docked. If it is undocked/active, the robot will reject the command with `NOT_READY` (exit code `20`).",
  "error": null,
  "n_skills": 139,
  "target_pos": 118,
  "prompt_tokens": 349915,
  "completion_tokens": 873,
  "sec": 9.095069169998169,
  "cost": 0.26571,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T20",
  "target": "skill_opencode_data",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "When running an export with the OpenCode CLI (`opencode_skill`), subagent fan-out sessions may be missing for the following reasons:\n\n### 1. Why they are missing\n* **Default filter behavior**: By default, `opencode_skill export` explicitly skips subagent fan-out sessions to keep the export focused on main sessions.\n* **Turn requirement**: The exporter only exports sessions that contain at least one user turn. If a subagent session lacks user turns, it will not be exported.\n\n---\n\n### 2. How to include them\nTo include subagent fan-out sessions, pass 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 this with other export flags as needed (for example, limiting the time window or testing first with a dry run):\n\n```bash\n# Dry run preview\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent --dry-run\n\n# Export with a time window (e.g., past 30 days)\n.venv/bin/python -m opencode_skill export --out OUTPUT_DIR --include-subagent --since 30d\n```",
  "error": null,
  "n_skills": 139,
  "target_pos": 94,
  "prompt_tokens": 349899,
  "completion_tokens": 961,
  "sec": 9.385236263275146,
  "cost": 0.26602800000000004,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T21",
  "target": "speaker_notes",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "According to the speaker notes contract (in `speaker_notes.md`), the strict upper limit (**hard gate**) is that **no ordinary spoken sentence may exceed 22 words** (with the recommended range being between 12 and 20 words so each sentence remains easy to say in one breath).",
  "error": null,
  "n_skills": 139,
  "target_pos": 122,
  "prompt_tokens": 349897,
  "completion_tokens": 256,
  "sec": 4.634866952896118,
  "cost": 0.26338275000000005,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T22",
  "target": "skill_semantic_search",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Based on the `semantic-search` skill documentation:\n\n* **Command-line option:** Use **`--source-root`** (e.g., `--source-root /path/to/base/dir`) to specify the base directory from which relative entries in the file list should resolve.\n* **Default behavior if omitted:** If `--source-root` is omitted, relative paths are resolved against the **CLI's current working directory** (where the command is being executed), rather than the directory where the file list was generated.",
  "error": null,
  "n_skills": 139,
  "target_pos": 79,
  "prompt_tokens": 349921,
  "completion_tokens": 469,
  "sec": 6.106724977493286,
  "cost": 0.2641995,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T23",
  "target": "smart_home_skill",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "**No, you cannot.** \n\nDue to a protocol limitation with the local Tizen WebSocket API (`wss://TV_IP:8002`), **volume level and mute status are not readable**. \n\nThe integration's status endpoint (`get_status`) returns **`PowerState` only**.",
  "error": null,
  "n_skills": 139,
  "target_pos": 7,
  "prompt_tokens": 349911,
  "completion_tokens": 324,
  "sec": 5.359501838684082,
  "cost": 0.26364825000000003,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": true
 },
 {
  "id": "T24",
  "target": "skill_unifi",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "According to the `unifi-skill` documentation:\n\n1. **Exit Code:** Exit code **`10`** indicates an authentication failure caused by an expired cookie (`auth`).\n2. **Resolution:** Notify the human operator to refresh and export a new session cookie (referencing `docs/cookie_refresh.md`). This cannot be done autonomously by the agent and is strictly a manual human step. Once the human exports the new cookie into the configured path/`.env`, the CLI commands can be rerun.",
  "error": null,
  "n_skills": 139,
  "target_pos": 99,
  "prompt_tokens": 349897,
  "completion_tokens": 356,
  "sec": 5.317656755447388,
  "cost": 0.26375775,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T25",
  "target": "asr",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "In the local speech transcription pipeline (`asr-skill`), the maximum duration for a single audio file is **7,200 seconds (2 hours)**. \n\nAny single file exceeding 7,200 seconds will be **rejected before the model loads**.\n\n### Key Considerations for Long Recordings:\n- **Splitting Files**: If you have recordings longer than 2 hours, split them into separate files under 7,200 seconds before running the pipeline.\n- **Speaker Attribution**: Keep in mind that speaker labels (e.g., speaker letters) will not carry over across split slices.\n- **Total Batch Duration**: The 7,200-second limit applies strictly to individual files; you can process multiple files in a batch that collectively exceed 2 hours without issue.",
  "error": null,
  "n_skills": 139,
  "target_pos": 67,
  "prompt_tokens": 349901,
  "completion_tokens": 626,
  "sec": 9.792788028717041,
  "cost": 0.26477325,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T26",
  "target": "skill_outlook",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "If no `--timezone` argument is provided to `calendar invite` or `calendar update`, the default timezone used is **`UTC`**.\n\nImportant details regarding this behavior:\n- `--start` and `--end` are interpreted as wall-clock times in that default `UTC` zone.\n- For `calendar update`, the command defaults to `UTC` and **does not** inherit the event's existing timezone, so omitting `--timezone` when updating a local-time event will shift the meeting by the local offset.",
  "error": null,
  "n_skills": 139,
  "target_pos": 56,
  "prompt_tokens": 349906,
  "completion_tokens": 460,
  "sec": 6.045866966247559,
  "cost": 0.26415449999999996,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": true
 },
 {
  "id": "T27",
  "target": "skill_resend_email",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "### Why This Happened\n\nThe command returned a validation error because **`--after` and `--before` are pagination cursors, not timestamp filters**. \n\nWhen you pass an ISO-8601 timestamp like `2023-10-01T00:00:00Z`, the Resend API rejects the request because it expects a **UUID cursor value** representing an email record identifier from which to paginate.\n\n---\n\n### What `--after` Actually Expects\n\n* **Expected Value**: A **UUID cursor** (e.g., an email ID).\n* **Purpose**: Cursor-based pagination across pages of received emails, pointing to the record after which the next page should begin.\n\n---\n\n### How to Filter by a Date Window\n\nBecause the Resend receiving API does not support server-side date filtering via query parameters, the standard workflow is to export a page of recent emails and filter them locally:\n\n1. **Export recent emails to local Markdown files** (adjust `--limit` as needed):\n   ```bash\n   .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\n2. **Filter locally by date**:\n   Each exported Markdown file contains a `created_at` timestamp in its YAML frontmatter. You can parse or grep those files locally to select only messages within your desired time window (e.g., matching or comparing against `created_at: 2023-10-01...`).",
  "error": null,
  "n_skills": 139,
  "target_pos": 69,
  "prompt_tokens": 349931,
  "completion_tokens": 772,
  "sec": 9.158766031265259,
  "cost": 0.26534325,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T28",
  "target": "skill_firecrawl",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Depending on whether you are using **`firecrawl-skill`** or **`tavily-skill`**, the setup differs due to API constraints.\n\n---\n\n### Option 1: Firecrawl CLI (`python -m firecrawl_skill`)\n\nIn Firecrawl, the CLI flags `--include-domain` and `--exclude-domain` are **mutually exclusive** (combining both flags directly causes a constraint error).\n\nTo target specific sites while excluding another, use `--include-domain` for target sites and add the **`-site:` operator** directly inside your query text:\n\n```bash\npython -m firecrawl_skill search \"agent framework -site:unwanted.com\" \\\n  --include-domain github.com \\\n  --include-domain docs.anthropic.com\n```\n\n* **Target domains**: Repeated `--include-domain <domain>` flags.\n* **Excluded domain**: `-site:<domain>` within the quoted search query.\n\n---\n\n### Option 2: Tavily CLI (`python -m tavily_skill`)\n\nTavily allows combining both flags directly in the CLI call without mutual exclusivity restrictions:\n\n```bash\npython -m tavily_skill search \"agent framework\" \\\n  --include-domain github.com \\\n  --include-domain docs.anthropic.com \\\n  --exclude-domain unwanted.com\n```\n\n* **Target domains**: Repeat `--include-domain <domain>` for each site to focus on.\n* **Excluded domain**: Add `--exclude-domain <domain>` (repeatable if excluding multiple).\n\n---\n\n### Accessing the Results\n\nBoth CLIs do not dump full article contents directly to stdout. Instead:\n1. The command outputs a status JSON object to `stdout` containing the file path:\n   ```json\n   {\n     \"status\": \"ok\",\n     \"output_path\": \"tmp/.../search_results.json\",\n     ...\n   }\n   ```\n2. Read the full results directly from the file specified in `output_path` using Python or your script.",
  "error": null,
  "n_skills": 139,
  "target_pos": 106,
  "prompt_tokens": 349899,
  "completion_tokens": 1376,
  "sec": 12.476443767547607,
  "cost": 0.26758425,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T29",
  "target": "video-scoring-audio",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "You should avoid downmixing with `-ac 1` before measuring peak levels because **ffmpeg sums correlated stereo channels**, which boosts the measured signal by approximately **+3 dB**. \n\nFor correlated stereo (such as a mono track written to both left and right channels, common in voice-overs):\n\n1. **It causes false clipping reports:** A clean audio mix that is safely below full scale (for example, a true peak of -1.5 dBFS) will be summed above 1.0 (measured at ~1.19) and incorrectly flagged as clipped.\n2. **Proper measurement rule:** Always measure duration, peak, and clipping across **every channel independently** (`np.max(np.abs(xc))` over all raw channels), rather than after a downmix. \n\n*(Note: For an independent cross-check of integrated loudness and true peak without downmix distortion, use `ffmpeg -af ebur128=peak=true -f null -`.)*",
  "error": null,
  "n_skills": 139,
  "target_pos": 129,
  "prompt_tokens": 349892,
  "completion_tokens": 605,
  "sec": 13.615392923355103,
  "cost": 0.26468774999999994,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 },
 {
  "id": "T30",
  "target": "skill_playwright_test",
  "model": "gemini-3.8-flash",
  "condition": "full_all",
  "answer": "Based on the **Playwright E2E Test Skill**, this infinite redirect loop is caused by **stale session tokens in the browser's user data profile** (e.g., a reused Chrome CDP `--user-data-dir`).\n\n### Root Cause\n1. The browser profile directory contains outdated or invalid session tokens from a previous session or test.\n2. The SSO SDK detects the existing token on page load and attempts to use it.\n3. The backend rejects the stale token with an `HTTP 401 Unauthorized` response.\n4. The SSO SDK tries to refresh or re-authenticate, receives another 401, and re-triggers the authentication cycle—causing the page to oscillate endlessly between \"loading\" and \"signing in\".\n\n---\n\n### How to Fix\n\n- **Clean the browser profile before starting Chrome**:\n  Remove the existing debug profile directory so the browser starts in a completely clean state:\n  ```bash\n  rm -rf /tmp/pw_debug_profile\n  ```\n\n- **Use isolated contexts in automated tests**:\n  Ensure Playwright creates fresh contexts rather than reusing persistent storage:\n  ```python\n  context = await browser.new_context()\n  ```",
  "error": null,
  "n_skills": 139,
  "target_pos": 60,
  "prompt_tokens": 349912,
  "completion_tokens": 530,
  "sec": 11.259485960006714,
  "cost": 0.2644215,
  "web_tools": [],
  "n_calls": 1,
  "finish_reason": "stop",
  "nudged": false
 }
]