[ { "id": "T01", "target": "bestpractice_pdf_to_markdown", "model": "gemini-3.8-flash", "condition": "none", "answer": "### Cause of the Crash\n\nThe crash during layout detection under the MPS (Metal Performance Shaders) backend on Apple Silicon is typically caused by **unsupported tensor operators or dynamic-shape/memory allocation bugs in PyTorch's MPS implementation**. \n\nDocling's default layout analysis models (such as RT-DETR or LayoutLM variants) rely on specific computer vision layers (e.g., deformable convolutions, dynamic grids, or operations lacking complete native MPS kernels in PyTorch). When PyTorch attempts to execute these unsupported kernels on the Apple GPU via MPS, it triggers an unhandled kernel failure, out-of-memory assertion, or a segmentation fault.\n\n---\n\n### Workaround\n\nTo resolve this issue, force Docling to run inference on the CPU rather than using the default GPU/MPS acceleration.\n\n#### Command-Line Flag\nPass the **`--device cpu`** flag to the `docling` CLI:\n\n```bash\ndocling --device cpu path/to/document.pdf\n```\n\n---\n\n### Additional / Alternative Solutions\n\n* **Environment Variable Fallback:** If you prefer to attempt GPU acceleration where possible while letting unsupported operations fall back to the CPU automatically, set:\n ```bash\n export PYTORCH_ENABLE_MPS_FALLBACK=1\n docling path/to/document.pdf\n ```\n *(Note: If the crash is caused by a memory allocator bug or hard segmentation fault inside Metal, this environment variable may not prevent the crash, making `--device cpu` the most reliable fix.)*\n\n* **Python API:** If running Docling in Python, specify the device in the pipeline options:\n ```python\n from docling.datamodel.base_models import InputFormat\n from docling.datamodel.pipeline_options import PdfPipelineOptions\n from docling.document_converter import DocumentConverter, PdfFormatOption\n\n pipeline_options = PdfPipelineOptions()\n pipeline_options.accelerator_options.device = \"cpu\"\n\n converter = DocumentConverter(\n format_options={\n InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)\n }\n )\n ```", "error": null, "prompt_tokens": 96, "completion_tokens": 1533, "sec": 19.489661931991577, "cost": 0.00582075, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T02", "target": "mac_universal_clipboard", "model": "gemini-3.8-flash", "condition": "none", "answer": "Yes. Continuity and Handoff rely on your **Apple ID Identity Certificate** and Identity Services (IDS) keys stored in your local keychain. If this certificate becomes desynchronized or corrupted, Universal Clipboard fails even when signed in.\n\nYou can delete this certificate from Keychain Access to force macOS to negotiate a fresh identity key with Apple's servers without having to fully sign out of iCloud.\n\n---\n\n### Step 1: Delete the Apple ID Identity Certificate\n\n1. Open **Keychain Access** (found in `/Applications/Utilities/` or via Spotlight).\n2. In Keychain Access:\n * On newer macOS versions, click **login** under \"Default Keychains\" in the sidebar, then switch to the **My Certificates** tab.\n * If you don't see it there, select **All Items** and type your **Apple ID email address** in the search bar.\n3. Look for a certificate named with your Apple ID (typically displayed as `Apple ID: your_email@example.com` or simply `your_email@example.com`, issued by an Apple CA such as `Apple ID Personal CA`).\n4. Click the disclosure arrow next to it to ensure you see the paired private key.\n5. Right-click the certificate and choose **Delete \"Apple ID: [email]\"**. Confirm deletion of both the certificate and the associated private key.\n\n*(Note: Do not delete any root CA certificates or third-party developer certificates; only delete the personal Apple ID identity certificate).*\n\n---\n\n### Step 2: Clear Stale IDS / Handoff Caches\n\nBecause the background daemons keep the cryptographic identity cached in memory, deleting the keychain item won't immediately take effect until you restart the relevant services.\n\n1. Open **Terminal** (`/Applications/Utilities/Terminal.app`).\n2. Run the following command to kill the Handoff, Activity, and Clipboard daemons:\n ```bash\n killall sharingd useractivityd pboard\n ```\n *(macOS will immediately relaunch these processes automatically in the background).*\n\n---\n\n### Step 3: Trigger Key Regeneration\n\n1. Go to **System Settings** > **General** > **AirDrop & Handoff**.\n2. Toggle **Allow Handoff between this Mac and your iCloud devices** **Off**, wait ~10 seconds, then toggle it back **On**.\n3. Do the same on your iPhone/iPad (**Settings** > **General** > **AirPlay & Handoff**).\n4. Allow 1–2 minutes for the new identity certificate to regenerate (you can refresh Keychain Access to see the new `Apple ID` certificate populate under \"My Certificates\").\n5. Test Universal Clipboard by copying text on one device and pasting on the other.", "error": null, "prompt_tokens": 109, "completion_tokens": 2614, "sec": 31.293898820877075, "cost": 0.00988425, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T03", "target": "openreview", "model": "gemini-3.8-flash", "condition": "none", "answer": "To resolve the HTTP 403 error caused by an expired token, you need to re-authenticate and refresh your locally cached session. \n\nFollow these steps:\n\n### 1. Re-authenticate using the CLI\nRun the OpenReview login command:\n\n```bash\nopenreview login\n```\n\nWhen prompted:\n1. Enter your OpenReview **email/username**.\n2. Enter your **password**.\n\nThis will generate a new authentication token and overwrite the expired token stored in your local configuration file (typically located at `~/.openreview`).\n\n---\n\n### 2. If the issue persists (Token Cache Conflict)\nIf the CLI fails to overwrite the old token automatically, clear the cached configuration manually and log in again:\n\n1. **Remove the expired token file:**\n ```bash\n rm ~/.openreview\n ```\n *(On Windows, delete `C:\\Users\\\\.openreview`)*\n\n2. **Log in again:**\n ```bash\n openreview login\n ```\n\n---\n\n### Alternative: Supply Credentials Directly\nIf you are running automated scripts or want to bypass the stored token entirely, you can:\n\n* **Pass credentials inline** with your query command:\n ```bash\n openreview --username \"\" --password \"\"\n ```\n* **Set environment variables:**\n ```bash\n export OPENREVIEW_USERNAME=\"your-email@example.com\"\n export OPENREVIEW_PASSWORD=\"your-password\"\n ```", "error": null, "prompt_tokens": 98, "completion_tokens": 909, "sec": 15.227504968643188, "cost": 0.00348225, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T04", "target": "workflow_bilibili_whisper_transcription", "model": "gemini-3.8-flash", "condition": "none", "answer": "To prevent triggering Bilibili’s rate-limiting, risk-control challenges (Geetest), or temporary IP blocks (HTTP 412 / 429), you should implement a **dynamic delay with randomized jitter** rather than a fixed interval.\n\n---\n\n### 1. Recommended Interval Timing\n\n* **Standard Batching (20–50 tracks):** \n Maintain an interval of **8 to 15 seconds** between downloads, randomized per request (e.g., uniform random between 8.0 and 15.0s).\n* **Large Batches (100+ tracks):** \n Increase the interval to **15 to 30 seconds** per track.\n* **Batch Cool-downs (Macro-pauses):** \n Do not run uninterrupted sequences. Every **20 to 25 downloads**, introduce an extended sleep of **2 to 5 minutes** to emulate natural user browsing sessions.\n* **Concurrency:** \n Keep concurrency to **1 download at a time** per IP address. Simultaneous multi-threaded requests to Bilibili's media CDN endpoints are one of the fastest ways to trigger an IP ban.\n\n---\n\n### 2. Implementation Pattern (Python Example)\n\n```python\nimport random\nimport time\n\ndef safe_batch_download(track_list):\n for i, track in enumerate(track_list):\n download_audio(track)\n \n # Micro-pause between tracks (Randomized)\n jitter = random.uniform(8.0, 16.0)\n time.sleep(jitter)\n \n # Macro-pause every 20 downloads\n if (i + 1) % 20 == 0 and (i + 1) != len(track_list):\n cooldown = random.uniform(120.0, 300.0)\n print(f\"Batch checkpoint reached. Cooling down for {cooldown:.1f}s...\")\n time.sleep(cooldown)\n```\n\n---\n\n### 3. Essential Safeguards Alongside Timing\n\nEven with safe intervals, Bilibili’s CDN and API will block requests if standard validation criteria are missing:\n\n1. **Required Headers:**\n * **`Referer`:** Must be set to `https://www.bilibili.com/` (or the specific video page URL). Requests directly accessing the audio stream without this header return `HTTP 403 Forbidden` instantly.\n * **`User-Agent`:** Use a modern desktop browser UA string.\n2. **Account Cookies (`SESSDATA`):**\n * While unauthenticated requests can download low-bitrate audio, they are subject to much stricter IP rate limits.\n * If passing `SESSDATA` (logged-in session), avoid overly aggressive speeds, as this risks flagging or temporarily locking your account rather than just your IP.\n3. **Handle HTTP 412 / 429 Gracefully:**\n * If you receive HTTP `412 Precondition Failed` or `429 Too Many Requests`, Bilibili has flagged your session.\n * Immediately halt downloads and implement an **exponential backoff** starting at **10 to 15 minutes**. Continuing to poll during a block resets and extends the penalty window.", "error": null, "prompt_tokens": 90, "completion_tokens": 1470, "sec": 20.410858869552612, "cost": 0.00558, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T05", "target": "playwright_ajax_capture", "model": "gemini-3.8-flash", "condition": "none", "answer": "When an API returns a tracking identifier like `creation_uuid` instead of a resource `id`, it usually indicates an **asynchronous creation pattern** (job/worker queue) or a **CQRS (Command Query Responsibility Segregation)** architecture.\n\nHere is how you can reliably uncover and obtain the real resource ID:\n\n---\n\n### 1. Trace the Official Client’s Network Waterfall (Most Direct)\nSince you are reverse-engineering an internal API, the official frontend (web app, mobile app, or desktop client) must also solve this problem.\n\n1. Open DevTools (Network tab) or an intercepting proxy (Burp Suite, Charles, Proxyman).\n2. Trigger the resource creation in the UI.\n3. Observe all network requests made **immediately after** the `POST` request:\n - **Polling an operation/task endpoint:** Look for repeated requests like `GET /api/tasks/{creation_uuid}` or `GET /api/jobs/{creation_uuid}`. The payload of the completed task almost always contains `resource_id` or `id`.\n - **Direct UUID lookup:** Look for calls like `GET /api/resources/{creation_uuid}` or `GET /api/resources?creation_uuid={...}` where the backend resolves the UUID alias into the full model.\n - **Collection refresh:** Look for a `GET /api/resources?sort=created_at:desc&limit=1` or similar listing query right after creation.\n\n---\n\n### 2. Inspect Response Headers & HTTP Status Code\nAPIs following REST standards often communicate resource locations through headers rather than the JSON body:\n\n* **HTTP 202 Accepted:** Signals asynchronous processing. Check the **`Location`** header. It often points to a status URL (e.g., `Location: /api/tasks/abc-123`) or directly to the pending resource URL.\n* **Custom Headers:** Inspect response headers for fields such as `X-Resource-ID`, `X-Entity-ID`, or `Location`.\n\n---\n\n### 3. Check for Status/Task Endpoints\nIf the backend uses background workers (Celery, BullMQ, Sidekiq, etc.), creation is a multi-step process:\n\n1. **Submit creation:** Returns `{ \"creation_uuid\": \"...\", \"status\": \"pending\" }`.\n2. **Poll task status:**\n ```http\n GET /api/tasks/{creation_uuid}\n # or\n GET /api/operations/{creation_uuid}\n # or\n GET /api/jobs/{creation_uuid}\n ```\n3. **Parse result:** When `\"status\": \"completed\"` or `\"succeeded\"`, the response payload typically contains:\n ```json\n {\n \"status\": \"completed\",\n \"result\": {\n \"id\": 12345\n }\n }\n ```\n *Tip:* If polling, implement an exponential backoff with a timeout (e.g., poll every 500ms–1s up to 10 seconds).\n\n---\n\n### 4. Query by `creation_uuid` on the Main Resource Endpoint\nMany backends store the `creation_uuid` (or client idempotency key) in the database alongside the primary key:\n\n* Try fetching the resource directly using the UUID:\n ```http\n GET /api/resources/{creation_uuid}\n ```\n* Try filtering the list endpoint:\n ```http\n GET /api/resources?uuid={creation_uuid}\n GET /api/resources?creation_uuid={creation_uuid}\n GET /api/resources?correlation_id={creation_uuid}\n ```\n\n---\n\n### 5. Correlate via Unique Attributes / Listing Endpoint\nIf no task endpoint exists and the UUID is purely an ephemeral tracking token:\n\n1. When sending the creation payload, include a unique, temporary string in an arbitrary field (e.g., an unusual name, unique tag, or metadata field like `\"tag\": \"probe_\"`).\n2. Query the collection endpoint:\n ```http\n GET /api/resources?sort=-created_at&limit=5\n ```\n or filter by the unique marker.\n3. Match the returned item by your unique marker to extract its assigned integer/canonical `id`.\n\n---\n\n### 6. Check WebSockets or Server-Sent Events (SSE)\nIf the web client shows the new resource instantly without polling HTTP endpoints:\n\n1. Look in the Network tab under the **WS** (WebSocket) or **EventStream** filters.\n2. Many modern architectures push completion events down to the client:\n ```json\n {\n \"event\": \"RESOURCE_CREATED\",\n \"creation_uuid\": \"...\",\n \"id\": \"real_id_9876\"\n }\n ```\n3. If this is the case, you will need to open a persistent connection alongside your HTTP calls to listen for creation acknowledgments.", "error": null, "prompt_tokens": 116, "completion_tokens": 1470, "sec": 15.964047193527222, "cost": 0.0055995, "web_tools": [], "n_calls": 1, "nudged": true }, { "id": "T06", "target": "workflow_watchdog", "model": "gemini-3.8-flash", "condition": "none", "answer": "The recommended default wake-up interval for checking in on long-running background sub-agents is **30 to 60 seconds** (with **60 seconds / 1 minute** being the standard baseline for tasks running longer than a few minutes).\n\nSetting the interval in this range provides an optimal balance: it prevents excessive API token consumption and rate limiting caused by tight polling loops, while remaining responsive enough to handle completion or failures without introducing unacceptable latency into your workflow.\n\n---\n\n### Recommended Implementation Steps\n\n#### 1. Set the Baseline Interval\n* **Short-to-medium tasks (1–5 minutes):** Use **30 seconds**.\n* **Long-running tasks (5+ minutes):** Use **60 seconds** as the default check-in heartbeat.\n* **Very long / batch jobs (30+ minutes):** Increase the interval to **2–5 minutes** to minimize unnecessary wake-up overhead.\n\n#### 2. Implement Exponential Backoff with a Cap\nInstead of a purely static interval, use an exponential backoff pattern if you do not know the exact runtime:\n1. Start with an initial check at **15–30 seconds** (in case a sub-agent fails immediately or completes a trivial subtask).\n2. Double the interval on successive checks (e.g., 30s $\\rightarrow$ 60s $\\rightarrow$ 120s).\n3. Cap the maximum interval at **3 to 5 minutes** so the coordinator does not sleep too long once the task finishes.\n\n#### 3. Use Asynchronous Callbacks / Notifications When Possible\nIf your orchestrator framework supports it (e.g., event listeners, webhooks, or task queues like Celery/Temporal):\n* Prefer **push-based notifications** (sub-agent emits a `completed` or `failed` event) over active polling.\n* Use the wake-up interval strictly as a **heartbeat / watchdog timer** to detect frozen, hanging, or orphaned agents rather than as the primary completion trigger.\n\n#### 4. Keep Check-Ins \"Lightweight\"\nWhen the parent agent wakes up:\n* Query task status endpoints or check shared memory/state (e.g., `status: RUNNING | COMPLETED | FAILED`) rather than requesting full sub-agent message histories or logs.\n* Only retrieve verbose output or sub-agent artifacts once the task status transitions to `COMPLETED`.", "error": null, "prompt_tokens": 93, "completion_tokens": 1753, "sec": 22.38733220100403, "cost": 0.0066435, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T07", "target": "workflow_parallel_subagents", "model": "gemini-3.8-flash", "condition": "none", "answer": "While setting `temperature = 0` (greedy decoding) seems like an intuitive way to make subagents predictable and deterministic, **defaulting to 0 across subagents often decreases overall system resilience and success rates.**\n\nHere is why you should not make `temperature = 0` your default, followed by actionable steps to configure temperatures properly.\n\n---\n\n### Why Temperature = 0 Can Harm Subagent Stability\n\n1. **The \"Retry Trap\" (Deterministic Failure Loops)**\n * **The Problem:** Subagents frequently interact with tools, environments, or code interpreters. If an agent executes an invalid tool call or encounters an error, the agent loop typically feeds the error message back to the agent to try again.\n * **The Failure:** At `temperature = 0`, the model selects the single highest-probability token at every step. If the prompt and error context do not strongly shift the probability distribution, the subagent will generate the exact same (or nearly identical) flawed output repeatedly until hitting token or iteration limits.\n * **Result:** Total workflow failure instead of self-correction.\n\n2. **Brittleness in Complex Reasoning and Edge Cases**\n * **The Problem:** The most likely token path is not always the correct one, especially in multi-step planning, mathematical reasoning, or edge-case debugging.\n * **The Failure:** Greedy decoding prevents the model from exploring alternative branches of thought. At a non-zero temperature, a slight perturbation early in the generation can allow the model to follow a viable alternative reasoning path.\n\n3. **Repetition and Hallucination Lock-In**\n * **The Problem:** When an LLM begins to repeat a phrase or enter a structural loop (such as endless markdown formatting or repeated JSON keys), greedy decoding heavily biases subsequent tokens to reinforce that loop.\n * **The Failure:** A non-zero temperature introduces enough sampling variance to break repetitive token cycles.\n\n4. **False Guarantee of Determinism**\n * **The Problem:** Developers often use `temperature = 0` assuming it guarantees 100% reproducible outputs.\n * **The Reality:** In modern distributed LLM inference (e.g., mixture-of-experts models, dynamic batching, GPU floating-point non-associativity), true bitwise determinism is rarely achieved even at `temperature = 0`. Relying on it for functional stability builds architecture on a false premise.\n\n5. **Loss of Diversity in Multi-Agent Collaboration**\n * **The Problem:** Subagents often serve specialized roles—such as red-teaming, critique, code review, or exploratory data analysis.\n * **The Failure:** If all subagents operate at `temperature = 0`, their outputs tend to be homogenous and predictable, defeating the purpose of an ensemble or multi-perspective review.\n\n---\n\n### Recommended Implementation Steps\n\nInstead of setting a global default of `0`, configure subagent temperatures dynamically based on task type and error states.\n\n#### Step 1: Assign Temperatures by Subagent Role\n* **Strict Extraction / Schema Mapping (0.0 – 0.1):** Use for subagents whose sole job is extracting fields into structured JSON or parsing raw text where creativity is harmful.\n* **Tool Calling / Execution (0.1 – 0.3):** Use a low, near-zero temperature. This retains high precision and adherence to tool definitions while preventing hard deterministic lockups.\n* **Planning / Debugging / Problem-Solving (0.4 – 0.7):** Use a moderate temperature so the subagent can discover alternative approaches when the standard path fails.\n* **Critique / Red-Teaming (0.7 – 0.9):** Use higher sampling so the subagent generates novel counterarguments or test cases.\n\n#### Step 2: Implement Dynamic Temperature Backoff (Retry Escalation)\nIf a subagent fails a tool call, validation step, or unit test, do not query it with identical parameters. Increase the temperature on subsequent attempts:\n\n```python\ndef execute_subagent_with_retry(subagent, prompt, max_retries=3):\n base_temp = 0.1\n temp_step = 0.2\n\n for attempt in range(max_retries):\n current_temp = min(base_temp + (attempt * temp_step), 0.8)\n \n response = subagent.run(prompt, temperature=current_temp)\n validation_result = validate_output(response)\n \n if validation_result.is_valid:\n return response\n \n # Append error to the context for the next attempt\n prompt = append_error_context(prompt, validation_result.error_message)\n\n raise RuntimeError(\"Subagent failed to produce valid output after retries.\")\n```\n\n#### Step 3: Enforce Stability via Constraints, Not Temperature\nIf you are using `temperature = 0` to prevent formatting errors or invalid responses, address the root cause with structural enforcement instead:\n* Use **Structured Outputs / JSON Schema validation** (e.g., grammar-based decoding, Pydantic/OpenAI JSON mode).\n* Use **system prompt guardrails** and negative constraints.\n* Add **explicit unit-test assertions** at the orchestration layer before accepting a subagent's answer.", "error": null, "prompt_tokens": 77, "completion_tokens": 1800, "sec": 24.410073280334473, "cost": 0.0068077499999999996, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T08", "target": "bestpractice_markdown_html_conversion", "model": "gemini-3.8-flash", "condition": "none", "answer": "The recommended minimum horizontal padding for mobile screens is **`16px` (or `1rem`)**. \n\nA baseline of **`16px`** aligns with major mobile design systems (including Apple Human Interface Guidelines and Google Material Design). It provides enough breathing room to prevent text from touching device bezels while maximizing the usable reading area on narrow screens (360px–390px wide).\n\n---\n\n### Recommended CSS Implementation\n\nTo apply this properly to your Markdown documentation, follow these steps:\n\n#### 1. Reset `box-sizing`\nEnsure padding doesn't push the container width beyond `100vw`, which causes unwanted horizontal scrolling.\n\n```css\n*, *::before, *::after {\n box-sizing: border-box;\n}\n```\n\n#### 2. Apply Padding and Max-Width to the Container\nApply the mobile-first padding to your `
` tag, article container, or ``. Pair it with a `max-width` (typically `65ch` to `75ch` or `700px`–`800px`) so documentation lines do not become uncomfortably long on larger screens:\n\n```css\n/* If applying directly to a wrapper or
*/\n.markdown-body,\nmain {\n width: 100%;\n max-width: 48rem; /* ~768px: comfortable reading width on desktop */\n margin-left: auto;\n margin-right: auto;\n \n /* 16px horizontal padding minimum on mobile */\n padding: 1.5rem 1rem; \n}\n\n/* Optional: Slightly increase breathing room on tablets/desktops */\n@media (min-width: 640px) {\n .markdown-body,\n main {\n padding-left: 1.5rem; /* 24px */\n padding-right: 1.5rem;\n }\n}\n```\n\n#### 3. Account for Mobile Notches and Curved Edges (Safe Area)\nModern mobile devices (especially iPhones in landscape orientation) require support for safe area insets so text is not obscured by hardware notches or home bars:\n\n```css\n.markdown-body,\nmain {\n /* Uses 1rem minimum, but expands if the hardware safe area is larger */\n padding-left: max(1rem, env(safe-area-inset-left));\n padding-right: max(1rem, env(safe-area-inset-right));\n}\n```\n\nMake sure your HTML `` includes the viewport meta tag with `viewport-fit=cover` to enable safe area calculations:\n```html\n\n```", "error": null, "prompt_tokens": 100, "completion_tokens": 1074, "sec": 15.051857233047485, "cost": 0.004102500000000001, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T09", "target": "codex_cli", "model": "gemini-3.8-flash", "condition": "none", "answer": "To disable `web_search` in your configuration file and resolve the HTTP 400 error, follow these specifications:\n\n---\n\n### 1. Value Type Required\n* **Value Type:** **Boolean** (`false`).\n* **Critical Note on Types:** \n * Do **not** use a string (`\"false\"`, `'false'`, or `\"disabled\"`). In JSON and YAML parsers, non-empty strings evaluate as truthy or fail strict schema validation.\n * Do **not** use an integer (`0` or `1`).\n\n---\n\n### 2. Configuration File Syntax\n\n#### If your configuration is in **YAML** (e.g., `config.yaml`, `librechat.yaml`, or tool config):\nEnsure the value is written as a bare boolean:\n```yaml\nweb_search: false\n```\n*(Make sure there are no quotation marks around `false`.)*\n\n#### If your configuration is in **JSON** (e.g., `config.json`, `settings.json`):\nEnsure the value is a native JSON boolean literal:\n```json\n{\n \"web_search\": false\n}\n```\n\n---\n\n### 3. Special Cases Based on Backend Behavior\n\nDepending on your front-end/gateway and how strict your backend schema validation is:\n\n#### Case A: Backend rejects the key entirely (Strict Schema / `additionalProperties: false`)\nIf your custom backend strictly adheres to standard OpenAI specifications and rejects requests simply because the `web_search` key exists in the payload (even when set to `false`):\n1. **Omit the key entirely:** Delete the `web_search` line from the model parameters/request body configuration.\n2. **If using LibreChat (`librechat.yaml`):** Use `dropParams` to strip unsupported parameters before they reach the backend. The value type must be an **Array / List of strings**:\n ```yaml\n endpoints:\n custom:\n - name: \"custom-openai\"\n baseURL: \"http://your-backend:port/v1\"\n apiKey: \"your-key\"\n dropParams: [\"web_search\"]\n ```\n\n#### Case B: If using LiteLLM Proxy (`config.yaml`)\nIf requests pass through a LiteLLM proxy to your OpenAI-compatible backend, enable parameter dropping so unsupported keys are automatically stripped. The value type is a **Boolean**:\n```yaml\nlitellm_settings:\n drop_params: true\n```\n\n---\n\n### Step-by-Step Instructions to Apply\n\n1. Open your configuration file (`.yaml` or `.json`).\n2. Locate the model/endpoint definition or the search setting block.\n3. Update `web_search` to the boolean `false` (or remove the parameter / add it to `dropParams` if the backend forbids unknown keys).\n4. Save the file.\n5. Restart your service or client application to reload the configuration.", "error": null, "prompt_tokens": 105, "completion_tokens": 5497, "sec": 52.35096096992493, "cost": 0.0206925, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T10", "target": "apple_photos", "model": "gemini-3.8-flash", "condition": "none", "answer": "The maximum number of image pairs allowed in a single delete manifest when planning deletions with the Apple Photos deduplication tool is **50 image pairs**.\n\n### Key Rules & Handling Steps\n\n1. **Batching Beyond 50 Pairs:**\n * If you have identified more than 50 duplicate pairs to delete, you must divide them into multiple batches.\n * Generate separate delete manifests for each batch of up to 50 pairs and execute/plan them sequentially.\n\n2. **Constructing the Manifest:**\n * Ensure each entry in the manifest clearly specifies the target pair (the image asset to retain and the duplicate asset to be removed).\n * Double-check asset IDs to avoid deletion conflicts within the same batch.\n\n3. **Executing Planned Deletions:**\n * Generate the manifest first using the planning command/function.\n * Review the generated manifest for accuracy before applying or confirming the deletions to your Apple Photos library.", "error": null, "prompt_tokens": 81, "completion_tokens": 2316, "sec": 27.074750185012817, "cost": 0.00874575, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T11", "target": "colab", "model": "gemini-3.8-flash", "condition": "none", "answer": "In version 0.7.4 of the Colab CLI, passing an unrecognized or misspelled value to the `--gpu` flag does not trigger a client-side validation error. Instead, the CLI and backend handle it as follows:\n\n### 1. The CLI Passes the Value Straight Through\nThe CLI does not enforce a strict local `choices` validation list on the `--gpu` argument. It accepts the string as provided, packages it into the session creation request payload, and transmits it directly to the Colab backend API.\n\n### 2. The Backend Falls Back to the Default GPU\nBecause the backend recognizes that an accelerator was requested (via the presence of the flag) but cannot match the specified string to a specific premium tier (such as `a100`, `v100`, or `l4`), it defaults to standard accelerator allocation:\n* **Fallback Hardware:** It provisions the default standard GPU tier—typically an **Nvidia T4** (or whatever base GPU is currently the default standard for your account tier).\n* **Execution:** Your command or session proceeds normally without failing, but running on standard T4 compute units rather than the intended hardware.\n\n---\n\n### How to Check and Correct It\n\n1. **Verify the active GPU inside the runtime:**\n If you want to confirm which accelerator was actually attached, run:\n ```bash\n colab exec -- nvidia-smi\n ```\n *(Or within a notebook cell: `!nvidia-smi`)*\n\n2. **Valid accelerator identifiers:**\n To ensure you receive the intended hardware, use the exact supported identifiers (case-insensitive in most commands):\n * `t4`\n * `v100`\n * `a100`\n * `l4`", "error": null, "prompt_tokens": 107, "completion_tokens": 2308, "sec": 21.43028688430786, "cost": 0.008735250000000002, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T12", "target": "dataforseo", "model": "gemini-3.8-flash", "condition": "none", "answer": "In the DataForSEO API, error code **`40104`** translates to **\"Access Denied\" / \"Endpoint Restricted\"**. \n\nYour balance check succeeds because account and metadata endpoints (such as `/v3/appendix/user_data`) are accessible to all valid credentials regardless of account status or funding. However, keyword endpoints (like the Keywords Data API, SERP API, or DataForSEO Labs) have strict access controls.\n\n---\n\n### Root Causes\n\n1. **Unfunded Account / Initial Deposit Required**\n * While newly registered accounts receive a small trial credit, several keyword endpoints (particularly Keywords Data API, Google Ads search volume, and DataForSEO Labs) require a verified account with a minimum initial deposit (typically $50) before access is unlocked.\n2. **Endpoint Not Enabled for Your Account**\n * Certain keyword data modules (e.g., Google Ads integrations, certain live scrape endpoints) must be activated manually for your account or require specific subscription permissions.\n3. **Sandbox Limitations**\n * The DataForSEO sandbox (`sandbox.dataforseo.com`) **does not accept arbitrary keywords**. It only responds to the exact, hardcoded sample payloads published in their documentation. Sending custom parameters or arbitrary keywords to the sandbox will trigger access or validation errors. Additionally, some keyword endpoints are disabled entirely in the sandbox environment.\n\n---\n\n### How to Fix It\n\n#### Step 1: Check Your Balance and Top Up\n1. Log in to the [DataForSEO Dashboard](https://app.dataforseo.com/).\n2. Verify that your balance is greater than `$0.00`.\n3. If you are on a free trial or zero-balance account, make an initial top-up. Most keyword endpoints will automatically unlock once a funded balance is detected.\n\n#### Step 2: Contact Support to Activate the Endpoint\nDataForSEO provides 24/7 live chat support via their dashboard:\n1. Open the live chat widget on the bottom right of the dashboard.\n2. Provide your account email and specify the exact endpoint you are querying (e.g., `v3/keywords_data/google_ads/search_volume/live`).\n3. Request activation for that specific module. Support typically enables restricted endpoints within minutes.\n\n#### Step 3: Test Correctly Against the Sandbox (If Not Using Live)\nIf you must test against the sandbox environment:\n* Do not submit arbitrary keywords.\n* Copy the **exact sample request body** directly from the DataForSEO documentation for your specific endpoint.\n* Verify if the specific keyword endpoint supports sandbox mode; if it does not, test directly against the live endpoint with a funded account.\n\n#### Step 4: Verify CLI Target Configuration\nEnsure your CLI is pointed to the live API rather than the sandbox once your account is activated:\n* **Live Base URL:** `https://api.dataforseo.com/v3/`\n* Ensure your CLI credentials match the API login (email) and API password (secret token generated under **API Dashboard > API Access**), rather than your web dashboard password.", "error": null, "prompt_tokens": 103, "completion_tokens": 2786, "sec": 27.62737798690796, "cost": 0.01052475, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T13", "target": "firewalla", "model": "gemini-3.8-flash", "condition": "none", "answer": "The flag you need to specify is:\n\n`--query-box` *(or `queryBox=true` if passing parameters directly to the underlying MSP API)*.\n\n---\n\n### Why is this flag needed?\n\n1. **Cloud-First Caching Architecture:**\n The Firewalla CLI communicates with the Firewalla Cloud / MSP (Managed Service Provider) backend rather than directly establishing a local network connection to your hardware. By default, read operations (such as listing devices or alarms) pull from a **cached snapshot** stored in the cloud.\n\n2. **Appliance Resource Protection:**\n Firewalla gateways are embedded devices handling high-throughput routing, packet inspection, and filtering. Defaulting to cloud-cached data prevents frequent CLI scripts or polling operations from consuming the physical box's CPU, memory, and bandwidth.\n\n3. **Handling Network Latency and Offline States:**\n Querying the physical box requires the cloud backend to open a secure tunnel and request live status from the device in real-time. If the box is busy or briefly disconnected, direct queries can lag or time out. \n\nWhen you append `--query-box`, you explicitly instruct the cloud service to bypass the cache, reach down to the physical appliance, and pull the live, real-time state.", "error": null, "prompt_tokens": 100, "completion_tokens": 1208, "sec": 11.924032926559448, "cost": 0.004605000000000001, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T14", "target": "apple_calendar", "model": "gemini-3.8-flash", "condition": "none", "answer": "### 1. Which Path and File to Copy\n\nOn macOS, the active calendar database is:\n\n```\n~/Library/Calendars/Calendar Cache\n```\n\nAlong with its companion Write-Ahead Log (WAL) files:\n* `~/Library/Calendars/Calendar Cache-wal`\n* `~/Library/Calendars/Calendar Cache-shm`\n\n*(Note: Despite having no file extension, `Calendar Cache` is a standard SQLite 3 database.)*\n\n---\n\n### 2. Why `Calendar.sqlitedb` Returned Empty Results\n\n1. **macOS vs. iOS Naming Convention:** \n On iOS, the primary calendar database is named `Calendar.sqlitedb`. On macOS, Apple uses `Calendar Cache` for the unified local database populated by `CalendarAgent` and EventKit.\n2. **Stub / Legacy File:** \n A `Calendar.sqlitedb` file under `~/Library/Calendars/` is typically a legacy remnant or an unpopulated template created during system initialization or migration. Because it has the schema tables initialized, queries execute without database errors but return zero rows.\n3. **Missing WAL Files (Write-Ahead Logging):** \n SQLite databases on macOS run in WAL mode. Even when copying the correct database, if you omit the `-wal` and `-shm` companion files, any data not yet committed to the main database file will be absent.\n\n---\n\n### 3. How to Copy and Query the Calendar Correctly\n\n#### Step 1: Grant Full Disk Access (if using Terminal)\nmacOS protects `~/Library/Calendars/` via TCC (Transparency, Consent, and Control). If your terminal or script cannot read the directory:\n1. Open **System Settings** > **Privacy & Security** > **Full Disk Access**.\n2. Enable your terminal emulator (e.g., Terminal, iTerm) or your IDE/Python binary.\n\n#### Step 2: Copy the Database and WAL Files Together\nCopy all three associated files to a working directory:\n\n```bash\nmkdir -p ~/Desktop/CalBackup\ncp ~/Library/Calendars/Calendar\\ Cache* ~/Desktop/CalBackup/\n```\n\n#### Step 3: Query the Database\nOpen the copied file with SQLite:\n\n```bash\nsqlite3 ~/Desktop/CalBackup/Calendar\\ Cache\n```\n\nTo view events, query the `CalendarItem` table (where macOS stores events, reminders, and occurrences):\n\n```sql\nSELECT summary, start_date, end_date FROM CalendarItem WHERE summary IS NOT NULL;\n```\n\n*(Note: Apple stores timestamps as seconds since January 1, 2001 (Core Data / Mac absolute time). To convert to Unix time, add `978307200` seconds.)*", "error": null, "prompt_tokens": 109, "completion_tokens": 3215, "sec": 27.746427059173584, "cost": 0.012138, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T15", "target": "skill_image_generation", "model": "gemini-3.8-flash", "condition": "none", "answer": "When generating infographics with GPT Image / multimodal generative models, you should avoid relying on text-only prompts. Instead, use a **Hybrid Layout-First Workflow** (or **Wireframe-Conditioned Workflow**), combining structured visual inputs with post-generation vector assembly.\n\n---\n\n### The Workflow to Use\n\nFollow this four-step process:\n\n#### Step 1: Draft and Lock Content First (Text LLM)\n* Do not ask the image model to compose copy on the fly. \n* Use a text prompt first to produce the exact hierarchy:\n * **Header:** Title and one-sentence subtitle.\n * **Data points:** 3–5 bullet points or quantitative metrics.\n * **Visual anchors:** Descriptions of icons/illustrations needed for each point.\n\n#### Step 2: Create a Low-Fidelity Spatial Wireframe\n* Create a simple, high-contrast visual layout grid using a tool like Figma, Canva, PowerPoint, or even a pen-and-paper sketch.\n* Block out the zones using colored rectangles:\n * Top banner for title.\n * 3–4 distinct content boxes or cards.\n * Clear blank spaces where text and charts will live.\n* Save this wireframe as an image (PNG/JPG).\n\n#### Step 3: Run Image Generation with Multimodal Conditioning\n* Upload your wireframe image to the model alongside your prompt.\n* Instruct the model to treat the uploaded image strictly as a **spatial and compositional guide**.\n* **Prompt formula:**\n > *\"Use the attached image strictly as a structural wireframe for layout, spacing, and panel positioning. Generate an infographic in [specific art style, e.g., clean modern vector, isometric 3D, corporate flat art] following this exact arrangement. Style the background, borders, and thematic illustrations inside the panels. Do not render final paragraph text or complex numbers—leave panels clean for typography.\"*\n\n#### Step 4: Final Overlay in a Design Tool (Figma, Canva, or Illustrator)\n* Bring the generated graphic into your design tool.\n* Place the real, editable text, exact percentage labels, and clean vector icons directly on top of the generated panels.\n\n---\n\n### Why This Workflow Is Necessary (vs. Text Alone)\n\n1. **Elimination of Text Hallucination and Garbled Glyphs**\n * While modern models can render short words, generating dense text, subtitles, and exact figures in an infographic from a single prompt almost always results in spelling errors, unreadable characters, and inconsistent font sizes.\n\n2. **Precise Spatial Hierarchy and Alignment**\n * Text-only prompts struggle with complex spatial reasoning (e.g., *\"place 4 evenly spaced boxes with equal padding and aligned headers\"*). Feeding the model a reference sketch or layout establishes strict spatial boundaries that the model’s attention layers can anchor to.\n\n3. **Factual and Data Accuracy**\n * Generative image models cannot plot real data. A pie chart or bar graph generated from text will depict arbitrary proportions. By generating the visual background first and placing data charts/numbers manually, you ensure data integrity.\n\n4. **Editability and Versioning**\n * If a single metric, title, or typo changes in a pure text-to-image output, you have to re-roll the prompt, losing the layout you liked. Separating the visual asset generation from the typography allows you to update content instantly without re-generating the image.", "error": null, "prompt_tokens": 86, "completion_tokens": 1706, "sec": 20.51277208328247, "cost": 0.006462000000000001, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T16", "target": "skill_intake", "model": "gemini-3.8-flash", "condition": "none", "answer": "By default, Apple Voice Memos intake sync tools and scripts natively scan for **`.m4a`** files (MPEG-4 Audio encoded with AAC or ALAC), as this is the only native format Apple Voice Memos creates and stores in its database folder.\n\nDepending on the specific intake script, plugin, or pipeline you are using, supported extensions usually fall into one of two categories:\n\n### 1. File Extensions Typically Accepted\n* **Primary / Native:**\n * `.m4a`\n* **Extended (if the intake tool supports general audio):**\n * `.aac`\n * `.caf` (Apple Core Audio Format)\n * `.mp3`\n * `.wav`\n * `.aif` / `.aiff`\n\n---\n\n### 2. Common Reasons Recordings Are Being Skipped\n\nIf you have recordings in the folder that are not syncing, check the following common causes:\n\n1. **Case-Sensitivity (`.m4a` vs. `.M4A`)**\n * Many sync scripts use case-sensitive matching. If a recording has an uppercase `.M4A` extension, a script looking strictly for `.m4a` will ignore it.\n2. **iCloud Placeholder Files (`.icloud`)**\n * If **Optimize Mac Storage** is enabled, macOS offloads the actual audio file to iCloud and leaves a stub named something like `.RecordingName.m4a.icloud`.\n * The intake sync will not recognize this as an audio file because its extension is `.icloud`.\n * **Fix:** Double-click or open the file in Finder to force macOS to download the local copy before running the sync.\n3. **In-Progress or Temporary Files**\n * Voice Memos temporarily writes recordings with auxiliary extensions like `.tmp` or `.mov` metadata wrappers while actively recording or syncing across iCloud. Sync tools intentionally ignore these until the file finalized into `.m4a`.\n4. **SQLite Manifest Filtering**\n * If your sync tool inspects the Voice Memos database (`CloudRecordings.db` / `Recordings.db`) rather than doing a raw directory scan, files manually dropped into the recordings folder without a corresponding database entry will be skipped.\n\n---\n\n### Steps to Fix Skipped Files\n\n1. **Inspect the skipped file extensions:**\n * Open Terminal and run:\n ```bash\n ls -la \"~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings\"\n ```\n *(Or check your custom source folder path).*\n2. **Ensure all files end in lowercase `.m4a`:**\n * If any end in `.M4A`, rename them to lowercase `.m4a`.\n3. **Download any `.icloud` files:**\n * If you see files starting with a `.` and ending with `.icloud`, trigger their download:\n ```bash\n brctl download /path/to/source/folder/*.icloud\n ```", "error": null, "prompt_tokens": 85, "completion_tokens": 1422, "sec": 14.782032012939453, "cost": 0.00539625, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T17", "target": "watch_development", "model": "gemini-3.8-flash", "condition": "none", "answer": "On watchOS 9 and later, Apple hides the **Developer Mode** toggle by default to protect everyday users. It will not appear in **Settings > Privacy & Security** until the watch has been actively targeted for development using a Mac running Xcode.\n\nFollow these steps to reveal and activate the Developer Mode option:\n\n---\n\n### Step 1: Prepare Your Devices\n1. Ensure your Mac has **Xcode** installed (available free from the Mac App Store).\n2. Ensure your Apple Watch is paired to your iPhone, unlocked, and on your wrist or charger.\n3. Make sure Wi-Fi and Bluetooth are turned on across your Mac, iPhone, and Apple Watch.\n\n---\n\n### Step 2: Connect via Xcode\n1. Connect your **paired iPhone** to your Mac using a USB-to-Lightning or USB-C cable.\n2. If prompted on the iPhone, tap **Trust This Computer** and enter your passcode.\n3. Open **Xcode** on your Mac.\n4. In Xcode, go to the top menu bar and select **Window > Devices and Simulators** (or press `Shift + Command + 2`).\n5. Select your iPhone in the left sidebar. Under your iPhone's details, verify that your **Apple Watch** is listed and connected.\n6. Allow Xcode a minute or two to finish \"Preparing device for development\" or fetching symbols for the watch.\n\n*(Note: If you have a watchOS project in Xcode, selecting your Apple Watch as the run destination and clicking **Run / Play** will immediately trigger the system to expose the Developer Mode menu.)*\n\n---\n\n### Step 3: Turn on Developer Mode on the Watch\nOnce Xcode has communicated with the watch:\n\n1. On your **Apple Watch**, open the **Settings** app.\n2. Scroll down and tap **Privacy & Security**.\n3. Scroll to the very bottom — **Developer Mode** should now be visible.\n4. Tap **Developer Mode**, then toggle the switch to **On**.\n5. Scroll down and tap **Restart**.\n\n---\n\n### Step 4: Confirm Activation\n1. After the Apple Watch reboots, unlock it.\n2. A prompt will appear asking to confirm enabling Developer Mode: tap **Turn On**.\n3. Enter your watch **passcode** to complete the setup.", "error": null, "prompt_tokens": 96, "completion_tokens": 1236, "sec": 12.09265923500061, "cost": 0.004706999999999999, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T18", "target": "koyeb", "model": "gemini-3.8-flash", "condition": "none", "answer": "### Why You Shouldn't Rely on the Standard Koyeb CLI for Traffic Checks\n\nRelying on the Koyeb CLI (e.g., `koyeb service get` or CLI metrics commands) in automated scaling pipelines is discouraged for several reasons:\n\n1. **Human-Oriented Formatting:** The Koyeb CLI is designed for interactive human display. Its output format, column layout, and metric summaries (such as truncated numbers, metric prefixes like `m` for millirequests, or averaged rates) can change across CLI versions, making text scraping brittle.\n2. **Lack of Granular Window Queries:** The CLI typically exposes current snapshot metrics or predefined aggregate periods rather than letting you strictly verify a sliding time window (e.g., *\"confirm zero requests across all instances between $T - 15m$ and $T$ with a 1-minute step\"*).\n3. **No Native Zero-Traffic Exit Status:** The CLI returns exit code `0` as long as the CLI command itself succeeds, regardless of whether traffic is flowing. To evaluate traffic values, you would need complex regex or string parsing.\n4. **Direct API Consistency:** Koyeb's underlying REST API (`https://api.koyeb.com/v1/metrics`) provides exact, structured JSON time-series data (`samples: [[timestamp, value], ...]`), making it deterministic and light on dependencies in CI/CD or cron runners (requiring only `curl` and `jq`).\n\n---\n\n### Recommended Script\n\nUse a script that queries the Koyeb REST API directly for the `http_throughput` metric over a defined lookback window (e.g., the last 15 minutes). If any data point exceeds `0`, it flags active traffic.\n\n#### Prerequisites\n- `curl` and `jq` installed on the host running the automation.\n- A Koyeb API token with read permissions (`KOYEB_TOKEN`).\n- Your Koyeb Service ID (`KOYEB_SERVICE_ID`).\n\n#### `check_zero_traffic.sh`\n\n```bash\n#!/usr/bin/env bash\nset -euo pipefail\n\n# Configuration\nKOYEB_TOKEN=\"${KOYEB_TOKEN:?Error: KOYEB_TOKEN is not set}\"\nKOYEB_SERVICE_ID=\"${KOYEB_SERVICE_ID:?Error: KOYEB_SERVICE_ID is not set}\"\nLOOKBACK_MINUTES=\"${LOOKBACK_MINUTES:-15}\"\nSTEP=\"${STEP:-1m}\"\n\n# Calculate UTC start and end timestamps in RFC 3339 format\nEND_TIME=$(date -u +\"%Y-%m-%dT%H:%M:%SZ\")\nif date -v -1M >/dev/null 2>&1; then\n # macOS / BSD date\n START_TIME=$(date -u -v -\"${LOOKBACK_MINUTES}\"M +\"%Y-%m-%dT%H:%M:%SZ\")\nelse\n # GNU date\n START_TIME=$(date -u -d \"${LOOKBACK_MINUTES} minutes ago\" +\"%Y-%m-%dT%H:%M:%SZ\")\nfi\n\necho \"Checking traffic for service: ${KOYEB_SERVICE_ID}\"\necho \"Window: ${START_TIME} to ${END_TIME} (step: ${STEP})\"\n\n# Query the Koyeb Metrics API for HTTP throughput\nRESPONSE=$(curl -sS -X GET \"https://api.koyeb.com/v1/metrics\" \\\n -H \"Authorization: Bearer ${KOYEB_TOKEN}\" \\\n -H \"Content-Type: application/json\" \\\n -G \\\n --data-urlencode \"service_id=${KOYEB_SERVICE_ID}\" \\\n --data-urlencode \"name=http_throughput\" \\\n --data-urlencode \"start=${START_TIME}\" \\\n --data-urlencode \"end=${END_TIME}\" \\\n --data-urlencode \"step=${STEP}\")\n\n# Check for API error responses\nif echo \"${RESPONSE}\" | jq -e '.message' >/dev/null 2>&1; then\n echo \"API Error: $(echo \"${RESPONSE}\" | jq -r '.message')\" >&2\n exit 2\nfi\n\n# Extract and sum all sample values across all series returned\nTOTAL_ACTIVITY=$(echo \"${RESPONSE}\" | jq '[.metrics[].samples[][1] // 0] | add // 0')\n\necho \"Sum of throughput values across window: ${TOTAL_ACTIVITY}\"\n\n# Evaluate activity\nif (( $(echo \"${TOTAL_ACTIVITY} == 0\" | bc -l) )); then\n echo \"SUCCESS: Zero traffic detected over the last ${LOOKBACK_MINUTES} minutes. Safe to scale down.\"\n exit 0\nelse\n echo \"WARNING: Traffic detected (${TOTAL_ACTIVITY}). Do not scale down.\"\n exit 1\nfi\n```\n\n### How to Use\n\n1. Make the script executable:\n ```bash\n chmod +x check_zero_traffic.sh\n ```\n\n2. Run it in your automation pipeline:\n ```bash\n export KOYEB_TOKEN=\"your-api-token\"\n export KOYEB_SERVICE_ID=\"your-service-id\"\n export LOOKBACK_MINUTES=15 # Optional, defaults to 15\n\n if ./check_zero_traffic.sh; then\n echo \"Proceeding with scale-down...\"\n # Call scale down command or API\n else\n echo \"Service is still handling traffic. Skipping scale down.\"\n fi\n ```\n\n### Exit Codes\n- `0`: No traffic recorded within the selected lookback window; safe to scale down.\n- `1`: Non-zero traffic detected.\n- `2`: API failure or misconfiguration.", "error": null, "prompt_tokens": 107, "completion_tokens": 2710, "sec": 27.093878030776978, "cost": 0.010242750000000002, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T19", "target": "narwal", "model": "gemini-3.8-flash", "condition": "none", "answer": "When `is_docked` and `charging_state` return `null`, it usually indicates that your vacuum's firmware version reports these states under different or unmapped raw telemetry keys. \n\nTo determine if the vacuum is docked and charging using the CLI, follow these steps:\n\n---\n\n### 1. Inspect the Raw Snapshot Payload\nBypass the top-level normalized fields and inspect the raw telemetry message directly using `--raw`:\n\n```bash\nnarwal-local snapshot --raw | jq .\n```\n\nLook for the underlying hardware properties in the JSON output, particularly under nested keys such as `station`, `base`, `device`, or `state`:\n\n* **Docked Status:** Look for keys such as:\n * `\"in_station\": true`\n * `\"dock_status\": 1`\n * `\"station_type\"` / `\"base_connected\": true`\n * `\"distance_to_dock\": 0`\n* **Charging Status:** Look for keys such as:\n * `\"battery_charging\": true`\n * `\"power_state\": \"charging\"`\n * `\"charge_status\": 1` (or `2`, depending on model)\n\n---\n\n### 2. Check the Device `work_status` or Mode Code\nNarwal robots report a numeric state machine code for their current operation mode. Query the state enum directly:\n\n```bash\nnarwal-local snapshot | jq '.state // .status // .work_mode // .device_status'\n```\n\nCommon numeric state codes on Narwal devices:\n* **Idle / Docked:** `0` or `1` (Idle at station)\n* **Charging:** Typically reflected when the robot is idle (`0` or `1`) and connected to station power, or has a sub-state code for charging/drying mop pads (`5` or `charging`).\n* **Active Cleaning:** Typically `2` (sweeping), `3` (mopping), or `4` (returning to dock).\n\n---\n\n### 3. Stream Live MQTT/Telemetry Events\nIf snapshot values are cached or unpopulated until an event occurs, stream live events to see state transitions when the vacuum is placed onto or leaves the dock:\n\n```bash\nnarwal-local monitor\n```\n*(or `narwal-local listen` depending on the version)*\n\nWatch for incoming topic messages such as:\n* Base station handshakes (`station/status` or `base/info`).\n* Battery delta updates (if charging, updates will show battery levels with an active charging flag or incrementing percentage).\n\n---\n\n### 4. Verify via Battery Level Drift\nIf the firmware completely suppresses charging flags in local mode:\n1. Run `narwal-local snapshot | jq .battery` to get the current percentage.\n2. Wait 2–3 minutes and run the command again.\n3. If the battery percentage rises (or stays locked at `100`), the robot is drawing power from the dock pins.", "error": null, "prompt_tokens": 106, "completion_tokens": 1663, "sec": 16.859833002090454, "cost": 0.006315749999999999, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T20", "target": "skill_opencode_data", "model": "gemini-3.8-flash", "condition": "none", "answer": "When exporting OpenCode sessions to markdown, missing subagent (fan-out) sessions typically occur due to how OpenCode tracks session hierarchies and the default scope of the export command.\n\n---\n\n### Why Subagent Fan-Out Sessions Are Missing\n\n1. **Default Root-Level Filtering:**\n By default, OpenCode exports operate strictly on top-level (root) sessions. Subagent fan-outs are spawned as child sessions linked to a parent session ID. The export process often treats subagent activity simply as tool calls within the parent log, omitting the child session threads themselves from markdown generation.\n\n2. **Session Storage Separation:**\n OpenCode isolates parallel or fan-out subagent executions into distinct session records (each with its own `session_id` and a `parent_session_id`). Unless recursive traversal is enabled, the exporter will not iterate through linked child IDs.\n\n3. **Ephemeral or Uncommitted Child Runs:**\n If the subagents were invoked in an ephemeral context or completed without committing an explicit session title/index entry, workspace-level export queries that look for standard session registries will skip them.\n\n---\n\n### How to Include Subagent Sessions\n\nFollow these steps and options to ensure all child and fan-out sessions are included in the markdown output:\n\n#### 1. Use Recursive or Subagent Flags on Export\nCheck if your installed version of the OpenCode CLI supports recursive or subagent inclusion flags during export:\n\n```bash\n# Include all child/subagent sessions recursively\nopencode export --recursive --output ./markdown-export\n\n# Or specifically flag subagents/child sessions (depending on version)\nopencode export --include-subagents --output ./markdown-export\n```\n\nIf you are exporting all sessions across a project or workspace rather than a single thread, use:\n```bash\nopencode export --all --include-children --output ./markdown-export\n```\n\n#### 2. Query and Export Child Session IDs Directly\nIf your version does not recursively traverse child sessions automatically:\n\n1. **Find the parent session and child IDs:**\n Inspect the parent session metadata or query the session list:\n ```bash\n opencode session list --all\n ```\n Look for session entries where the `parent_id` matches your main orchestrator session.\n\n2. **Export child sessions by ID:**\n You can batch-export the identified subagent session IDs into the target directory:\n ```bash\n for id in $(opencode session list --parent --json | jq -r '.[].id'); do\n opencode export --session \"$id\" --output ./markdown-export\n done\n ```\n\n#### 3. Inspect Session Storage Directly\nIf child sessions were recorded in the local OpenCode database/cache (typically under `~/.opencode/sessions` or SQLite storage in your project directory):\n\n1. Check `~/.opencode/sessions/` for JSON/storage files corresponding to subagent executions.\n2. If the sessions are stored as raw JSON, you can convert them directly to markdown via the CLI by passing the raw session path:\n ```bash\n opencode export --file ~/.opencode/sessions/.json --output ./markdown-export/\n ```\n\n#### 4. Configure Subagent Trace Persistence\nTo prevent fan-out sessions from being treated as transient background traces in future runs:\n* Update your OpenCode configuration (`opencode.json` or config file) to ensure child/subagent traces are persisted as standalone sessions:\n ```json\n {\n \"subagents\": {\n \"persistSessions\": true,\n \"exportable\": true\n }\n }\n ```", "error": null, "prompt_tokens": 90, "completion_tokens": 1781, "sec": 19.573824167251587, "cost": 0.00674625, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T21", "target": "speaker_notes", "model": "gemini-3.8-flash", "condition": "none", "answer": "In professional speechwriting and presentation design, the strict upper limit for an ordinary spoken sentence is **20 words** (with an absolute ceiling of **25 words** only if heavily punctuated with pauses). \n\nWhen spoken aloud, sentences longer than 20 words cause the speaker to run out of breath and force the audience to hold too many clauses in short-term memory without the visual aid of written punctuation.\n\n---\n\n### Practical Steps to Edit Speaker Notes Under the Limit\n\n1. **Target the Sweet Spot (10–15 words)**\n * While 20 is the maximum cap, aim for an **average sentence length of 10 to 15 words**. Varying length between short punches (4–7 words) and medium thoughts (10–14 words) creates a dynamic rhythm.\n\n2. **Apply the \"One-Breath\" Test**\n * Read every note out loud at your intended speaking pace. If you cannot deliver the sentence comfortably in a single breath without rushing or straining, it is too long.\n\n3. **Break Compound Sentences at Conjunctions**\n * Identify connecting words like *and*, *which*, *because*, *however*, *whereas*, or *so*.\n * Replace the conjunction and comma with a period.\n * *Example (27 words):* \"Our revenue increased significantly across all regions in Q3, but our supply chain costs rose faster, which ultimately led to an overall contraction in our net profit margin.\"\n * *Revised (Two sentences: 10 and 11 words):* \"Our revenue increased significantly across all regions in Q3. However, rising supply chain costs ultimately contracted our net margin.\"\n\n4. **Eliminate Subordinate Clauses**\n * Cut introductory clauses or parenthetical phrases. Put the subject and verb as close to the beginning of the sentence as possible.\n\n5. **Format for Spoken Delivery**\n * Write one complete spoken thought per line or bullet point.\n * Use bold text on keywords for visual anchoring so you can look up from the notes and back down without losing your place.", "error": null, "prompt_tokens": 88, "completion_tokens": 999, "sec": 9.11162781715393, "cost": 0.0038122500000000005, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T22", "target": "skill_semantic_search", "model": "gemini-3.8-flash", "condition": "none", "answer": "To define the base directory for relative paths in a file list when using `semantic-search`, use the **`--base-dir`** (or in some implementations, **`--base-path`**) option.\n\n### Summary:\n* **Option to set the base directory:** \n `--base-dir ` (e.g., `semantic-search --file-list list.txt --base-dir /path/to/project`)\n* **Default behavior when omitted:** \n If the option is omitted, the tool resolves relative paths against the **current working directory** (the directory from which the command is executed).", "error": null, "prompt_tokens": 112, "completion_tokens": 1069, "sec": 12.99707293510437, "cost": 0.00409275, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T23", "target": "smart_home_skill", "model": "gemini-3.8-flash", "condition": "none", "answer": "**Yes, you can.** When using the Samsung SmartThings REST API (the standard smart home integration for Samsung TVs), both the volume level and mute status are exposed under the device’s status endpoint.\n\nSpecifically, they map to standard SmartThings capabilities:\n* **`audioVolume`**: contains the `volume` attribute (an integer, typically `0`–`100`).\n* **`audioMute`**: contains the `mute` attribute (a string value: `\"muted\"` or `\"unmuted\"`).\n\n---\n\n### Step-by-Step Instructions\n\n#### 1. Prerequisites\n* A SmartThings **Personal Access Token (PAT)** with device read permissions (`r:devices:*`). You can generate one at the [SmartThings Developer Workspace / Tokens page](https://account.smartthings.com/tokens).\n* Your TV’s **Device ID** (if you don’t have it, list your devices via `GET https://api.smartthings.com/v1/devices`).\n\n---\n\n#### 2. Call the Device Status Endpoint\nSend an authorized `GET` request to the device status endpoint:\n\n```bash\ncurl -X GET \"https://api.smartthings.com/v1/devices/{deviceId}/status\" \\\n -H \"Authorization: Bearer YOUR_PERSONAL_ACCESS_TOKEN\" \\\n -H \"Accept: application/json\"\n```\n\n*(Alternatively, to fetch only those specific capabilities rather than the full TV status, you can query:*\n* `GET https://api.smartthings.com/v1/devices/{deviceId}/components/main/capabilities/audioVolume/status`\n* `GET https://api.smartthings.com/v1/devices/{deviceId}/components/main/capabilities/audioMute/status`*)*\n\n---\n\n#### 3. Parse the Response\nIn the full status JSON response, navigate down to the `main` component:\n\n```json\n{\n \"components\": {\n \"main\": {\n \"audioVolume\": {\n \"volume\": {\n \"value\": 18,\n \"unit\": \"%\",\n \"timestamp\": \"2025-01-15T12:00:00.000Z\"\n }\n },\n \"audioMute\": {\n \"mute\": {\n \"value\": \"unmuted\",\n \"timestamp\": \"2025-01-15T12:00:00.000Z\"\n }\n }\n }\n }\n}\n```\n\n* **Volume Level:** `response.components.main.audioVolume.volume.value`\n* **Mute State:** `response.components.main.audioMute.mute.value` (`\"muted\"` or `\"unmuted\"`)\n\n---\n\n### Important Considerations for Automations\n* **TV Power State:** When the TV is powered off (in standby) and not connected to ambient Wi-Fi features, the API may either report the last-known cached values or return offline state errors. Check `components.main.switch.switch.value` to ensure it is `\"on\"` before acting on the values.\n* **External Audio / Soundbars:** If your Samsung TV outputs audio via eARC/Optical to an external AV receiver, the TV may report fixed levels or rely on HDMI-CEC. Ensure your TV's SmartThings device reflects internal CEC-synced volume if you are controlling an external sound system.", "error": null, "prompt_tokens": 102, "completion_tokens": 1367, "sec": 14.343893051147461, "cost": 0.00520275, "web_tools": [], "n_calls": 1, "nudged": true }, { "id": "T24", "target": "skill_unifi", "model": "gemini-3.8-flash", "condition": "none", "answer": "In UniFi controller observation scripts and CLI monitoring utilities (such as API-based collectors and polling tools), an authentication failure caused by an expired session or stale cookie is typically represented by exit code **`2`** (Authentication/Authorization Failure) or HTTP **`401`** (which maps to non-zero exit codes such as **`2`** or **`3`** depending on the specific wrapper implementation).\n\n---\n\n### How to Resolve the Expired Cookie\n\nFollow these steps to re-authenticate and restore the session:\n\n#### 1. Invalidate/Remove the Stale Cookie\nDelete the existing cached cookie file used by the CLI tool:\n```bash\n# Look for the stored cookie in the tool's working directory or standard cache paths\nrm -f /tmp/unifi_cookie\n# or (depending on your configuration)\nrm -f ~/.unifi/cookie.txt\n```\n\n#### 2. Re-authenticate to Generate a Fresh Cookie\nLog back into the UniFi Controller / UniFi OS console using your administrator (or read-only monitoring) credentials to obtain a new session cookie:\n\n* **For UniFi OS (UDM, UDR, Cloud Key Gen2 with UniFi OS v2+ / v3+):**\n ```bash\n curl -s -k -c /tmp/unifi_cookie -X POST https:///api/auth/login \\\n -H \"Content-Type: application/json\" \\\n -d '{\"username\": \"\", \"password\": \"\"}'\n ```\n\n* **For Legacy Standalone UniFi Network Controllers:**\n ```bash\n curl -s -k -c /tmp/unifi_cookie -X POST https://:8443/api/login \\\n -H \"Content-Type: application/json\" \\\n -d '{\"username\": \"\", \"password\": \"\"}'\n ```\n\n* **If using the tool's built-in login subcommand:**\n ```bash\n unifi-cli login --host --user --password \n ```\n\n#### 3. Re-run the Observation Command\nExecute your observation CLI tool again, ensuring it references the new cookie file or uses the updated session:\n```bash\n# Example passing the fresh cookie path\nunifi-cli observe --cookie-jar /tmp/unifi_cookie\n```\n\n#### 4. Prevent Future Failures (Best Practices)\n* **Wrap with Auto-Reauth:** If running the observation CLI as a cron job or daemon, add a check to inspect the exit code. If exit code `2` / `401` is returned, trigger the login routine automatically before retrying the observation query.\n* **Use API Keys (UniFi OS 3.x+):** If your controller version supports API tokens, switch authentication from session cookies to a persistent API key (`X-API-KEY` header), which does not expire with regular user session timeouts.", "error": null, "prompt_tokens": 88, "completion_tokens": 2216, "sec": 22.898263931274414, "cost": 0.008376, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T25", "target": "asr", "model": "gemini-3.8-flash", "condition": "none", "answer": "In standard local, offline transcription pipelines (such as those based on OpenAI Whisper, Faster-Whisper, Vosk, or Kaldi), **there is typically no hard-coded duration limit that causes the engine itself to reject a file**. Most modern local engines process audio by streaming or chunking it internally (for example, Whisper processes audio in 30-second windows).\n\nHowever, whether a file is rejected or crashes depends on the **pipeline architecture, RAM/VRAM constraints, and ingestion wrappers**.\n\n---\n\n### Key Limits by Setup\n\n1. **Direct Python / CLI Engines (Whisper, Faster-Whisper):**\n * **Duration Limit:** None by default. You can process recordings that are 5–10+ hours long.\n * **Failure Point:** Memory exhaustion (OOM). If the pipeline loads the entire decoded audio waveform into system RAM at once (as a 16 kHz float32 array), extremely long files can cause the process to be killed by the operating system.\n\n2. **Local Web Interfaces / REST APIs (e.g., LocalAI, Whisper WebUI, FastAPI wrappers):**\n * **Duration / Size Limit:** Often constrained by the web server's upload size rather than audio length (commonly defaulted to **25 MB, 100 MB, or 2 GB**).\n * **Rejection:** Files exceeding the payload limit will return an HTTP `413 Payload Too Large` error.\n\n3. **Audio Decoder Limits (FFmpeg / PyAV):**\n * Very long recordings (typically exceeding **12–24 hours** in a single file) can suffer from timestamp overflow or drift during internal resampling to 16 kHz mono.\n\n---\n\n### Steps to Verify and Prepare Your Pipeline\n\nFollow these steps to ensure your long interview recordings process reliably without rejection or crashes:\n\n#### Step 1: Check for Configured Upload or Validation Limits\nIf you are running the pipeline via an API or Web interface, inspect the configuration files for explicit validation guards:\n* **FastAPI / Flask:** Search code for `MAX_CONTENT_LENGTH` or explicit duration checks (e.g., `audio.duration > ...`).\n* **Nginx / Reverse Proxy:** Look for `client_max_body_size` (often set to a default of 1 MB or 10 MB if not explicitly raised).\n\n#### Step 2: Normalize Audio Format Before Ingestion\nLong uncompressed formats (like 24-bit 96 kHz stereo WAV) consume massive amounts of memory during decoding. Convert your interviews to standard transcription-ready format (**16 kHz, mono, 16-bit**):\n\n```bash\nffmpeg -i input_interview.mp3 -ar 16000 -ac 1 -c:a pcm_s16le normalized_interview.wav\n```\n*A 1-hour 16 kHz 16-bit mono WAV file is only ~115 MB, making it very light on memory.*\n\n#### Step 3: Segment Files Exceeding 2–3 Hours (Recommended)\nWhile local engines can theoretically run indefinitely, processing a single file longer than 2–3 hours increases the risk of hallucination loops, timestamp drift, or losing all progress if the job fails midway. \n\nSplit large files into 60-minute segments using `ffmpeg` without re-encoding:\n\n```bash\nffmpeg -i input_interview.mp3 -f segment -segment_time 3600 -c copy chunk_%03d.mp3\n```\n\n#### Step 4: Monitor VRAM / RAM Usage\nRun a test with one of your mid-length files (e.g., 1 hour) while monitoring system resources:\n* **Linux/macOS:** Use `htop` for system RAM.\n* **Nvidia GPUs:** Run `watch -n 1 nvidia-smi` to ensure memory does not spike and trigger a CUDA Out-of-Memory error.", "error": null, "prompt_tokens": 92, "completion_tokens": 1703, "sec": 19.410365104675293, "cost": 0.00645525, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T26", "target": "skill_outlook", "model": "gemini-3.8-flash", "condition": "none", "answer": "When using `calendar invite` or `calendar update` without providing the `--timezone` flag, the command defaults to the **local system timezone** of the machine executing the command (typically determined by your operating system settings or the `TZ` environment variable).\n\n### Practical Tips:\n\n* **Explicitly specify a timezone:**\n To avoid ambiguity, you can supply an IANA timezone identifier directly:\n ```bash\n calendar invite --timezone \"America/New_York\" ...\n calendar update --timezone \"UTC\" ...\n ```\n\n* **Check your current local timezone:**\n * **Linux / macOS:** Run `date +\"%Z %z\"` or check `echo $TZ`.\n * **Linux (systemd):** Run `timedatectl`.\n\n* **Temporarily override the default timezone for a single command:**\n You can prepend the `TZ` environment variable:\n ```bash\n TZ=\"UTC\" calendar invite ...\n ```", "error": null, "prompt_tokens": 97, "completion_tokens": 1123, "sec": 10.135656118392944, "cost": 0.0042840000000000005, "web_tools": [], "n_calls": 1, "nudged": true }, { "id": "T27", "target": "skill_resend_email", "model": "gemini-3.8-flash", "condition": "none", "answer": "The validation error occurred because the `--after` flag in this CLI does not accept an ISO-8601 date string. \n\n### Why This Happened and What `--after` Expects\n\nIn modern email and cloud service CLIs, the `--after` flag typically falls into one of two patterns:\n\n#### 1. It is a Pagination Cursor, Not a Date Filter (Most Likely)\nMost email/REST APIs use **cursor-based pagination** rather than offset pagination. In these systems:\n* **What it expects:** An **object/record ID** (such as an email ID or UUID, e.g., `re_123abc` or `msg_abc123`), not a timestamp.\n* **How it works:** It returns the batch of records that occur *after that specific record* in the list.\n* **Why it failed:** When you passed `2023-10-01T00:00:00Z`, the CLI's schema validator rejected it because the value did not match the expected ID format (e.g., UUID or prefixed string).\n\n#### 2. It Expects a Unix Epoch Timestamp\nIf the tool *does* use `--after` for date filtering:\n* **What it expects:** An integer representing the **Unix epoch timestamp** (seconds or milliseconds since Jan 1, 1970).\n* **Why it failed:** The parser expected an integer (e.g., `1696118400`) instead of an ISO string format.\n\n---\n\n### How to Resolve It\n\n#### Step 1: Check the command's help documentation\nRun the `--help` flag on the command to see the exact schema and whether a dedicated date flag exists:\n```bash\nreceived list --help\n```\nLook for:\n* The description of `--after` (whether it says \"cursor / starting ID\" or \"timestamp\").\n* Time-filtering flags, which are commonly named `--since`, `--from`, `--created-after`, or `--start-date`.\n\n#### Step 2: If `--after` is a pagination cursor\nIf you need to paginate through records:\n1. Fetch the initial list:\n ```bash\n received list --limit 50\n ```\n2. Copy the ID of the last email returned in the list.\n3. Pass that ID to `--after`:\n ```bash\n received list --after \n ```\n\n#### Step 3: If filtering by date is not supported directly by the API\nIf the CLI only supports cursor pagination and cannot filter by time server-side, fetch the list and filter client-side using `jq` (assuming JSON output):\n\n```bash\n# Example filtering for emails created on or after 2023-10-01:\nreceived list --limit 100 | jq '.[] | select(.created_at >= \"2023-10-01T00:00:00Z\")'\n```\n\n#### Step 4: If the tool expects a Unix Epoch Timestamp\nConvert your target date to seconds and retry:\n```bash\n# Convert 2023-10-01T00:00:00Z to Unix epoch (1696118400)\nreceived list --after 1696118400\n```", "error": null, "prompt_tokens": 122, "completion_tokens": 2413, "sec": 21.38999891281128, "cost": 0.00914025, "web_tools": [], "n_calls": 1, "nudged": false }, { "id": "T28", "target": "skill_firecrawl", "model": "gemini-3.8-flash", "condition": "none", "answer": "To focus a search on specific domains while excluding another from the command line, you combine standard search operators (`site:` and `-site:`) inside CLI search tools like **`ddgr`** (DuckDuckGo), **`googler`** (Google), or a direct **`curl`** call.\n\n---\n\n### The Search Operator Syntax\n\nRegardless of which tool you run, search engines parse domain filters using the following logic:\n\n* **Include specific domains:** `(site:domain1.com OR site:domain2.com)`\n* **Exclude a domain:** `-site:badsite.com`\n* **Combined with keywords:** \n ```text\n \"my search terms\" (site:github.com OR site:gitlab.com) -site:gist.github.com\n ```\n\n---\n\n### Method 1: Using `ddgr` (DuckDuckGo CLI)\n\n`ddgr` is a lightweight CLI tool to search DuckDuckGo directly from your terminal.\n\n1. **Install `ddgr`** (if not already installed):\n * Ubuntu/Debian: `sudo apt install ddgr`\n * macOS (Homebrew): `brew install ddgr`\n * Arch Linux: `sudo pacman -S ddgr`\n\n2. **Run the search:**\n Pass the entire query string enclosed in quotes so the shell does not misinterpret parentheses or minus signs:\n ```bash\n ddgr \"python automation (site:github.com OR site:gitlab.com) -site:gist.github.com\"\n ```\n\n---\n\n### Method 2: Using `googler` (Google CLI)\n\n`googler` provides Google search results inside the terminal.\n\n1. **Install `googler`**:\n * Ubuntu/Debian: `sudo apt install googler`\n * macOS (Homebrew): `brew install googler`\n\n2. **Run the search:**\n ```bash\n googler \"docker networking (site:stackoverflow.com OR site:serverfault.com) -site:meta.stackoverflow.com\"\n ```\n\n---\n\n### Method 3: Using `curl` with DuckDuckGo (No Extra Packages Required)\n\nIf you cannot install third-party packages, you can fetch search results directly via `curl` and parse them:\n\n```bash\nQUERY='python async (site:github.com OR site:gitlab.com) -site:gist.github.com'\n\ncurl -s -G \"https://html.duckduckgo.com/html/\" \\\n --data-urlencode \"q=$QUERY\" \\\n -H \"User-Agent: Mozilla/5.0\" | grep -oP '(?<=` or `Cookie` header actually being sent?\n * If using cookies: Look at the **Application / Storage -> Cookies** tab. Is the session cookie present? Check its `SameSite`, `Secure`, and `Domain` properties.\n * If using `SameSite`, ensure it is set to `Lax` (or `None; Secure` if cross-site).\n\n#### Step 3: Check Backend Logs for Exact 401 Reason\nLook at the backend auth middleware logs. Typical errors include:\n* `\"No authorization header provided\"` $\\rightarrow$ Frontend issue (missing token / omitted credentials).\n* `\"jwt expired\"` or `\"jwt not active yet\"` $\\rightarrow$ Server clock skew or misconfigured token lifetime.\n* `\"jwt audience invalid\"` $\\rightarrow$ Backend SSO configuration mismatch.\n* `\"unable to verify signature\"` $\\rightarrow$ Key mismatch / network failure reaching the IdP JWKS endpoint.\n\n#### Step 4: Configure CORS & Credentials Properly\nIf the frontend and backend are on different subdomains/ports:\n* **Frontend:**\n ```javascript\n // Axios\n axios.defaults.withCredentials = true;\n\n // Fetch\n fetch(url, { credentials: 'include' });\n ```\n* **Backend:** Ensure CORS headers explicitly allow the origin and credentials:\n * `Access-Control-Allow-Credentials: true`\n * `Access-Control-Allow-Origin: https://app.example.com` *(cannot be `*` when credentials are included)*.", "error": null, "prompt_tokens": 103, "completion_tokens": 2043, "sec": 23.854100942611694, "cost": 0.007738500000000001, "web_tools": [], "n_calls": 1, "nudged": false } ]