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improvement(logfire): scope block outputs per operation and refresh brand chrome
- gate each block output on the operations that actually return it
- swap in the official Logfire mark, black tile with brand-magenta bare icon
- move host to advanced mode and alphabetize the tool registry entries
- add track-logfire-llm-cost and verify-logfire-token-target skills
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'Base URL of your Logfire instance. Leave blank for Logfire Cloud — the region is read from your token. Set this for a self-hosted deployment.',
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mode: 'advanced',
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},
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{
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id: 'region',
@@ -355,12 +356,39 @@ Return ONLY the IANA timezone string - no explanations or quotes.`,
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type: 'json',
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description:
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'Result rows. Raw SQL returns the query projection; Search Records and Get Trace return records (startTimestamp, endTimestamp, duration, level, message, spanName, kind, serviceName, deploymentEnvironment, traceId, spanId, parentSpanId, isException, exceptionType, exceptionMessage).',
'# Reconstruct Logfire Trace\n\nTurn a trace ID into a readable story of one request.\n\n## Steps\n1. Run Get Trace with the trace ID. Spans come back earliest first.\n2. Build the tree using spanId and parentSpanId to see which operations nested inside which.\n3. Note each span duration to find where time was spent, and flag any span where isException is true.\n4. If the trace is truncated, raise the limit and refetch.\n\n## Output\nAn ordered walkthrough of the request: the entry point, each significant nested operation with its duration, and where it failed or ended. Call out the single slowest span and the first exception.',
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},
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{
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name: 'track-logfire-llm-cost',
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description: 'Aggregate LLM token usage and spend per model from Logfire GenAI spans.',
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content:
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"# Track Logfire LLM Cost\n\nAttribute AI spend to models and services.\n\n## Steps\n1. Run a SQL Query against the records table reading the GenAI attributes Logfire records under OpenTelemetry conventions, for example: SELECT date_trunc('day', start_timestamp) AS day, attributes->>'gen_ai.request.model' AS model, sum((attributes->'gen_ai.usage.input_tokens')::numeric) AS input_tokens, sum((attributes->'gen_ai.usage.output_tokens')::numeric) AS output_tokens FROM records WHERE attributes ? 'gen_ai.request.model' GROUP BY day, model ORDER BY day DESC.\n2. Add sum((attributes->'operation.cost')::numeric) AS cost_usd when the instrumentation records cost.\n3. Slice by service_name or deployment_environment to attribute spend to a team or environment.\n4. Cast JSON attributes to numeric before any arithmetic — they are stored as JSON, not numbers.\n\n## Output\nSpend and token totals broken down by model and day, the biggest contributor, and any model whose cost per call moved materially.",
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},
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{
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name: 'verify-logfire-token-target',
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description: 'Confirm which Logfire organization and project a read token points at.',
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content:
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'# Verify Logfire Token Target\n\nCheck a credential before trusting the data it returns.\n\n## Steps\n1. Run Get Token Info to resolve the organization and project the read token belongs to.\n2. Compare them against the project the user expected to query.\n3. If they differ, stop and report the mismatch rather than querying — the token points at another project.\n4. When they match, proceed with Search Records or a SQL Query.\n\n## Output\nThe organization and project names behind the token, and a clear statement of whether they match the expected target.',
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Copy file name to clipboardExpand all lines: apps/sim/lib/integrations/integrations.json
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{
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"updatedAt": "2026-07-31",
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"updatedAt": "2026-08-02",
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"integrations": [
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{
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"type": "onepassword",
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"name": "Logfire",
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"description": "Query traces, logs, and metrics in Pydantic Logfire",
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"longDescription": "Integrate Pydantic Logfire into workflows. Run SQL over your observability data, search spans and logs with structured filters, pull an entire trace by ID, and confirm which project a read token targets.",
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