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3 — error/incident correlation engine. The moat: connect git + code + errors + time.","maintainers":[{"name":"mneme_npm","email":"patsa2561@gmail.com"}],"readme":"# @mneme-ai/correlator\n\nPhase 3 — error/incident correlation engine for [Mneme](https://github.com/patsa2561-art/mneme-ai).\n\nThe differentiator. Joins commits + incidents + time into one graph, so you can ask:\n\n> *\"Every time PaymentService.charge is touched, a Stripe webhook 500 spikes within 48h.\"*\n>\n> *\"This PR touches code that has caused 3 of the last 5 incidents in OrderQueue.\"*\n>\n> *\"Incident INC-1287 was likely introduced by commit a1b2c3d (87% confidence — same file, 14h before the spike).\"*\n\n## What's in here\n\n- `temporal.ts` — `TemporalCorrelationEngine` (temporal proximity + file overlap)\n- `adapters/sentry.ts` — first pager adapter (REST API; live)\n- `adapters/datadog.ts` — Datadog adapter (stub, in progress)\n- `adapters/manual.ts` — JSON file input (works today, useful for testing)\n\n## Usage\n\n```ts\nimport { TemporalCorrelationEngine, SentryAdapter } from \"@mneme-ai/correlator\";\n\nconst sentry = new SentryAdapter({\n  orgSlug: \"my-org\",\n  projectSlug: \"web\",\n  apiToken: process.env.SENTRY_TOKEN!,\n});\n\nconst incidents = await sentry.fetch({ since: \"2025-01-01T00:00:00Z\" });\nconst engine = new TemporalCorrelationEngine();\nconst correlations = await engine.correlate({\n  commits,\n  incidents,\n  windowMs: 7 * 24 * 60 * 60 * 1000,\n});\n```\n\n## License\n\nMIT.\n","readmeFilename":"README.md"}