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Bayesian Beta reputation engine — 11-signal model with Thompson Sampling","maintainers":[{"name":"aroha-labs","email":"projectmed99@gmail.com"}],"readme":"# @aroha-sdk/reputation\n\nTrack and query agent reputation scores — surface agents that consistently honor their mandate bounds.\n\n## Install\n\n```bash\nnpm install @aroha-sdk/reputation\n```\n\n## Quick example\n\n```typescript\nimport { ReputationClient } from \"@aroha-sdk/reputation\";\n\nconst rep = new ReputationClient();\n\n// After a successful mandate-bounded interaction, record a positive signal\nawait rep.record({\n  agentDID: \"did:aroha:flight-agent\",\n  event: \"mandate.honored\",\n  mandateConstraints: { spendLimitUsd: 300 },\n  actualSpend: 299,\n});\n\n// Record a violation\nawait rep.record({\n  agentDID: \"did:aroha:rogue-agent\",\n  event: \"mandate.violated\",\n  reason: \"Attempted to charge $400 against a $300 mandate\",\n});\n\n// Query before deciding whether to issue a mandate\nconst score = await rep.score(\"did:aroha:flight-agent\");\n// score.value: 0–100\n// score.mandateViolations: 0\n// score.successfulDelegations: 142\n```\n\n## Why this exists in the mandate chain\n\nCryptographic mandates prevent unauthorized actions, but they don't tell you which agents are reliable over time. Reputation surfaces agents that consistently operate within their granted scope — so orchestrators can make informed decisions before issuing a mandate.\n\n## API\n\n- `ReputationClient(opts?)` — connect to the Aroha reputation service (or a local store for testing).\n  - `rep.record(signal)` — record a reputation signal for a DID.\n  - `rep.score(did)` — get the current score and signal history for an agent.\n  - `rep.leaderboard(filter?)` — list agents ranked by score, optionally filtered by capability.\n\n## License\n\nMIT\n","readmeFilename":"README.md"}