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AaronVick

Agent Fables

by AaronVick

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    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to scout, rank, and preflight software work before implementation, returning evidence-backed ACT, VERIFY, or SKIP decisions for issues and pull requests.
      60 npm
      2
      MIT
    • A
      license
      B
      quality
      B
      maintenance
      Enables AI clients to analyze software test results and defect records, producing explainable GO, CONDITIONAL_GO, or NO_GO release-readiness recommendations with deterministic risk scoring, failed-test retrieval, defect hotspot ranking, and regression test planning.
      4
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables ingesting caller-supplied agent execution traces and comparing them against signed, versioned baselines of normal behaviour to surface silent drift. Reports severity with per-dimension attribution, a replayable exemplar episode, and visible suppression of declared maintenance windows or known change markers.
      1
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables a local AI agent to investigate service incidents by querying read-only telemetry such as service health, metrics, logs, and traces, then produce verifiable assessments with evidence links and uncertainty.
      Apache 2.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Provides tools for agents to manage a local review graph, tracking acceptance behaviors, evidence, review passes, and human waivers to decouple review convergence from shipping readiness.
      3
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Provides structured, sandboxed test and lint feedback for coding agents, returning compact typed verdicts with failure fingerprints instead of raw runner output. It enables impact-selected test execution in isolated containers, distinguishing pre-existing failures from regressions.
      4
      Apache 2.0