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Spec-driven plan to (1) adopt HarnessX (Darwin-Agent/HarnessX) directly where it pays off — the LLM hunter loop, the LLM-judge verifier, and a meta-harness self-improvement loop — and migrate scan flows onto it stage-by-stage; (2) make detection rules versioned, centrally-governed data whose severity comes from a vendored verycode CWE policy (CWE_Score.tsv) instead of the rule itself, fixing "rules too influential / not centrally managed"; (3) adapt rules/policy inside HarnessX's MetaAgent.evolve loop under bounded levers with replay/novelty/ validator gates; (4) make benchmarks actionable per-rule signals with a hard accept gate and byte-for-byte revert; (5) compute a 0-100 verycode-style project score with a two-condition CI gate. Key decisions captured in research.md: HarnessX is an LLM-agent harness (not a generic stage pipeline), so it is adopted only on the agentic plane behind an optional SHA-pinned extra while the governance/scoring/benchmark planes stay zero-dependency; the >=90% recall / <10% FP detection gate is a hard constraint separate from the score reward and must hold at every phase; CWE-120 is unmapped in verycode so C/C++ buffer rules remap to CWE-121 (severity 5). Artifacts: spec.json (phase tasks-generated, approvals pending), requirements.md (7 requirements, EARS), design.md (three-plane architecture), tasks.md (4 phases), research.md (D1-D9 decisions). Awaiting human approval per the kiro 3-phase gate; not yet implemented. Co-Authored-By: Claude Opus 4.8 <[email protected]>
23 lines
466 B
JSON
23 lines
466 B
JSON
{
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"feature_name": "harnessx-self-improving-rulesets",
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"created_at": "2026-06-22T10:07:18Z",
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"updated_at": "2026-06-22T10:07:18Z",
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"language": "en",
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"phase": "tasks-generated",
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"approvals": {
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"requirements": {
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"generated": true,
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"approved": false
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},
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"design": {
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"generated": true,
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"approved": false
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},
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"tasks": {
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"generated": true,
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"approved": false
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}
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},
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"ready_for_implementation": false
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}
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