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Scope: catch the ten taxonomy families reliably on real-world and generated application code, including forgotten authorization checks; not browser engines, interpreters or memory-safety fuzzing. Decisions: verifier-aligned families, a measured per-family noise floor as the regression budget, a cheap model on an independent chat endpoint with embeddings elsewhere, canary-oracle verifiers for the four most dangerous families first, the obligations lever folded into the access-control family's first round, web families before the C slice. Eleven requirements in EARS form; awaiting approval. Co-Authored-By: Claude Fable 5.1 <[email protected]> Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
30 lines
646 B
JSON
30 lines
646 B
JSON
{
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"feature_name": "learning-harness",
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"created_at": "2026-09-06T06:32:41Z",
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"updated_at": "2026-09-06T06:34:14Z",
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"language": "en",
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"phase": "requirements-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": false,
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"approved": false
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},
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"tasks": {
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"generated": false,
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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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"depends_on": [
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"pair-corpus-honesty",
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"corpus-seeded-mechanisms",
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"authorization-obligations",
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"tree-sitter-overlay-extra",
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"harnessx-self-improving-rulesets"
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]
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}
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