A learning package layered families -> classify -> scoring -> split ->
detectors and verifiers -> rounds -> publish, composing the existing seams:
the tool hunter gains prompt, context and tool parameters; the hunter path of
the pair scorer takes an injected family matcher so pairs never imports
learning; every learning path selects teachers through one train-only rule;
the regress verdict takes an optional canary oracle; the chat endpoint resolves
DeepSeek before OpenRouter with thinking disabled and reasoning replayed, while
embeddings keep their own endpoint; HarnessX proposes behind the seam with a
scripted proposer for offline runs. Fifty-nine acceptance criteria traced;
boundary sections, file structure plan and migration phases populated.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
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
