Oleks 6b634fd9f8 audit-snapshot: fix wrong decline reason for a stability-downgraded candidate
decline_reason() only ever read a candidate's own grade, so a candidate
boundary-classify graded high/complete-triple -- meaning it cleared FR-4.2
fine -- fell through to the generic 'did not clear the filing threshold'
line when stability-classify downgraded it afterward. That's the opposite of
what happened, and it directly contradicted the correct FR-4.5 section
rendered a few lines below it in the same judgment-calls drawer, poisoning
the next audit's step-2b recall with a false reason.

Found live: real audit of anxious (2026-07-30) downgraded
agent-wip/release-policy-derivation at 11.85% of audited spend -- the one
gate it DID clear -- and the drawer said it didn't. Filed and now fixed as
kotkan/claude-plugin-inference-arbitrage#16.

Fix keeps FR-4.2's own check order first (a candidate can coincidentally be
named in an unrelated stability finding without having actually cleared
those checks) and only replaces the final fallback, since a candidate that
reaches it cleared every earlier check on its own grade -- it cannot have
also failed the filing threshold on its own merits.

New regression case (h) in tests/memory.test.sh. Suite: all tests pass.
2026-07-30 04:35:42 +03:00

inference-arbitrage

Audits a Claude Code plugin — its skill/agent definitions and its real usage transcripts — to find steps that are being done by raw LLM inference but pass every test of a deterministic script, and files the well-evidenced ones as issues on the target plugin's own repo. Runs on demand; each run accumulates into a snapshot history so cost and candidate status can be tracked over time.

The name echoes builder-arbitrage: route each unit of work to the cheapest executor that can do it correctly — here, "script vs. model" instead of "which build node."

Status: scaffolding, pre-implementation. See design/ for the full specification:

Tracked as issues on this repo under the v0.1.0 milestone.

S
Description
Claude Code plugin — optimize Claude model selection by routing inference requests to the best model per query, balancing cost, latency, and accuracy.
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