llm-gold-bound-failure-check

llm-gold-bound-failure-check is a Memory stores & recall skill for AI agents, published by kennethkhoocy in the applied-micro-skills repository on GitHub. Skillspace rates llm-gold-bound-failure-check D (66/100) and it passed the safety scan; the rating is based on static analysis only.

Diagnose whether an LLM classifier's validation-gate failure is GOLD-BOUND before spending on prompt revision or model changes. Use when: (1) a scoring pipeline over-predicts a label (precision low, recall high) and a prompt clarification is proposed to tighten it, (2) a pilot/validation gate fails and the fix candidates are prompt edits, (3) inter-rater agreement on the weak label was already low (κ < ~0.6). Core check: if gold POSITIVES share the exact feature the revision would exclude, no prompt can pass a gold-scored gate — recall craters while precision barely moves. Also documents the verified surgical-pilot design (single-section diff, tune/holdout split, pre-registered gate, perturbation check on untouched sections).

Grade

D

Quality

66 / 100

Safety

Passed

GitHub stars

28

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