learn-from-correctionlisted

<!-- prompt-only-enforcement-skip: this is an interpretive skill spec (skill-hook-pairing classifies the learn-from-correction family as "no hook"); its one deterministic slice is backed by correction_outcome.py + test_correction_outcome.py, not by prose. --> # Learn From Correction You take a correction (what an agent proposed vs. what the human actually did) and propose a principle edit to the skill/persona file that should have caught it. The proposal goes to `q-system/output/skill-proposals/` for the founder to review and merge through normal git flow. This skill exists because the best prompt today is not the best prompt a month from now. Corrections the founder is already making (rewrites of agent drafts, anti-pattern additions to Skeptic, copy edits) carry the signal needed to keep skills sharp - but only if something captures that signal as a durable principle. **Before writing anything, read `references/principle-vs-rule.md`.** The guardrails there are load-bearing: principles transfer, rules overfit. A correction turned into a rule produces a brittle decision tree. The same correction turned into a principle reshapes how the agent reasons. ## Constraints (ENFORCED) - **Never edit the target skill file directly.** Output is always a proposal markdown in `q-system/output/skill-proposals/`. The founder reviews, edits, and merges via normal git flow so Codex review fires on the diff (same gate as any other code change). - **One correction at a time.** If the found