autoresearchlisted

# Autoresearch — Skill Self-Optimization The system improves its own skills through iterative loops. ## Process ### 1. Baseline Run /eval on the target skill. Record trigger accuracy and fidelity. ### 2. Diagnosis For each failure: - Trigger miss → description too narrow - False trigger → description too broad - Fidelity fail → instructions in body incorrect ### 3. Mutation ONE change per iteration. Never more than one variable at a time. - For trigger: modify the description - For fidelity: modify the body - For stale eval: update the tests ### 4. Re-eval Run /eval again. Compare with baseline: - Improved? Keep the mutation. - Same or worse? Revert, try a different mutation. ### 5. Report Baseline → final score, what changed, how many iterations. ## Rules - One variable at a time - The eval is the judge, not your opinion - Max 5 iterations — if it does not converge, flag for human review - Never change the purpose of the skill — only refine trigger/fidelity ## Eval ## Trigger Tests # "optimize my deploy skill" -> activates # "skill X is failing tests" -> activates # "improve skill accuracy" -> activates # "run eval" -> does NOT activate (that is /eval) # "deploy" -> does NOT activate ## Fidelity Tests # Given skill with 2 trigger failures: mutates description, re-tests, reports improvement # Given skill with 0 failures: reports "already optimal, no mutations needed" # Given skill that does not converge in 5 iterations: flags for human review