eval-harness-skill

What I do

Structured evaluation framework: define measurable criteria → run checks → score 0-10 per criterion → report pass/fail vs thresholds → suggest improvements for failures. Unlike verification-loop-skill (gate execution), this skill owns scoring rubrics and thresholds.

Trigger phrases: "evaluate this code", "run eval", "score this implementation", "quality check", "benchmark against criteria".

Default Criteria (0-10 each, thresholds customizable per run)

Criterion	Threshold	Key questions
Correctness	7	Does it do what it should? Edge cases? Error paths?
Code Quality	7	Conventions, readability, abstractions without over-engineering
Test Coverage	7	Tests present? Edge cases covered? Maintainable?
Performance	6	Efficient algorithms, no wasted computation, caching where applicable
Security	7	Input validation, no injection, proper authn/authz

Score ranges: 9-10 excellent · 7-8 good · 5-6 adequate · 3-4 below standard · 1-2 poor · 0 failed. Overall = sum + percentage; verdict from thresholds.

Scope Variants

Single file · module/directory · skill output (skill:<name>) · PR diff (pr:<num>) · acceptance criteria (issue:<num>).

Workflow

1. Define criteria for the target (use defaults, add domain-specific ones). Criteria must be measurable ("functions under 20 lines", not "clean code") and consistent across comparable runs.
2. Execute: static analysis (lint, types, complexity) → pattern/anti-pattern checks → test run + coverage → score each criterion → gap analysis.
3. Report: per-criterion table (score / threshold / status) + overall + per-failure findings with concrete fix suggestions (priority, effort, expected impact).
4. Gate semantics: evaluations can gate PRs — a failed critical criterion blocks merge. Judge trends across runs, not absolute scores; note exceptions when a criterion doesn't apply.

Criteria starters: code quality — naming per language convention, function ≤20 lines, cyclomatic <10, no silent catches, DRY. Skill effectiveness — task completion, output production-readiness, consistency, graceful failure, integration. Acceptance criteria — each criterion checked with evidence, no partial completions, no regressions.

Integration

• verification-loop-skill — canonical gate contract; eval supplies scoring rubrics, not gate execution
• continuous-learning-skill — learn which patterns score well
• civiltekk-context-optimization-skill — compact preserves eval results
• linting-workflow-skill / code-smells-skill / solid-principles-skill — signals feeding the quality criterion

References

• verification-loop-skill — gates and verification during implementation
• continuous-learning-skill — learning from evaluation results
• civiltekk-context-optimization-skill — preserving eval context

Iteration Protocol (opt-in)

DO NOT execute any of the following unless AUTORESEARCH_PROTOCOL=1 is set in your environment. When unset, this skill behaves exactly as documented in all sections above; the Iteration Protocol block is descriptive only.

When AUTORESEARCH_PROTOCOL=1:

1. Gate check: confirm env var is set; if unset, follow default behavior above.
2. Auto-detection: if this skill is invoked on a task that looks iterative (multiple cycles expected), prompt ONCE per session: "This looks iterative. Enable autoresearch protocol? (y/n)". On "y", continue; on "n", default behavior. Cache the answer for the session.
3. 5-stage loop: cycle Understand → Hypothesize → Experiment → Evaluate → Log & Iterate. See autoresearch-core-skill/SKILL.md.
4. Evaluator contract: emit {"pass":bool,"score":N} JSON from a mechanical evaluator. Pass determines keep/revert; score logged to eval-harness-results.tsv. See autoresearch-core-skill/references/evaluator-contract.md.
5. Stuck detection: 3 consecutive non-improving iterations → strategy pivot; 5 consecutive → paradigm shift. See autoresearch-core-skill/references/stuck-detection.md.
6. Audit trail: append every iteration to eval-harness-results.tsv (8-column: iteration, commit, metric, delta, status, description, timestamp, evaluator_output). See autoresearch-core-skill/references/audit-trail.md.
7. Crash recovery: syntax errors → fix immediately (don't count); runtime → max 3 fix attempts then skip; timeout → revert + log; OOM → smaller variant. See autoresearch-core-skill/references/crash-recovery.md.
8. Git-as-memory: commit before each verify; auto-revert (git reset --hard HEAD~1) on pass:false.
9. Iteration safety: bounded-by-default (Iterations: 25); safety blocks .env, node_modules/, rm -rf, git push --force. See autoresearch-core-skill/references/iteration-safety.md.

Skill-specific override

TSV audit trail. Append every eval run to eval-harness-results.tsv. Overnight persistence via autoresearch-core-skill/scripts/autoresearch-loop.sh (detects available CLI tool). Score is the eval metric; pass = meets target threshold.

Max iterations

• Default: 25 iterations
• Hard cap: 100 (explicit Iterations: unlimited overrides)

Removal note (2026-09-19, #409 trim per LEARNINGS #383 recipe): dropped the worked criteria/report markdown examples, the evaluation-templates section (compressed to criteria starters), the Example Usage section, and Best Practices prose (compressed into the workflow). Kept verbatim: frontmatter, criteria/threshold/score tables, workflow contract, Iteration Protocol section.