agent-self-evaluation

name agent-self-evaluation description Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions. origin ECC Agent Self-Evaluation After completing a complex task, the agent pauses to rate its own output against a structured 5-axis rubric. This is NOT a pass/fail gate — it's a deliberate reflection step that catches omissions, flags overconfidence, and surface areas for improvement before the user has to. When to Activate After writing code that spans 3+ files or 50+ lines After completing a multi-step workflow (implement → test → review) After a debugging session that involved 3+ attempts After producing a design document, architecture decision, or written analysis When the user asks "how good was that?" or "rate yourself" At the end of any session Stop hook (if configured — see references/hook-integration.md ) Core Concepts The 5 Evaluation Axes Axis Question What it catches Accuracy Are the facts, claims, and outputs correct? Hallucinations, wrong API names, incorrect syntax, false statements Completeness Did it cover everything the user asked for? Missed edge cases, unhandled error paths, forgotten requirements, skipped subtasks Clarity Is the explanation understandable and well-structured? Confusing explanations, jargon without definition, missing context, rambling Actionability Can the user act on the output immediately? Vague suggestions, missing steps, "you should X" without showing how, no verification path Conciseness Did it use the minimum words/tokens needed? Redundancy, over-explanation, repeating the user's question verbatim, filler content Scoring Scale 5 — Exceptional: no reasonable improvement possible 4 — Good: minor nits only, no substantive gaps 3 — Adequate: meets the request but has a notable weakness on at least one axis 2 — Weak: has a clear gap that affects usability or correctness 1 — Poor: fundamentally misses the request or contains significant errors The Evidence Rule Every score below 5 MUST cite specific evidence. A score of 3 cannot just say "could be better" — it must say exactly what is missing or wrong. The mantra: "Show the gap, don't just name it." Workflow Step 1: Collect the Raw Material Gather what you'll evaluate: - The original user request (read back from conversation) - Your final response/output (the deliverable) - Any tool outputs that verify correctness (test results, exit codes, lint output) - Any user feedback received during the task (corrections, "try again", "that's not right") Step 2: Score Each Axis Independently Work through the 5 axes one at a time. For each: Read the axis question Find evidence (or lack of evidence) in the output Assign a score 1-5 If score < 5, write a one-sentence improvement note citing the gap Do NOT average the scores in your head first and then work backwards. Score each axis fresh. Step 3: Produce the Evaluation Report Use the template from templates/evaluation-report.md . The report must include: - One-line summary - 5-axis scorecard (score + evidence per axis) - Overall score (simple average, rounded to 1 decimal) - 1-3 specific improvements ranked by impact - Self-check: "Would the user agree with this assessment?" Step 4: Apply the Improvement If any axis scored 3 or below: State what you would do differently If the gap is fixable in < 30 seconds (missing link, unclear phrasing), fix it now If the gap requires rework, flag it explicitly: "This axis scored [reason] because [evidence]. Re-running with [specific fix] would likely raise it to [score]." Code Examples Example: Good Evaluation (Score 4+) Task: Add retry logic to HTTP client Scorecard: Accuracy: 5 — All API calls correct. Verified: retries use exponential backoff. No hallucinated methods. Completeness: 4 — Covered happy path + 3 error cases. Missing: timeout handling for hung connections. Clarity: 5 — Code comments explain backoff formula. PR description links to incident that motivated this. Actionability:5 — Single merge. No follow-up tasks. Tests pass. Conciseness: 4 — 47 lines total. The retry loop could be extracted into a helper to drop ~8 lines. Overall: 4.6 — One gap (timeout handling). Fix before merging. Example: Weak Evaluation (Score 2-3) Task: Add retry logic to HTTP client Scorecard: Accuracy: 2 — Used urllib3 which doesn't match our httpx-based codebase. Wrong library. Completeness: 3 — Works for GET. POST/PUT not handled (user said "all HTTP requests"). Clarity: 4 — Code is readable. Good variable names. Actionability:2 — "Add tests" mentioned but no test file created. User has to write tests before merging. Conciseness: 3 — 120 lines. The retry config is duplicated in 3 places instead of one shared RetryConfig object. Overall: 2.8 — Wrong library used. Needs httpx rewrite. Fix accuracy first (switch to httpx), then extend to all HTTP methods, then consolidate config. Anti-Patterns "Everything is a 5" FAIL: Accuracy: 5 — All good. Completeness: 5 — Everything covered. Clarity: 5 — Clear. No evidence cited. This is self-congratulation, not evaluation. A real 5 requires proving there's nothing to improve. Over-penalizing for scope creep FAIL: Completeness: 2 — Didn't handle WebSocket connections or gRPC streaming (user didn't ask for these) Only evaluate against what the user actually requested, not what you could have additionally built. Using the evaluation to re-litigate FAIL: "As I said earlier, this approach is wrong. Score: 1" The evaluation is about the delivered output, not about re-arguing design decisions that were already made. If the approach was wrong, that should have been caught before delivery. Mixing personal preference with objective gaps FAIL: "Score: 3. I don't like Python decorators." "Don't like" is not evidence. Cite a concrete readability, testability, or correctness concern, or leave the score at 4+. Best Practices Evaluate the output, not the process. The user cares about what you delivered, not how many iterations you took. One improvement per weak axis. Don't list 5 things for one axis — pick the highest-impact gap. Tie improvements to user impact. "Missing error handling means the user's API call will crash silently" beats "add error handling." Be specific about what 'fixed' looks like. "Re-run with httpx transport configured for retries" beats "fix the library issue." Use tool outputs as evidence. If tests passed, cite them. If lint is clean, cite it. Don't guess — grep for the proof. If you can't find any gaps, try harder. A perfect score across all 5 axes is rare. Ask: "If I were the user, what would annoy me about this output?" Related Skills agent-eval — Head-to-head comparison of different coding agents on benchmark tasks verification-loop — Systematic verification of outputs against expected results security-review — Security-focused code review checklist