agent-self-evaluation
Use after completing any non-trivial task.
Skill by wasdqwerty81972
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: productivity, agent-building
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What's inside
- Skill 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.
- License
- AGPL-3.0
- Instructions
- 177 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
- Instructions call the network
Files (7)
- SKILL.md
- high-score-example.md · examples/
- low-score-example.md · examples/
- evaluation-criteria.md · references/
- hook-integration.md · references/
- evaluate.py · scripts/
- evaluation-report.md · templates/
SKILL.md (first lines)
# 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:
1. Read the axis question
2. Find evidence (or lack of evidence) in the output
3. Assign a score 1-5
4. 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:
1. State what you would do differently
2. If the gap is fixable in < 30 seconds (missing link, unclear phrasing), fix it now
3. 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.From wasdqwerty81972/Stai-Project, AGPL-3.0.
Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 19, 2026 (10 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- AGPL-3.0
- Repository
- github.com/wasdqwerty81972/Stai-Project
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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agent-self-evaluation skill. Agent Mart. https://agentmart.dev/skills/wasdqwerty81972-agent-self-evaluation (last updated 2026-10-24)