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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
- MIT
- Instructions
- 178 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 JunMystery/Agent-Guidance, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Aug 18, 2026 (2 months ago)
- Last release
- Oct 23, 2026 (7 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/JunMystery/Agent-Guidance
- 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/junmystery-agent-self-evaluation (last updated 2026-10-24)