Self eval
Honest AI work evaluation for Claude Code — two-axis scoring with anti-inflation mechanisms
npx -y skills add ekreloff/claude-self-eval --skill self-evalAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Honestly evaluate AI work quality using a two-axis scoring system. Use after completing a task, code review, or work session to get an unbiased assessment of what was accomplished. Detects score inflation, forces devil's advocate reasoning.
SKILL.md
5.0 KB, as published. Nobody here has run it
Self-Eval: Honest Work Evaluation
ultrathink
What to Evaluate
$ARGUMENTS
If no arguments provided, review the full conversation history to identify what was accomplished this session. Summarize the work in one sentence before scoring.
How to Score — Two-Axis Model
Score on two independent axes, then combine using the matrix. Do NOT pick a number first and rationalize it — rate each axis separately, then read the matrix.
Axis 1: Task Ambition (what was attempted)
Rate the difficulty and risk of what was worked on. NOT how well it was done.
- Low (1) — Safe, familiar, routine. No real risk of failure. Examples: minor config changes, simple refactors, copy-paste with small modifications, tasks you were confident you'd complete before starting.
- Medium (2) — Meaningful work with novelty or challenge. Partial failure was possible. Examples: new feature implementation, integrating an unfamiliar API, architectural changes, debugging a tricky issue.
- High (3) — Ambitious, unfamiliar, or high-stakes. Real risk of complete failure. Examples: building something from scratch in an unfamiliar domain, complex system redesign, performance-critical optimization, shipping to production under pressure.
Self-check: If you were confident of success before starting, ambition is Low or Medium, not High.
Axis 2: Execution Quality (how well it was done)
Rate the quality of the actual output, independent of how ambitious the task was.
- Poor (1) — Major failures, incomplete, wrong output, or abandoned mid-task. The deliverable doesn't meet its own stated criteria.
- Adequate (2) — Completed but with gaps, shortcuts, or missing rigor. Did the thing but left obvious improvements on the table.
- Strong (3) — Well-executed, thorough, quality output. No obvious improvements left undone given the scope.
Composite Score Matrix
| Poor Exec (1) | Adequate Exec (2) | Strong Exec (3) | |
|---|---|---|---|
| Low Ambition (1) | 1 | 2 | 2 |
| Medium Ambition (2) | 2 | 3 | 4 |
| High Ambition (3) | 2 | 4 | 5 |
Read the matrix, don't override it. The composite is your score. The devil's advocate below can cause you to re-rate an axis — but you cannot directly override the matrix result.
Key properties:
- Low ambition caps at 2. Safe work done perfectly is still safe work.
- A 5 requires BOTH high ambition AND strong execution. It should be rare.
- High ambition + poor execution = 2. Bold failure hurts.
- The most common honest score for solid work is 3 (medium ambition, adequate execution).
Devil's Advocate (MANDATORY)
Before writing your final score, you MUST write all three of these:
- Case for LOWER: Why might this work deserve a lower score? What was easy, what was avoided, what was less ambitious than it appears? Would a skeptical reviewer agree with your axis ratings?
- Case for HIGHER: Why might this work deserve a higher score? What was genuinely challenging, surprising, or exceeded the original plan?
- Resolution: If either case reveals you mis-rated an axis, re-rate it and recompute the matrix result. Then state your final score with a 1-2 sentence justification that addresses at least one point from each case.
If your devil's advocate is less than 3 sentences total, you're not engaging with it — try harder.
Anti-Inflation Check
Check for a score history file at .self-eval-scores.jsonl in the current working directory.
If the file exists, read it and check the last 5 scores. If 4+ of the last 5 are the same number, flag it:
Warning: Score clustering detected. Last 5 scores: [list]. Consider whether you're anchoring to a default.
If the file doesn't exist, ask yourself: "Would an outside observer rate this the same way I am?"
Score Persistence
After presenting your evaluation, append one line to .self-eval-scores.jsonl in the current working directory:
{"date":"YYYY-MM-DD","score":N,"ambition":"Low|Medium|High","execution":"Poor|Adequate|Strong","task":"1-sentence summary"}
This enables the anti-inflation check to work across sessions. If the file doesn't exist, create it.
Output
Present your evaluation in this format:
Self-Evaluation
Task: [1-sentence summary of what was attempted] Ambition: [Low/Medium/High] — [1-sentence justification] Execution: [Poor/Adequate/Strong] — [1-sentence justification]
Devil's Advocate:
- Lower: [why it might deserve less]
- Higher: [why it might deserve more]
- Resolution: [final reasoning]
Score: [1-5] — [1-sentence final justification]