self-eval
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. Detects score inflation, forces devil's advocate reasoning, and persists scores across sessions.
Profile →npx skills add alirezarezvani/claude-skills --skill self-eval --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Self-Eval: Honest Work Evaluation ultrathink **Tier:** STANDARD **Category:** Engineering / Quality **Dependencies:** None (prompt-only, no external tools required) ## Description Self-eval is a Claude Code skill that produces honest, calibrated work evaluations. It replaces the default AI tendency to rate everything 4/5 with a structured two-axis scoring system, mandatory devil's advocate reasoning, and cross-session anti-inflation detection. The core insight: AI self-assessment converges to "everything is a 4" because a single-axis score conflates task difficulty with execution quality. Self-eval separates these axes, then combines them via a fixed matrix that the model cannot override. ## Features - **Two-axis scoring** — Independently rates task ambition (Low/Medium/High) and execution quality (Poor/Adequate/Strong), then combines via a lookup matrix - **Mandatory devil's advocate** — Before finalizing, must argue for both higher AND lower scores, then resolve the tension - **Score persistence** — Appends scores to `.self-eval-scores.jsonl` in the working directory, building history across sessions - **Anti-inflation detection** — Reads past scores and flags clustering (4+ of
- Description
- Features
- Usage
- Examples
- Example 1: Feature Implementation
- Example 2: Bug Fix
- What to Evaluate
- How to Score — Two-Axis Model
- Axis 1: Task Ambition (what was attempted)
- Axis 2: Execution Quality (how well it was done)
- Composite Score Matrix
- Devil's Advocate (MANDATORY)
- Anti-Inflation Check
- Score Persistence
What does the self-eval skill do?
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. Detects score inflation, forces devil's advocate reasoning, and persists scores across sessions.
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill self-eval --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.