self-eval

原文涵盖的主题

• Self-Eval: Honest Work Evaluation
• Description
• Features
• Usage
• Examples
• Example 1: Feature Implementation
• Example 2: Bug Fix
• What to Evaluate

ultrathink Tier: STANDARD Category: Engineering / Quality Dependencies: None (prompt-only, no external tools required) 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. 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 last 5 identical) Matrix-locked scoring — The composite score comes from the matrix, not from direct

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