self-eval

Evaluate completed work with two-axis scoring and devil's advocate reasoning.

Updated Apr 9, 2026
One-click install
npx skills add https://github.com/Patasse97/claude-skills --skill self-eval-patasse97
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-eval
Source: https://github.com/Patasse97/claude-skills/tree/main/engineering/self-eval
Command: npx skills add https://github.com/Patasse97/claude-skills --skill self-eval-patasse97

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Self-eval provides honest, calibrated work evaluations after completing tasks or reviews by applying a two-axis scoring system and mandatory devil's advocate reasoning, addressing the common bias of inflated self-assessments.

Core Features & Use Cases

  • Two-axis scoring: independently rates task ambition and execution quality, then combines via a fixed matrix.
  • Mandatory devil's advocate: requires arguing for both higher and lower scores before finalizing.
  • Score persistence: appends evaluation results to .self-eval-scores.jsonl, building history across sessions.
  • Anti-inflation detection: analyzes past scores to flag clustering and stabilize assessments over time.
  • Matrix-based composite scoring: final score derived from a predefined matrix to ensure consistent judgments.

Quick Start

After completing work in a Claude Code session, run /self-eval with context about what was evaluated to generate the assessment.

Frequently Asked Questions about self-eval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get honest calibrated work evaluations for code reviews?▼

Calibrated work evaluations apply a two-axis scoring system and mandatory devil's advocate reasoning to independently rate task ambition and execution quality, preventing inflated self-assessments.

What is devil's advocate reasoning for AI quality assurance?▼

Devil's advocate reasoning for AI quality assurance requires arguing for both higher and lower scores before finalizing an assessment, ensuring honest reviews and mitigating self-evaluation bias.

How do I prevent score inflation across multiple coding sessions?▼

Prevent score inflation by persisting evaluation results to a dedicated JSONL history file, which analyzes past scores to detect clustering and stabilize assessments over time.

Does self-eval require external dependencies or tools to run?▼

Self-eval requires no external tools or dependencies to run, operating entirely within the session to apply matrix-based composite scoring and write results to local history files.

What is the best way to score task ambition and execution quality independently?▼

The best way to score task ambition and execution quality independently is using a two-axis scoring system, where a predefined matrix derives the final composite score for consistent judgments.

When should I not use automated self-evaluations for tasks?▼

Avoid automated self-evaluations when you lack session history for anti-inflation detection, as the tool relies on reading past score files to flag clustering and calibrate current assessments.