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.