Agent Self-Evaluation

by affaan-m

Agent Self-Evaluation is a Quality skill for Claude Code, published by affaan-m in ECC.

239.8K stars36.4K forkson affaan-m/ECCAdded 2026/08/13Repository updated 2026/08/12

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ai-agentsanthropicclaudeclaude-codedeveloper-toolsllmmcpproductivity

Install in seconds

Install Agent Self-Evaluation

Copy Agent Self-Evaluation into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/affaan-m/ECC/tree/main/skills/agent-self-evaluation ~/.claude/skills/agent-self-evaluation

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/affaan-m/ECC.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/agent-self-evaluation/SKILL.md in affaan-m/ECC
Installs to: ~/.claude/skills/agent-self-evaluation
Collection: One of 25 skills cataloged from this repository
Category: Quality — 1354 skills

What Agent Self-Evaluation does

Agent Self-Evaluation rates an agent's own output on five axes—accuracy, completeness, clarity, actionability, and conciseness—with evidence per criterion. Use it after completing any non-trivial task to catch omissions and surface improvements before the user has to.

Agent Self-Evaluation is cataloged under Quality on DirSkills. Agent Self-Evaluation comes from a repository tagged ai-agents, anthropic, claude, claude-code and developer-tools.

Documentation

README

Excerpt

Agent Self-Evaluation

After completing a complex task, the agent pauses to rate its own output against a structured 5-axis rubric. This is NOT a pass/fail gate — it's a deliberate reflection step that catches omissions, flags overconfidence, and surface areas for improvement before the user has to.

When to Activate

After writing code that spans 3+ files or 50+ lines
After completing a multi-step workflow (implement → test → review)
After a debugging session that involved 3+ attempts
After producing a design document, architecture decision, or written analysis
When the user asks "how good was that?" or "rate yourself"
At the end of any session Stop hook (if configured — see references/hook-integration.md)

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Agent Self-Evaluation

What else does affaan-m publish alongside Agent Self-Evaluation?
Agent Self-Evaluation is one of 25 skills that DirSkills catalogs from affaan-m/ECC, the repository it ships in. Its siblings there include AI Regression Testing, AI-First Engineering and API Connector Builder. Each one is a separate skill with its own page in this directory, installs the same way Agent Self-Evaluation does, and is maintained by affaan-m in that same repository. The rest of the collection is listed on the affaan-m/ECC page.
How does Agent Self-Evaluation compare to other Quality skills?
Agent Self-Evaluation ranks #4 by stars among the 1354 Quality skills in this catalog. The most-starred ones next to it are Benchmark, Benchmark Optimization Loop and API Design Patterns. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Agent Self-Evaluation against them. Open each page to compare what they document and how they install.