Nm Abstract Skills Eval

开发编程

通过审计评估 Claude 技能质量

代码审查

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★ 5.0

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Night Market Skill — ported from claude-night-market/abstract. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Skills Evaluation and Improvement#

Table of Contents#

Overview
Quick Start
Evaluation Workflow
Evaluation and Optimization
Resources

Overview#

This framework audits Claude skills against quality standards to improve performance and reduce token consumption. Automated tools analyze skill structure, measure context usage, and identify specific technical improvements. Run verification commands after each audit to confirm fixes work correctly.

The skills-auditor provides structural analysis, while the improvement-suggester ranks fixes by impact. Compliance is verified through the compliance-checker. Runtime efficiency is monitored by tool-performance-analyzer and token-usage-tracker.

Quick Start#

Basic Audit#

Run a full audit of all skills or target a specific file to identify structural issues.

bash

# Audit all skills
make audit-all

# Audit specific skill
make audit-skill TARGET=path/to/skill/SKILL.md

Analysis and Optimization#

Use skill_analyzer.py for complexity checks and token_estimator.py to verify the context budget.

bash

make analyze-skill TARGET=path/to/skill/SKILL.md
make estimate-tokens TARGET=path/to/skill/SKILL.md

Improvements#

Generate a prioritized plan and verify standards compliance using improvement_suggester.py and compliance_checker.py.

bash

make improve-skill TARGET=path/to/skill/SKILL.md
make check-compliance TARGET=path/to/skill/SKILL.md

Evaluation Workflow#

Start with make audit-all to inventory skills and identify high-priority targets. For each skill requiring attention, run analysis with analyze-skill to map complexity. Generate an improvement plan, apply fixes, and run check-compliance to verify the skill meets project standards. Finalize by checking the token budget for efficiency.

Evaluation and Optimization#

Quality assessments use the skills-auditor and improvement-suggester to generate detailed reports. Performance analysis focuses on token efficiency through the token-usage-tracker and tool performance via tool-performance-analyzer. For standards compliance, the compliance-checker automates common fixes for structural issues.

Scoring and Prioritization#

We evaluate skills across five dimensions: structure compliance, content quality, token efficiency, activation reliability, and tool integration. Scores above 90 represent production-ready skills, while scores below 50 indicate critical issues requiring immediate attention.

Improvements are prioritized by impact. Critical issues include security vulnerabilities or broken functionality. High-priority items cover structural flaws that hinder discoverability. Medium and low priorities focus on best practices and minor optimizations.

Structural Patterns#

Deprecated: skills/shared/modules/ directories. Shared modules must be relocated into the consuming skill's own modules/ directory. The evaluator flags any remaining skills/shared/ as a structural warning.

Current: Each skill owns its modules at skills/<skill-name>/modules/. Cross-skill references use relative paths (e.g., ../skill-authoring/modules/anti-rationalization.md).