Audit and improve skill collections with an 8-dimension scoring framework, duplication detection, remediation planning, and CI quality gates; use when evaluating skill quality, generating remediation plans, validating report format, or enforcing repository-wide skill artifact conventions.
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Validation for skill structure
Your software engineering team has been developing a collection of AI agent skills over the past six months. The collection has grown to include 15 different skills covering various development practices, from testing strategies to deployment workflows. However, there's been no systematic quality control process in place.
The engineering manager wants to establish a monthly quality audit process that can automatically evaluate all skills in the collection, track improvements over time, and generate actionable reports. The audit system should be able to run both comprehensive audits of the entire collection and targeted audits of recently changed skills during pull request reviews.
The team needs a reliable way to ensure that skills meet quality standards before they're used in production, and they want to be able to compare current quality metrics against previous audit results to track progress over time.
Implement a comprehensive audit workflow that produces:
The audit should cover the entire skills collection and generate organized output files that can be used for tracking quality trends over time.
The following files are provided as inputs. Extract them before beginning.
Write tests for your code...
=============== FILE: inputs/skills/skill-b/SKILL.md ===============
---
name: skill-b
description: Advanced deployment strategies
---
# Deployment Patterns
Deploy applications safely...=============== FILE: inputs/previous-audit.json =============== { "audit_date": "2026-01-15", "total_skills": 2, "average_score": 76, "skills": { "skill-a": {"score": 72, "grade": "C"}, "skill-b": {"score": 80, "grade": "B-"} } }
Install with Tessl CLI
npx tessl i pantheon-ai/skill-quality-auditorevals
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references
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