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tessl-labs/skill-review-optimizer

Automate iterative skill improvement using tessl skill review. Use when optimizing skills, improving skill quality scores, iterating on skill design based on tessl feedback, or systematically enhancing skill descriptions and content. Runs tessl reviews, parses scores and suggestions, identifies missing metadata fields, rewrites descriptions with concrete actions, restructures content sections, adjusts frontmatter fields, and guides incremental refinement until target scores are achieved.

Overall
score

100%

Review — 100%

Does it follow best practices?

Validation — 16 / 16 Passed

Validation for skill structure

Overview
Skills
Evals
Files

SKILL.md

name:
skill-review-optimizer
description:
Automate iterative skill improvement using tessl skill review. Use when optimizing skills, improving skill quality scores, iterating on skill design based on tessl feedback, or systematically enhancing skill descriptions and content. Runs tessl reviews, parses scores and suggestions, identifies missing metadata fields, rewrites descriptions with concrete actions, restructures content sections, adjusts frontmatter fields, and guides incremental refinement until target scores are achieved.
license:
MIT
metadata:
{"version":"1.0.0","category":"skill-development"}

Skill Review Optimizer

Automate the process of iteratively improving skills using tessl skill review feedback until they achieve target quality scores.

Output Format

Displays baseline scores, suggestions, and score progression directly to stdout for immediate review and action.

Workflow

  1. Setup: Verify tessl is installed (auto-install if needed via npm or brew)
  2. Baseline: Run initial tessl skill review to get starting scores
  3. Analyze: Parse review output to extract scores, warnings, and suggestions
  4. Improve: Apply suggested improvements to skill files based on feedback
  5. Validate: Re-run review to verify improvements and measure progress
  6. Iterate: Repeat steps 4-5 until target score reached or max iterations hit
  7. Summarize: Generate final report with all changes and score progression

Prerequisites

Tessl CLI (auto-installs via npm or brew if missing)

Quick Start

Step 1: Run Baseline Review

Run scripts/optimize_skill.py from this skill directory:

python3 scripts/optimize_skill.py /path/to/skill [--max-iterations N]

Target criteria: No validation errors, Description score 100%, Content score ≥ 90%

Step 2: Apply Improvements Based on Suggestions

The script identifies improvement opportunities but does not auto-apply edits. Review suggestions and make targeted changes:

Metadata fields: Add missing frontmatter entries

metadata:
  version: "1.0.0"
  category: "your-category"

Description improvements: Add concrete action verbs and trigger terms

description: Automate X by doing Y. Use when user needs Z. Performs A, B, and C.

Content actionability: Replace vague guidance with executable commands

# Instead of: "Run the build"
# Write: "npm run build"

See STRATEGIES.md for comprehensive optimization patterns.

Step 3: Iterate Until Target Reached

After making improvements, re-run the script to measure progress. Continue the improve → review cycle until target criteria are met.

python3 scripts/optimize_skill.py /path/to/skill

Validation checkpoint: If score decreased or unchanged after 3 iterations, review STRATEGIES.md for alternative approaches. Focus on the first 2-3 suggestions in review output—these typically have highest impact on scores.

Troubleshooting

Scores not improving: Review suggestions in output, focus on highest-impact items first. See STRATEGIES.md for proven optimization patterns.

Understanding scores: See SCORING_GUIDE.md for how tessl evaluates description and content quality.

Validation errors: Fix YAML frontmatter, ensure required fields (name, description) exist.

Install with Tessl CLI

npx tessl i tessl-labs/skill-review-optimizer@0.1.1

SKILL.md

tile.json