improvement-workflow

from adaptationio/skrillz

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1 stars0 forksUpdated Jan 16, 2026
npx skills add https://github.com/adaptationio/skrillz --skill improvement-workflow

SKILL.md

Improvement Workflow

Overview

improvement-workflow orchestrates the complete continuous improvement cycle for Claude Code skills, transforming review findings into applied improvements and validated enhancements.

Purpose: End-to-end skill improvement from review through validated enhancement

Component Skills (5):

  1. review-multi - Comprehensive multi-dimensional review (identify issues)
  2. analysis - Pattern analysis across findings (understand systemic issues)
  3. best-practices-learner - Extract learnings (capture insights)
  4. skill-updater - Apply improvements (implement changes)
  5. skill-validator - Validate improvements (ensure quality maintained)

Workflow Pattern: Sequential pipeline with feedback loop

Result: Systematically improved skills with validated enhancements and captured learnings

When to Use

  • Continuous improvement iterations (make good skills better)
  • Applying review recommendations (systematic implementation)
  • Post-deployment enhancement (v1.0 → v1.1)
  • Multiple skill improvements (consistent process)
  • Learning-driven development (capture and apply insights)

Improvement Workflow

Step 1: Comprehensive Review (review-multi)

Purpose: Identify improvement opportunities through multi-dimensional assessment

Process: Run review-multi comprehensive mode (all 5 operations)

Outputs:

  • Overall score and grade
  • Per-dimension scores
  • Prioritized improvement recommendations
  • Identified issues and anti-patterns

Time: 1.5-2.5 hours


Step 2: Pattern Analysis (analysis)

Purpose: Understand systemic patterns in findings

Process: Use analysis Operation 5 (Pattern Recognition)

  • Review findings from Step 1
  • Identify recurring themes (if reviewing multiple skills)
  • Understand root causes
  • Prioritize by impact

Outputs:

  • Pattern analysis (systemic issues vs one-offs)
  • Root cause understanding
  • Impact-prioritized improvements

Time: 45-90 minutes


Step 3: Extract Learnings (best-practices-learner)

Purpose: Capture insights for future application

Process: Use best-practices-learner Operations 1-2

  • Extract patterns from review findings
  • Document what worked/didn't work
  • Capture insights for guidelines

Outputs:

  • Documented patterns
  • Learnings log
  • Insights for guideline updates

Time: 30-60 minutes


Step 4: Apply Improvements (skill-updater)

Purpose: Systematically implement improvements

Process: Use skill-updater workflow

  1. Plan updates (prioritize recommendations)
  2. Backup skill
  3. Apply changes (one at a time)
  4. Test each change

Outputs:

  • Updated skill with improvements applied
  • Change documentation
  • Version update

Time: 1-4 hours (varies by number of improvements)


Step 5: Validate Improvements (skill-validator + review-multi)

Purpose: Ensure improvements effective, no regressions

Process:

  1. Run skill-validator (ensure still passes minimum standards)
  2. Re-run review-multi (compare before/after scores)
  3. Validate improvements achieved goals
  4. Document impact

Outputs:

  • Validation results (pass/fail)
  • Before/after score comparison
  • Impact measurement
  • Regression check

Time: 30-60 minutes


Step 6: Update Guidelines (best-practices-learner)

Purpose: Feed learnings back into ecosystem

Process: Use best-practices-learner Operation 3

  • Update common-patterns.md with new patterns
  • Update templates if needed
  • Propagate learnings to future skills

Outputs:

  • Updated guidelines
  • Improved templates
  • Enhanced ecosystem knowledge

Time: 20-40 minutes


Post-Workflow: Iteration Decision

After completing workflow:

If Score Improved Significantly (≥0.5 points):

  • ✅ Improvements effective
  • Document success
  • Apply similar improvements to other skills

If Score Improved Slightly (<0.5 points):

  • ⚠️ Minor impact
  • Assess if effort worth benefit
  • Consider different improvements

If Score Unchanged or Decreased:

  • ❌ Improvements ineffective or caused regressions
  • Review what went wrong
  • Revert changes if regression
  • Try different approach

Iterate:

  • Can run workflow again for further improvements
  • Diminishing returns after 2-3 iterations
  • Focus on highest-impact improvements first

Best Practices

1. Review Before Improve

Practice: Always review comprehensively before changing

Rationale: Understand current state fully prevents fixing wrong things

2. Prioritize by Impact

Practice: Apply high-impact improvements first

Rationale: Maximum benefit for effort invested

3. One Improvement at a Time

Practice: Apply and validate each change individually

Rationale: Prevents compounding errors, identifies what actually helped

4. Measure Impact

Practice: Compare before/after scores objectively

Rationale: Data-driven understanding of effectiveness

5. Capture Learnings

Practice: Document what

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