Tessl Registry Compliance Framework

Supplementary validation checks for skills intended for Tessl registry submission. These checks extend the core 8-dimension framework with agent-agnostic and performance-focused evaluations.

Use after: Core 8-dimension evaluation (≥108 points required) Target: 100% tessl compliance for registry acceptance

Overview

Tessl focuses on performance-evaluated, agent-agnostic skills that provide measurable effectiveness improvements. This framework adds 3 supplementary validation areas:

1. Agent-Agnostic Validation - Ensure cross-platform compatibility
2. Performance Metrics Integration - Quantify effectiveness improvements
3. Cross-Platform Compatibility - Validate tool/feature support

Agent-Agnostic Validation (Pass/Fail)

Purpose: Ensure skills work across different AI assistant platforms without agent-specific dependencies.

Validation Checks

❌ NEVER: Agent-Specific Tool Dependencies

Check allowed-tools frontmatter for agent-specific tools:

# Bad - Claude Code specific
allowed-tools: [claude-artifact, claude-codebase]

# Good - Universal tools
allowed-tools: [bash, edit, read, write]

Auto-check pattern: Flag tools containing agent names (claude-, cursor-, openai-, etc.)

❌ NEVER: Platform-Specific Instructions

Scan content for agent-specific references:

❌ BAD: "Tell Claude to run the command"
❌ BAD: "Use Cursor's autocomplete feature"  
❌ BAD: "In OpenAI's interface, click..."

✅ GOOD: "Run the command using your bash tool"
✅ GOOD: "Use your code completion capabilities"
✅ GOOD: "Execute the following workflow"

Auto-check pattern: /\b(claude|cursor|openai|copilot|gemini|chatgpt)\b/i

❌ NEVER: Hardcoded Agent Behaviors

Avoid assuming specific agent capabilities:

❌ BAD: "Since you can't execute code directly..."
❌ BAD: "Use your web browsing to..."
❌ BAD: "Your image generation will..."

✅ GOOD: "If code execution tools are available..."
✅ GOOD: "When web access is supported..."
✅ GOOD: "For agents with image capabilities..."

Scoring

• PASS: No agent-specific dependencies detected
• FAIL: Any agent-specific references found

Performance Metrics Integration (Pass/Fail)

Purpose: Ensure skills define measurable effectiveness improvements that can be evaluated.

Required Components

1. Success Metrics Definition

Skills must include quantifiable outcomes:

## Success Metrics

This skill provides:
- ✅ 85% reduction in configuration errors
- ✅ 3x faster setup time (5 minutes vs 15 minutes)
- ✅ 100% compliance with security standards

2. Before/After Scenarios

Show clear improvement examples:

## Effectiveness Examples

### Before Using This Skill
- Manual setup takes 30+ commands
- 40% failure rate on first attempt
- Inconsistent configuration across environments

### After Using This Skill  
- One-command deployment
- <5% failure rate
- Standardized, reproducible environments

3. Measurable Outcomes

Define what "effective use" looks like:

## Expected Outcomes

When applied correctly, this skill delivers:
- Time savings: 60-90% reduction in task duration
- Quality improvement: 95%+ adherence to best practices
- Error reduction: <10% incident rate vs 30% baseline

Validation Checks

• Required sections: "Success Metrics", "Expected Outcomes", or equivalent
• Quantified claims: Must include specific numbers (percentages, time, error rates)
• Comparative data: Before/after or baseline comparisons

Scoring

• PASS: Contains measurable effectiveness claims with quantification
• FAIL: No performance metrics or only qualitative claims

Cross-Platform Compatibility (Pass/Fail)

Purpose: Validate that skill instructions work across different development environments and agent platforms.

Tool Compatibility Checks

Universal Tools Only

Verify all referenced tools are widely supported:

✅ GOOD: bash, read, write, edit, glob, grep
✅ GOOD: Standard CLI tools (git, npm, docker)
✅ GOOD: Common development commands

❌ BAD: Agent-specific tools
❌ BAD: Proprietary extensions
❌ BAD: Platform-locked features

Command Portability

Ensure shell commands work across operating systems:

# Bad - macOS specific
brew install package

# Good - Cross-platform with options
# Install using your package manager:
# - macOS: brew install package  
# - Ubuntu: apt install package
# - Windows: choco install package

Path References

Use portable path conventions:

❌ BAD: /usr/local/bin/tool (Unix-specific)
❌ BAD: C:\Program Files\tool (Windows-specific)

✅ GOOD: Add tool to your PATH
✅ GOOD: $(which tool) or equivalent

Agent Feature Assumptions

Avoid assuming specific agent capabilities:

❌ BAD: "Use your built-in web scraping"
❌ BAD: "Generate an image with DALL-E"
❌ BAD: "Create a diagram with your drawing tools"

✅ GOOD: "If web scraping tools are available..."
✅ GOOD: "Using image generation capabilities..."
✅ GOOD: "With diagram creation tools..."

Validation Process

1. Tool Audit: Check all tool references for universality
2. Command Review: Verify cross-platform shell commands
3. Feature Check: Flag agent-specific capability assumptions
4. Documentation Review: Ensure instructions work for any agent

Scoring

• PASS: No platform-specific dependencies detected
• FAIL: Contains non-portable tools or commands

Implementation Guide

For Skill Authors

When preparing skills for Tessl submission:

1. Run core evaluation first: Achieve A-grade (≥108 points)
2. Apply tessl validations: Use checks in this framework
3. Fix compatibility issues: Remove agent-specific dependencies
4. Add performance metrics: Quantify effectiveness improvements
5. Verify portability: Test commands across platforms

For Auditors

Integration with existing skill-quality-auditor workflow:

# Standard evaluation first
sh skills/skill-quality-auditor/scripts/evaluate.sh <skill-name> --json

# Then apply tessl compliance checks
sh skills/skill-quality-auditor/scripts/tessl-compliance-check.sh <skill-name>

Automated Validation

Agent-Agnostic Check:

# Check for agent-specific terms
grep -ri "claude\|cursor\|openai\|copilot\|gemini" skills/<skill>/

Tool Compatibility Check:

# Extract and validate allowed-tools
yq '.allowed-tools[]?' skills/<skill>/SKILL.md | grep -E "(claude|cursor|openai)-"

Performance Metrics Check:

# Look for quantified outcomes
grep -E "[0-9]+(%|x|times|\s(seconds|minutes|hours)|reduction|improvement)" skills/<skill>/

Integration with Core Framework

This framework supplements, not replaces, the 8-dimension evaluation:

See Also

• framework-skill-judge-dimensions.md - Core 8-dimension framework
• framework-quality-standards.md - A-grade requirements
• Tessl Registry - Performance-evaluated skills