micro-skill-creator

name	micro-skill-creator
description	Rapidly creates atomic, focused skills optimized with evidence-based prompting, specialist agents, and systematic testing. Each micro-skill does one thing exceptionally well using self-consistency, program-of-thought, and plan-and-solve patterns. Enhanced with agent-creator principles and functionality-audit validation. Perfect for building composable workflow components.
tags	["skill-creation","atomic","modular","evidence-based","specialist-agents","tier-1"]
version	2.0.0

Micro-Skill Creator (Enhanced)

Overview

Creates small, focused skills that each spawn a specialist agent optimized for a specific task using evidence-based prompting techniques. This enhanced version integrates agent-creator principles, prompt-architect patterns, and systematic testing from functionality-audit.

Philosophy: Atomic Excellence

Unix Philosophy for AI: Do one thing and do it well, with clean interfaces for composition.

Evidence-Based Agents: Every micro-skill spawns a specialist agent using research-validated techniques:

• Self-consistency for factual tasks
• Program-of-thought for analytical tasks
• Plan-and-solve for complex tasks
• Neural training integration for continuous improvement

Key Principles:

1. Single responsibility per skill
2. Specialist agent per domain
3. Clean input/output contracts
4. Systematic validation
5. Composability first

When to Create Micro-Skills

✅ Perfect For:

• Tasks you perform repeatedly
• Operations needing specialist expertise
• Building blocks for cascades
• Capabilities for slash commands
• Domain-specific workflows

❌ Don't Use For:

• One-off exploratory tasks
• Tasks too simple for specialization
• Better handled by external tools

Enhanced Creation Workflow

Step 1: Define Single Responsibility

State in ONE sentence what this skill does:

• "Extract structured data from unstructured documents"
• "Validate API responses against OpenAPI schemas"
• "Refactor code to use dependency injection patterns"

Trigger Pattern: Define keywords for Claude Code discovery.

Step 2: Design Specialist Agent (Enhanced)

Using agent-creator + prompt-architect principles:

A. Identity & Expertise

I am a [domain] specialist with expertise in:
- [Core competency 1]
- [Core competency 2]
- [Edge case handling]
- [Output quality standards]

B. Evidence-Based Methodology

For Factual Tasks (Self-Consistency):

Methodology:
1. Extract information from multiple perspectives
2. Cross-reference findings for consistency
3. Flag any inconsistencies or ambiguities
4. Provide confidence scores
5. Return validated results

For Analytical Tasks (Program-of-Thought):

Methodology:
1. Decompose problem into logical components
2. Work through each component systematically
3. Show intermediate reasoning
4. Validate logical consistency
5. Synthesize final analysis

For Complex Tasks (Plan-and-Solve):

Methodology:
1. Create comprehensive plan with dependencies
2. Break into executable steps
3. Execute plan systematically
4. Validate completion at each step
5. Return complete solution

C. Output Specification

Precise format enables reliable composition:

output:
  format: json | markdown | code
  structure:
    required_fields: [...]
    optional_fields: [...]
  validation_rules: [...]
  quality_standards: [...]

D. Failure Mode Awareness

Common Failure Modes & Mitigations:
- [Failure type 1]: [How to detect and handle]
- [Failure type 2]: [How to detect and handle]

Step 3: Create Skill Structure

SKILL.md Template:

---
name: skill-name
description: [Specific trigger description]
tags: [domain, task-type, evidence-technique]
version: 1.0.0
---

# Skill Name

## Purpose
[Clear, single-sentence purpose]

## Specialist Agent
[Agent system prompt using evidence-based patterns]

## Input Contract
[Explicit input requirements]

## Output Contract
[Explicit output format and validation]

## Integration Points
- Cascades: [How it composes]
- Commands: [Slash command bindings]
- Other Skills: [Dependencies or companions]

Step 4: Add Validation & Testing

Systematic Testing (from functionality-audit):

Test Cases:
1. Normal operation with typical inputs
2. Boundary conditions
3. Error cases with invalid inputs
4. Edge cases
5. Performance stress tests

Validation Checklist:

• Skill triggers correctly
• Agent executes with domain expertise
• Output matches specifications
• Errors handled gracefully
• Composes with other skills
• Performance acceptable

Step 5: Neural Training Integration

Enable Learning (from ruv-swarm):

training:
  pattern: [cognitive pattern type]
  feedback_collection: true
  improvement_iteration: true
  success_tracking: true

Micro-Skill Templates (Enhanced)

1. Data Extraction Micro-Skill

Agent System Prompt:

I am an extraction specialist using self-consistency checking for accuracy.

Methodology (Self-Consistency Pattern):
1. Scan source from multiple angles
2. Extract candidate information
3. Cross-validate findings
4. Flag confidence levels and ambiguities
5. Return structured data with metadata

Failure Modes:
- Ambiguous source: Flag for human review
- Missing information: Explicitly note gaps
- Low confidence: Provide alternative interpretations

Input/Output:

input:
  source_document: string | file_path
  target_schema: json_schema
  confidence_threshold: number (default: 0.8)

output:
  extracted_data: object (matches target_schema)
  confidence_scores: object (per field)
  ambiguities: array[string]
  metadata:
    extraction_quality: high | medium | low
    processing_time: number

2. Validation Micro-Skill

Agent System Prompt:

I am a validation specialist using program-of-thought decomposition.

Methodology (Program-of-Thought Pattern):
1. Parse input systematically
2. Load specification/rules
3. Check each rule with clear reasoning
4. Show validation logic step-by-step
5. Categorize violations by severity

Failure Modes:
- Ambiguous rules: Request clarification
- Conflicting rules: Flag inconsistencies
- Edge cases: Apply conservative interpretation

Input/Output:

input:
  data: object | array
  specification: schema | rules_file
  strictness: lenient | normal | strict

output:
  validation_result:
    status: pass | fail | warning
    violations: array[{rule, location, severity, message}]
    summary: {errors: number, warnings: number}
  suggested_fixes: array[{location, fix, confidence}]

3. Generation Micro-Skill

Agent System Prompt:

I am a generation specialist using plan-and-solve framework.

Methodology (Plan-and-Solve Pattern):
1. Parse specification and understand requirements
2. Create comprehensive generation plan
3. Execute plan systematically
4. Validate output against requirements
5. Review for completeness and correctness

Failure Modes:
- Incomplete specification: Request missing details
- Ambiguous requirements: Provide multiple options
- Validation failures: Iterate with fixes

Input/Output:

input:
  specification: object | markdown
  templates: array[template] (optional)
  config: object (generation parameters)

output:
  generated_artifact: string | object
  generation_metadata:
    decisions_made: array[{decision, rationale}]
    completeness_check: pass | partial | fail
    warnings: array[string]

4. Analysis Micro-Skill

Agent System Prompt:

I am an analysis specialist combining program-of-thought and self-consistency.

Methodology:
1. Gather data systematically
2. Apply analytical framework (program-of-thought)
3. Identify patterns and anomalies
4. Validate conclusions (self-consistency)
5. Prioritize findings by importance

Failure Modes:
- Insufficient data: Flag and request more
- Conflicting indicators: Present both interpretations
- Uncertain conclusions: Provide confidence levels

Input/Output:

input:
  data: object | array | file_path
  analysis_type: quality | security | performance | etc
  depth: shallow | normal | deep

output:
  analysis_report:
    key_findings: array[{finding, evidence, severity}]
    recommendations: array[{action, priority, rationale}]
    confidence_levels: object (per finding)
    supporting_data: object

Integration with Cascade Workflows

Composition Patterns:

# Sequential
extract-data → validate-data → transform-data → generate-report

# Parallel
input → [validate-schema + security-scan + quality-check] → merge-results

# Conditional
validate → (if pass: deploy) OR (if fail: generate-error-report)

# Map-Reduce
collection → map(analyze-item) → reduce(aggregate-results)

# Iterative
refactor → check-quality → (repeat if below threshold)

Integration with Slash Commands

Command Binding Example:

command:
  name: /validate-api
  binding:
    type: micro-skill
    target: validate-api-response
    parameter_mapping:
      file: ${file_path}
      schema: ${schema_path}
      strict: ${--strict flag}

Best Practices (Enhanced)

Skill Design

1. ✅ Truly atomic - one responsibility
2. ✅ Evidence-based agent methodology
3. ✅ Explicit input/output contracts
4. ✅ Comprehensive error handling
5. ✅ Systematic validation testing
6. ✅ Neural training enabled

Agent Optimization

1. ✅ Use appropriate evidence technique
2. ✅ Include failure mode awareness
3. ✅ Specify exact output formats
4. ✅ Add self-validation steps
5. ✅ Enable continuous learning

Composition

1. ✅ Clean interfaces for chaining
2. ✅ Standardized error formats
3. ✅ Idempotent when possible
4. ✅ Version interfaces carefully
5. ✅ Document dependencies

Working with Micro-Skill Creator

Invocation: "Create a micro-skill that [single responsibility] using [evidence technique] with [domain expertise]"

The creator will:

1. Guide you through agent design with evidence-based patterns
2. Generate skill structure with proper contracts
3. Create validation test cases
4. Set up neural training integration
5. Produce production-ready micro-skill

Integration:

• Works with agent-creator for agent design
• Works with cascade-orchestrator for workflow composition
• Works with slash-command-encoder for /command access
• Works with functionality-audit for validation
• Works with ruv-swarm MCP for neural training

---

Version 2.0 Enhancements:

• Evidence-based prompting patterns
• Systematic validation testing
• Neural training integration
• Enhanced agent design methodology
• Improved composition interfaces