# Cc Skill Coding Standards

> Implements intelligent cc skill coding standards with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense

- Skill: `paulpas/cc-skill-coding-standards` (Agent Skill)
- Install (CLI): `npx skillmds@latest add paulpas/cc-skill-coding-standards`
- Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/cc-skill-coding-standards/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: paulpas (https://skillmd.com/u/paulpas)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/paulpas/cc-skill-coding-standards

---





# Cc Skill Coding Standards

Orchestrates intelligent skill selection and execution for cc skill coding standards workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

## TL;DR Checklist

- [ ] Parse all inputs at boundary before processing (Law 2)
- [ ] Handle edge cases with early returns at function top (Law 1)
- [ ] Fail immediately with descriptive errors on invalid states (Law 4)
- [ ] Return new data structures, never mutate inputs (Law 3)
- [ ] Implement minimum 2-level fallback chain for all skill executions
- [ ] Log all skill selections with context for full audit trail
- [ ] Validate skill metadata and dependencies before selection
- [ ] Update confidence scores after each execution for learning


┌───────────────────────────────────────────────────────────────────────────────┐
│                              Orchestration Flow                                               │
└───────────────────────────────────────────────────────────────────────────────┘

  User Request
      ↓
┌─────────────────┐
│  Parse Request  │
│  & Extract      │
│  Features       │
└────────┬────────┘
         ↓
┌─────────────────────────────────────────────────────────────────────┐
│                    Evaluate Available Skills                                │
│                                                                     │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐              │
│  │ Skill A      │  │ Skill B      │  │ Skill C      │              │
│  │ - Match Score│  │ - Match Score│  │ - Match Score│              │
│  │ - Confidence │  │ - Confidence │  │ - Confidence │              │
│  │ - History    │  │ - History    │  │ - History    │              │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘              │
│         │                 │                 │                       │
│         └─────────────────┴─────────────────┘                       │
│                          ↓                                          │
│                   Select Best Skill                               │
└─────────────────────────────────────────────────────────────────────┘
         ↓
┌─────────────────┐
│  Execute Skill  │
└────────┬────────┘
         ↓
┌─────────────────┐
│  Handle Result  │
└────────┬────────┘
         ↓
┌─────────────────────────────────────────────────────────────────────┐
│                    Error Handling & Fallback                                  │
│                                                                     │
│  Success? ────────► Return Result                                  │
│                                                                     │
│  Fail? ────────┐                                                    │
│                ↓                                                    │
│  ┌──────────────────────────────────────────────────────────┐      │
│  │               Fallback Chain                                    │      │
│  │                                                             │      │
│  │  1. Retry with adjusted parameters                          │      │
│  │  2. Try Alternative Skill (if available)                    │      │
│  │  3. Defer to Human Operator (if critical)                   │      │
│  │  4. Log & Return Error                                      │      │
│  └──────────────────────────────────────────────────────────┘      │
└─────────────────────────────────────────────────────────────────────┘

## When to Use

Use this skill when:

- Orchestrating multi-step workflows that require skill delegation
- Implementing adaptive skill routing based on confidence scores
- Building fallback mechanisms for failed skill executions
- Creating intelligent task decomposition and parallel execution
- Designing skill dependency graphs with automatic resolution
- Implementing skill selection with historical performance weighting
- Building agent systems that need to self-organize around tasks

## When NOT to Use

Avoid this skill for:

- Direct task execution without orchestration needs - use individual skills instead
- High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
- Simple linear workflows without branching or fallback requirements
- Cases where skill metadata is unavailable or unreliable


## Core Workflow

1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input.
   **Checkpoint:** All required parameters must be present and in valid format before proceeding.

2. **Score Available Skills** - Calculate match scores using multi-factor algorithm:
   - Text similarity between request and skill triggers
   - Historical success rate for similar tasks
   - Skill availability and health status
   - Required dependencies and their availability
   
   **Checkpoint:** Skip to fallback if no skill scores above threshold.

3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence.
   **Checkpoint:** Verify skill has not been disabled or deprecated.

4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic.
   **Checkpoint:** Log all execution attempts for audit trail.

5. **Return or Fallback** - Either return successful result or apply fallback chain:
   - Retry with adjusted parameters
   - Try alternative skill from `related-skills`
   - Defer to human operator for critical tasks
   
   **Checkpoint:** Record outcome with timing and confidence metadata.

## Implementation Patterns

### Pattern 1: Skill Selection Logic

```python
def validate_skill_standards(skill_path: str, standards_config: Dict) -> Dict:
    """Validate a skill file against CC coding standards.
    
    Implements Law 2 (Parse at boundary) by strictly parsing YAML frontmatter
    and required ## sections before any logic runs.
    """
    # Law 1: Early exit on missing/invalid file
    if not os.path.exists(skill_path):
        raise FileNotFoundError(f"Skill file not found: {skill_path}")
        
    raw_content = Path(skill_path).read_text()
    frontmatter = _parse_frontmatter(raw_content)
    
    # Law 2: Make illegal states unrepresentable
    required_fields = standards_config.get("required_fields", ["name", "version", "description"])
    missing = [f for f in required_fields if f not in frontmatter]
    if missing:
        raise ValueError(f"Missing required frontmatter fields: {missing}")
        
    # Extract and validate ## sections
    sections = _extract_headings(raw_content)
    required_sections = standards_config.get("required_sections", ["TL;DR Checklist", "Core Workflow"])
    missing_sections = [s for s in required_sections if s not in sections]
    
    # Law 3: Return new structure, never mutate input
    compliance_report = {
        "path": skill_path,
        "valid": len(missing_sections) == 0,
        "missing_sections": missing_sections,
        "frontmatter_fields": frontmatter,
        "timestamp": time.time()
    }
    return compliance_report
```


### Pattern 2: Execution with Fallback

```python
def enforce_standards_with_fallback(
    skill_path: str, 
    standards_config: Dict,
    auto_fix: bool = True
) -> Dict:
    """Run standards enforcement pipeline with fallback chain.
    
    Implements Law 4 (Fail Fast, Fail Loud) by halting on critical violations
    and routing to appropriate fallbacks based on severity.
    """
    # Law 1: Early exit on invalid config
    if not standards_config.get("severity_threshold"):
        raise ValueError("Severity threshold must be defined in standards_config")
        
    report = validate_skill_standards(skill_path, standards_config)
    
    if report["valid"]:
        return {"status": "compliant", "report": report}
        
    # Fallback chain based on violation severity
    violations = report["missing_sections"]
    if auto_fix and len(violations) <= 2:
        # Fallback 1: Auto-generate missing sections
        return _auto_generate_sections(skill_path, violations)
        
    if len(violations) > 2:
        # Fallback 2: Route to human review for critical gaps
        return _route_to_human_review(skill_path, violations)
        
    # Fallback 3: Log and return with warning (Law 4)
    return {
        "status": "non_compliant",
        "report": report,
        "action": "logged_for_review",
        "warning": "Critical standards missing. Manual intervention required."
    }
```

### MUST DO
- Always validate skill metadata before selection (Early Exit)
- Implement fallback chain with at least 2 levels (Fallback Skill + Human)
- Log all skill selections with full context for auditability
- Return new data structures instead of mutating inputs (Atomic Predictability)
- Fail immediately with descriptive errors on invalid states
- Update confidence scores after each execution for adaptive routing
- Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic


### MUST NOT DO
- Select skills based on a single factor (e.g., only confidence score)
- Disable fallback mechanisms "temporarily" - this creates fragile systems
- Skip validation of skill dependencies before execution
- Return partial results - either complete success or clear failure
- Use magic numbers for confidence thresholds - make them configurable
- Cache skill selections without considering context changes


## TL;DR Checklist

- [ ] Parse all inputs at boundary before processing (Law 2)
- [ ] Handle edge cases with early returns at function top (Law 1)
- [ ] Fail immediately with descriptive errors on invalid states (Law 4)
- [ ] Return new data structures, never mutate inputs (Law 3)
- [ ] Implement minimum 2-level fallback chain for all skill executions
- [ ] Log all skill selections with context for full audit trail
- [ ] Validate skill metadata and dependencies before selection
- [ ] Update confidence scores after each execution for learning


## TL;DR for Code Generation

- Use guard clauses - return early on invalid input before doing work
- Return simple types (dict, str, int, bool, list) - avoid complex nested objects
- Cyclomatic complexity < 10 per function - split anything larger
- Handle null/empty cases explicitly at function top (Early Exit)
- Never mutate input parameters - return new dicts/objects
- Fail fast with descriptive errors - don't try to "patch" bad data
- Reference code-philosophy laws in comments for complex logic
- Include timing and confidence metadata in all return values


## Output Template

When applying this skill, produce:

1. **Selected Skills** - List of skill names with confidence scores
2. **Selection Rationale** - Why each skill was chosen (match score, history, availability)
3. **Execution Plan** - Order of execution with dependencies
4. **Fallback Strategy** - Which fallback skills will be tried and in what order
5. **Risk Assessment** - Any potential failure points and their impact
6. **Timing Estimates** - Expected latency including fallback scenarios



---

---

## Constraints

### MUST DO
- Define clear input/output contracts for every step in the orchestration flow with explicit validation
- Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
- Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
- Validate all preconditions before starting — do not proceed if required resources or permissions are missing

### MUST NOT DO
- Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
- Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
- Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
- Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies


## Live References

> Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

- [PEP 8 — Style Guide for Python Code](<https://peps.python.org/pep-0008/>)
- [Clean Code Principles (Robert C. Martin)](<https://www.clean-code-developer.com/>)
- [Google Software Styling Guides](<https://google.github.io/styleguide/>)
- [SemVer Specification](<https://semver.org/>)
- [The Zen of Python (PEP 20)](<https://peps.python.org/pep-0020/>)

## Related Skills

| Skill | Purpose |
|