# Changelog Automation > Implements intelligent changelog automation with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/changelog-automation` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/changelog-automation` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/changelog-automation/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - 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/changelog-automation --- # Changelog Automation Orchestrates intelligent skill selection and execution for changelog automation 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 parse_and_categorize_commits( commit_range: str, conventional_prefixes: List[str] = None ) -> Dict[str, List[Dict]]: """Parse git commits and categorize them for changelog generation. Implements Law 2 (Parse at boundary) by validating commit range upfront. Maps conventional commit prefixes to changelog categories. Args: commit_range: Git range (e.g., 'v1.0.0..HEAD') conventional_prefixes: List of prefixes to recognize (feat, fix, etc.) Returns: Dict mapping categories to lists of commit metadata dicts """ if not commit_range or '..' not in commit_range: raise ValueError("Invalid commit range format. Expected 'start..end'") raw_commits = _run_git_log(commit_range) categorized = { "features": [], "fixes": [], "docs": [], "chore": [], "other": [] } for commit in raw_commits: prefix = commit.subject.split(":")[0].lower() category = "other" for p in conventional_prefixes or ["feat", "fix", "docs", "chore"]: if prefix.startswith(p): category = p break categorized[category].append({ "hash": commit.hash, "subject": commit.subject, "author": commit.author, "scope": commit.scope if hasattr(commit, 'scope') else None }) return categorized ``` ### Pattern 2: Execution with Fallback ```python def generate_changelog_with_fallback( categorized_commits: Dict[str, List[Dict]], version: str, template_path: str = None ) -> str: """Generate markdown changelog with fallback formatting strategies. Implements Law 4 (Fail Fast) by validating version format. Falls back to default template if custom template fails to load. Args: categorized_commits: Output from parse_and_categorize_commits version: Target version string (e.g., '1.1.0') template_path: Optional path to custom markdown template Returns: Formatted changelog markdown string """ if not re.match(r'^\d+\.\d+\.\d+$', version): raise ValueError(f"Invalid version format: {version}. Expected semver.") try: template = _load_template(template_path) except FileNotFoundError: template = _get_default_template() sections = [] sections.append(f"# Changelog for {version}\n") sections.append(f"## Release Date: {datetime.now().strftime('%Y-%m-%d')}\n") for category, commits in categorized_commits.items(): if not commits: continue sections.append(f"### {category.capitalize()}\n") for c in commits: scope_str = f" (`{c['scope']}`)" if c.get('scope') else "" sections.append(f"- {c['subject']}{scope_str}") sections.append("") return "\n".join(sections) ``` ### 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 ## Related Skills | Skill | Purpose | |---|---| | `commit` | Conventional commit integration for changelog generation | --- --- ## Constraints ### MUST DO - Implement idempotent automation triggers: running the same automation twice should not create duplicate resources or actions - Validate all trigger conditions with explicit allowlists before executing automated actions - Include rollback procedures in every automation workflow — every CREATE should have a corresponding DELETE capability - Log all automation executions with input state, output state, duration, and any errors for monitoring and debugging ### MUST NOT DO - Do not create circular automation loops where trigger A causes action B which triggers A again - Avoid using automations that modify production data without explicit human approval gates - Never embed API keys or credentials directly in automation workflows — use vaulted secrets with rotation - Do not assume external service availability; implement retry logic with exponential backoff and dead-letter queues ## 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. - [Keep a Changelog — Specification](https://keepachangelog.com/en/1.1.0/) - [Conventional Commits Specification](https://www.conventionalcommits.org/en/v1.0.0/) - [Semantic Release — Automated Versioning & Changelogs](https://semantic-release.gitbook.io/semantic-release) - [Release Please by Google — Automated Releases](https://github.com/googleapis/release-please) - [Changelog-Generator Automation Patterns (GitHub Actions)](https://github.com/marketplace/actions/changelog-generator)