# Finishing A Development Branch > Implements intelligent finishing a development branch with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/finishing-a-development-branch` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/finishing-a-development-branch` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/finishing-a-development-branch/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/finishing-a-development-branch --- # Finishing A Development Branch Orchestrates intelligent skill selection and execution for finishing a development branch 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 select_branch_finishing_strategy( branch_metadata: Dict, project_config: Dict, available_tools: List[Dict] ) -> Dict: """Select the optimal finishing strategy based on branch type and project rules. Evaluates branch characteristics (feature, hotfix, release) against project-specific merge policies, required checks, and team conventions. """ if not branch_metadata.get("name"): raise ValueError("Branch name is required for strategy selection") branch_type = branch_metadata.get("type", "feature") target_ref = project_config.get("default_target", "main") strategy = { "type": branch_type, "target": target_ref, "required_checks": [], "merge_method": "squash", "fallback_actions": [] } # Apply project-specific merge policies policies = project_config.get("merge_policies", {}) if branch_type in policies: policy = policies[branch_type] strategy["required_checks"] = policy.get("checks", []) strategy["merge_method"] = policy.get("method", "merge") # Select appropriate toolchain from available tools for tool in available_tools: if tool.get("supports_type") == branch_type: strategy["toolchain"] = tool["name"] break # Validate strategy against branch constraints if branch_metadata.get("is_hotfix") and strategy["merge_method"] == "squash": strategy["merge_method"] = "fast-forward" strategy["fallback_actions"].append("notify_team_lead") return strategy ``` ### Pattern 2: Execution with Fallback ```python def execute_branch_finishing_pipeline( strategy: Dict, branch_state: Dict, ci_client: object, notifier: object ) -> Dict: """Execute the complete branch finishing workflow with domain-specific fallbacks. Pipeline: Validate -> Run Checks -> Merge -> Cleanup -> Notify Implements graceful degradation when non-critical checks fail. """ branch_name = branch_state["name"] target = strategy["target"] required_checks = strategy.get("required_checks", []) # Phase 1: Pre-flight Validation if not _validate_branch_up_to_date(branch_state, target): raise BranchStaleError(f"{branch_name} is behind {target}. Rebase required.") # Phase 2: Execute Required Checks check_results = [] for check in required_checks: try: result = ci_client.run_check(check, branch_name) check_results.append(result) if not result["passed"]: strategy["fallback_actions"].append(f"skip_{check}") except CheckTimeoutError: strategy["fallback_actions"].append(f"retry_{check}") # Phase 3: Merge Execution with Fallback merge_result = None for attempt in range(2): try: merge_result = _perform_merge(branch_name, target, strategy["merge_method"]) break except MergeConflictError as e: if attempt == 0: strategy["fallback_actions"].append("auto_resolve_conflicts") continue raise MergeFailedError(f"Merge failed after conflict resolution: {e}") # Phase 4: Cleanup & Notification if merge_result and merge_result["success"]: _cleanup_remote_branch(branch_name) notifier.send_update(target, merge_result["commit_hash"]) return {"status": "finished", "merge": merge_result} # Fallback execution for action in strategy["fallback_actions"]: _execute_fallback_action(action, branch_name, target) return {"status": "completed_with_fallbacks", "actions_taken": strategy["fallback_actions"]} ``` ### 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 | | --- --- ## Constraints ### MUST DO - Validate branch naming conventions and PR scope before creating pull requests — enforce repository-level policies - Require all CI checks to pass before merging; never allow bypass of required status checks without codeowner approval - Implement automated changelog generation from commit messages using conventional commits format - Maintain linear history via rebase on main branch; avoid merge commits except for release branches ### MUST NOT DO - Do not force-push to shared or protected branches — only the original author may force-push their own feature branch - Avoid squashing all commits during PR review when historical commit context is valuable for understanding evolution - Never skip required code reviews regardless of how small the change appears — automation cannot assess architectural impact - Do not create PRs larger than 400 lines of net changes without explicit approval from a senior reviewer ## 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. - [GitHub Flow Documentation]() - [Git branching model (Trunk-Based Development)]() - [GitLab Gitflow Workflow]() - [Bitbucket Branching Model]() - [Software Configuration Management (IEEE 828)]()