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

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

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)