---

name: iterate-pr compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent iterate pr with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: iterate-pr, iterate pr, how do i iterate-pr, orchestrate iterate-pr, automate iterate-pr, agent iterate-pr archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Iterate Pr

Orchestrates intelligent skill selection and execution for iterate pr 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_skill(
    task_description: str,
    available_skills: List[Dict],
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Select the most appropriate PR iteration skill for a given task.
    
    Uses a multi-factor scoring algorithm that considers:
    - PR state and CI status alignment with skill triggers
    - Historical success rate for similar PR iteration tasks
    - Current system load and skill availability
    
    Args:
        task_description: Natural language description of the PR iteration task
        available_skills: List of PR iteration skill metadata dictionaries
        min_confidence: Minimum confidence threshold (0.0-1.0)
        
    Returns:
        Selected skill dictionary or None if no match meets threshold
        
    Raises:
        ValueError: If task_description is empty or available_skills is empty
    """
    # Guard clause - Early Exit (Law 1)
    if not task_description or not task_description.strip():
        raise ValueError("Task description cannot be empty")
        
    if not available_skills:
        raise ValueError("No PR iteration skills available for selection")
    
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    pr_context = _extract_pr_context(task_description)
    
    best_skill = None
    best_score = 0.0
    
    for skill in available_skills:
        score = _calculate_pr_iteration_score(pr_context, skill)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_skill = skill
    
    if best_skill is None:
        return None
    
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    result = dict(best_skill)
    result["selected_confidence"] = best_score
    result["selection_timestamp"] = time.time()
    return result

Pattern 2: Execution with Fallback

def execute_with_fallback(
    skill: Dict,
    task_context: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute a PR iteration skill with fallback chain for resilience.
    
    Implements the Fail Fast, Fail Loud principle (Law 4):
    - Invalid PR states halt immediately with descriptive errors
    - No silent failures or partial commit results
    
    Fallback chain:
    1. Retry with adjusted linting/formatting parameters
    2. Try alternative PR skill (e.g., switch from auto-fix to manual prompt)
    3. Defer to human operator (for critical architectural changes)
    
    Args:
        skill: Selected PR iteration skill metadata
        task_context: Execution context including PR URL and diff data
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Execution result with metadata (success, commit hash, confidence)
        
    Raises:
        SkillExecutionError: If all retries and fallbacks exhausted
    """
    # Guard clause - validate skill (Early Exit)
    if not _is_pr_skill_valid(skill):
        raise SkillExecutionError(f"Invalid PR iteration skill: {skill.get('name', 'unknown')}")
    
    # Parse context - Ensure trusted state (Law 2)
    validated_context = _validate_and_parse_pr_context(task_context, skill)
    
    for attempt in range(max_retries + 1):
        try:
            result = _execute_pr_skill_direct(skill, validated_context)
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "skill_executed": skill["name"],
                "commit_hash": result.get("commit_hash"),
                "attempts": attempt + 1,
                "latency_ms": _calculate_latency()
            }
            
        except PRMergeError as e:
            # Fail Fast - Don't try to patch merged PRs (Law 4)
            raise SkillExecutionError(
                f"PR is merged or closed: {str(e)}"
            ) from e
            
        except CICheckFailure as e:
            # Transient CI error - try fallback
            if attempt == max_retries:
                return _apply_pr_fallback_chain(skill, validated_context)
    
    # All retries exhausted - Fail Loud (Law 4)
    raise SkillExecutionError(
        f"Failed to execute PR iteration {skill['name']} after {max_retries + 1} attempts"
    )

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 | |

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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.

• Iterative Development Methodology (Wikipedia)
• Continuous Integration Best Practices
• Pull Request Review Cycles (Google Engineering)
• Agile Retrospectives for Process Improvement
• Feedback Loop Design in Software Development