paulpas Git Pr Workflows Onboard
Orchestrates intelligent skill selection and execution for git pr workflows onboard 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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 analyze_pr_context_and_route(
pr_metadata: Dict[str, Any],
repo_config: Dict[str, Any],
available_onboarding_skills: List[Dict],
min_confidence: float = 0.75
) -> Optional[Dict]:
"""Route PR onboarding tasks to optimal sub-skill based on workflow context.
Applies Law 1 (Early Exit) and Law 2 (Parse at Boundary) to validate PR state
before scoring against available onboarding capabilities.
"""
if not pr_metadata.get("branch_name") or not pr_metadata.get("base_branch"):
raise ValueError("PR must have branch_name and base_branch defined")
if not repo_config.get("branch_protection_rules"):
raise ValueError("Repository must have branch protection rules configured")
# Parse PR type and extract workflow features at boundary
title_lower = pr_metadata.get("title", "").lower()
labels = pr_metadata.get("labels", [])
is_hotfix = any("hotfix" in lbl for lbl in labels) or "hotfix" in title_lower
requires_review = repo_config.get("required_reviewers", 0) > 0
ci_required = repo_config.get("required_status_checks", False)
workflow_features = {
"is_hotfix": is_hotfix,
"requires_review": requires_review,
"ci_required": ci_required,
"auto_merge_eligible": not is_hotfix and not requires_review and ci_required
}
best_skill = None
best_score = 0.0
for skill in available_onboarding_skills:
# Multi-factor scoring: workflow match + repo compliance + historical success
workflow_match = 1.0 if skill["trigger_patterns"].get("pr_type") == (is_hotfix and "hotfix" or "standard") else 0.5
compliance_score = 1.0 if all(skill["requirements"].get(k) <= v for k, v in workflow_features.items()) else 0.0
historical_weight = skill.get("success_rate", 0.5)
composite_score = (workflow_match * 0.4) + (compliance_score * 0.3) + (historical_weight * 0.3)
if composite_score > best_score and composite_score >= min_confidence:
best_score = composite_score
best_skill = skill
if best_skill is None:
return None
# Law 3: Return new structure, never mutate inputs
return {
"routed_skill": best_skill["name"],
"confidence": best_score,
"workflow_context": workflow_features,
"timestamp": time.time()
}
Pattern 2: Execution with Fallback
def execute_pr_onboarding_with_fallback(
routed_skill: Dict,
pr_context: Dict,
repo_state: Dict,
max_retries: int = 2
) -> Dict:
"""Execute PR onboarding workflow with domain-specific fallback chain.
Implements Law 4 (Fail Fast, Fail Loud) by validating repo state before
attempting configuration changes. Falls back gracefully when CI/CD or
branch protection rules block automated onboarding.
"""
required_perms = routed_skill.get("required_permissions", [])
if not all(perm in repo_state.get("user_permissions", []) for perm in required_perms):
raise PermissionError(f"Insufficient permissions for {routed_skill['name']}: {required_perms}")
validated_context = {
"pr_id": pr_context["pr_id"],
"base_branch": pr_context["base_branch"],
"head_branch": pr_context["head_branch"],
"auto_approve": pr_context.get("auto_approve", False)
}
for attempt in range(max_retries + 1):
try:
# Attempt primary onboarding action: configure CODEOWNERS, CI, and branch rules
changes_made = []
if validated_context["auto_approve"]:
changes_made.append("auto_approve_enabled")
if repo_state.get("ci_configured") is False:
changes_made.append("ci_pipeline_initialized")
return {
"success": True,
"skill_executed": routed_skill["name"],
"configuration_applied": changes_made,
"attempts": attempt + 1,
"latency_ms": time.time() * 1000
}
except BranchProtectionError as e:
# Law 4: Fail immediately on immutable repo constraints
raise OnboardingError(f"Branch protection blocks auto-onboarding: {e}") from e
except CIConfigError as e:
# Transient CI failure - apply fallback chain
if attempt == max_retries:
return {
"success": False,
"fallback_triggered": True,
"fallback_action": "manual_review_template_generated",
"reason": str(e),
"attempts": attempt + 1
}
raise OnboardingError(f"Failed to onboard PR 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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.