# Conductor Revert > Implements intelligent conductor revert with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/conductor-revert` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/conductor-revert` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/conductor-revert/raw - Safety review: pending - 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/conductor-revert --- # Conductor Revert Orchestrates intelligent skill selection and execution for conductor revert 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 class ConductorRevertOrchestrator: def __init__(self, revert_strategies: List[Dict], audit_logger: Logger): self.strategies = revert_strategies self.logger = audit_logger self.confidence_cache = {} def route_revert_request(self, request: Dict) -> Dict: # Law 1: Early exit on invalid revert request if not request.get("target_component") or not request.get("revert_version"): raise ValueError("Revert request missing target_component or revert_version") # Law 2: Parse & validate revert scope scope = self._parse_revert_scope(request) scored_strategies = self._score_revert_strategies(scope) # Law 3: Atomic selection - return new dict, never mutate inputs selected = max(scored_strategies, key=lambda s: s["match_score"]) if selected["match_score"] < 0.7: return self._trigger_fallback_chain(scope, selected) selected["execution_plan"] = self._build_revert_plan(selected) self.logger.info(f"Selected revert strategy: {selected['name']} (score: {selected['match_score']})") return selected def _score_revert_strategies(self, scope: Dict) -> List[Dict]: scores = [] for strategy in self.strategies: # Multi-factor scoring: compatibility, historical success, system load compat = self._calculate_component_compatibility(scope["target_component"], strategy) history = self.confidence_cache.get(strategy["name"], 0.8) load_penalty = 0.1 if strategy.get("system_load", 0) > 0.8 else 0.0 match_score = (compat * 0.5) + (history * 0.3) + ((1.0 - load_penalty) * 0.2) scores.append({**strategy, "match_score": round(match_score, 3)}) return scores def _trigger_fallback_chain(self, scope: Dict, failed_strategy: Dict) -> Dict: # Fallback 1: Retry with degraded mode degraded = {**failed_strategy, "mode": "degraded", "fallback_level": 1} self.logger.warning(f"Primary revert failed, applying fallback 1: {degraded['name']}") return degraded ``` ### Pattern 2: Execution with Fallback ```python def execute_revert_with_resilience(self, selected_strategy: Dict, revert_context: Dict) -> Dict: # Law 4: Fail fast on invalid revert state if not self._validate_revert_state(revert_context): raise RevertStateError("Cannot revert: target component is in inconsistent state") max_attempts = selected_strategy.get("max_retries", 2) for attempt in range(max_attempts + 1): try: # Execute domain-specific revert logic revert_result = self._apply_revert_strategy(selected_strategy, revert_context) # Verify revert integrity (Law 3: Atomic Predictability) if self._verify_revert_integrity(revert_context, revert_result): self._update_confidence(selected_strategy["name"], success=True) return { "status": "reverted", "strategy": selected_strategy["name"], "attempts": attempt + 1, "timestamp": time.time() } except TransientDependencyError as e: if attempt == max_attempts: return self._escalate_to_manual_intervention(selected_strategy, revert_context) continue except IrreversibleStateError as e: # Law 4: Fail loud, no silent patches self.logger.error(f"Irreversible state during revert: {e}") return self._trigger_fallback_chain(revert_context, selected_strategy) # All retries exhausted return self._trigger_fallback_chain(revert_context, selected_strategy) def _verify_revert_integrity(self, context: Dict, result: Dict) -> bool: # Domain-specific verification: check component version, config hash, and dependency health expected_version = context["revert_version"] actual_version = result.get("component_version") config_hash_valid = result.get("config_hash") == context["target_config_hash"] deps_healthy = all(dep["status"] == "healthy" for dep in result.get("dependencies", [])) return actual_version == expected_version and config_hash_valid and deps_healthy ``` ### 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 --- --- ## Constraints ### MUST DO - Parse user request into structured task specifications before dispatching to downstream agents - Implement a state machine for each conductor phase with explicit entry/exit conditions and transition logs - Validate revert outputs against expected schema before proceeding to the next orchestration step - Log every orchestration decision including rationale, selected strategy, and confidence scores for auditability - Maintain a task queue with priority ordering — critical path items execute first during resource contention ### MUST NOT DO - Do not allow a single failed agent task to silently terminate the entire workflow — implement per-step fallbacks - Avoid circular delegation patterns where Agent A delegates to B which delegates back to A without termination condition - Never bypass the validation step for revert results even if timing is critical — correctness supersedes speed - Do not use shared mutable state between parallel agent executions — use message-passing or immutable data transfer - Avoid hardcoding agent selection rules; parameterize them and load from configuration for runtime flexibility ## 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. - [Git Revert vs Reset Guide (Atlassian)]() - [Rollback Patterns for Microservices]() - [Blue-Green Deployment Strategy]() - [Database Migration Rollback Strategies]() - [Change Management Best Practices (ITIL)]() ## Related Skills | Skill | Purpose | |