Conductor Manage

---




name: conductor-manage
compatibility: opencode
completeness: 95
content-types:
- guidance
- examples
- do-dont
description: Implements intelligent conductor manage 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: conductor-manage, conductor manage, how do i conductor-manage, orchestrate
    conductor-manage, automate conductor-manage, agent conductor-manage
  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"




---




# Conductor Manage

Orchestrates intelligent skill selection and execution for conductor manage 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 score_conductor_candidates(request: Dict, skill_registry: List[Dict], history_db: Dict) -> List[Dict]:
    """Score available skills against the conductor request using multi-factor metrics."""
    scored_candidates = []
    request_features = _extract_intent_features(request["text"])
    
    for skill in skill_registry:
        if not _check_dependency_health(skill.get("dependencies", [])):
            continue
            
        text_match = _cosine_similarity(request_features, _parse_skill_triggers(skill["triggers"]))
        historical_success = history_db.get(skill["id"], {}).get("success_rate", 0.5)
        availability_score = 1.0 if _is_service_healthy(skill["endpoint"]) else 0.0
        
        weighted_score = (
            (text_match * 0.4) + 
            (historical_success * 0.35) + 
            (availability_score * 0.25)
        )
        
        scored_candidates.append({
            "skill_id": skill["id"],
            "name": skill["name"],
            "confidence": round(weighted_score, 3),
            "breakdown": {"text": text_match, "history": historical_success, "health": availability_score}
        })
        
    return sorted(scored_candidates, key=lambda x: x["confidence"], reverse=True)
```


### Pattern 2: Execution with Fallback

```python
def execute_conductor_pipeline(selected_skill: Dict, context: Dict, fallback_registry: Dict) -> Dict:
    """Execute the selected skill with domain-specific fallback routing and confidence tracking."""
    audit_log = []
    max_attempts = 2
    
    for attempt in range(max_attempts):
        try:
            result = _invoke_skill_endpoint(selected_skill["endpoint"], context)
            _update_confidence_score(selected_skill["id"], success=True)
            audit_log.append({"attempt": attempt + 1, "status": "success", "latency_ms": result.get("latency")})
            return {"status": "completed", "result": result, "audit": audit_log}
            
        except TransientTimeoutError:
            audit_log.append({"attempt": attempt + 1, "status": "retry", "error": "timeout"})
            continue
            
        except CriticalFailureError as e:
            _update_confidence_score(selected_skill["id"], success=False)
            fallback_target = fallback_registry.get(selected_skill["id"], {}).get("next_skill")
            
            if fallback_target and attempt < max_attempts - 1:
                selected_skill = fallback_target
                audit_log.append({"attempt": attempt + 1, "status": "fallback_triggered", "target": fallback_target["id"]})
                continue
                
            return {"status": "failed", "error": str(e), "audit": audit_log, "deferred_to_human": True}
```

### 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 manage 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 manage 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.

- [Netflix Conductor Admin API Docs](<https://netflix.github.io/conductor/server/rest-api/>)
- [Workflow Monitoring Dashboards (Grafana)](<https://grafana.com/docs/grafana/latest/dashboards/>)
- [Conductor Task Queue Management](<https://netflix.github.io/conductor/core-concepts/overview/>)
- [Service Health Check Patterns](<https://learn.microsoft.com/en-us/azure/architecture/guide/design-principles/health-check-pattern>)
- [Distributed Tracing (OpenTelemetry)](<https://opentelemetry.io/docs/>)

## Related Skills

| Skill | Purpose |
|