---
name: conductor-validator
description: 'Validates Conductor project artifacts for completeness,

  consistency, and correctness. Use after setup, when diagnosing issues, or

  before implementation to verify project context.

  '
risk: unknown
source: community
date_added: '2026-02-27'
---

# Check if conductor directory exists
ls -la conductor/

# Find all track directories
ls -la conductor/tracks/

# Check for required files
ls conductor/index.md conductor/product.md conductor/tech-stack.md conductor/workflow.md conductor/tracks.md
```

## Use this skill when

- Working on check if conductor directory exists tasks or workflows
- Needing guidance, best practices, or checklists for check if conductor directory exists

## Do not use this skill when

- The task is unrelated to check if conductor directory exists
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Pattern Matching

**Status markers in tracks.md:**

```
- [ ] Track Name  # Not started
- [~] Track Name  # In progress
- [x] Track Name  # Complete
```

**Task markers in plan.md:**

```
- [ ] Task description  # Pending
- [~] Task description  # In progress
- [x] Task description  # Complete
```

**Track ID pattern:**

```
<type>_<name>_<YYYYMMDD>
Example: feature_user_auth_20250115
```

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Cache workflow configurations and automation patterns. Retrieve prior pipeline designs to avoid re-building similar flows from scratch.

```bash
# Check for prior workflow/automation context before starting
python3 execution/memory_manager.py auto --query "automation patterns and workflow configurations for Conductor Validator"
```

### Storing Results

After completing work, store workflow/automation decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Workflow: automated data pipeline with retry logic, dead-letter queue, and Slack alerts on failure" \
  --type technical --project <project> \
  --tags conductor-validator workflow
```

### Multi-Agent Collaboration

Share workflow state with other agents so they can trigger, monitor, or extend the automation.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Workflow automation deployed — pipeline processing 1000+ events/day with 99.9% success rate" \
  --project <project>
```

### Playbook Engine

Combine this skill with others using the Playbook Engine (`execution/workflow_engine.py`) for guided multi-step automation with progress tracking.

<!-- AGI-INTEGRATION-END -->
