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cto-platform
by 5dlabs
Cognitive Task Orchestrator - GitOps on Bare Metal or Cloud for AI Agents
⭐ 2🍴 1📅 Jan 25, 2026
SKILL.md
name: cto-platform description: CTO Platform lifecycle expertise - Play workflow orchestration, intake processing, MCP tools, and agent sequencing. Use when managing the full development lifecycle from PRD to deployed code.
CTO Platform Skill
Comprehensive knowledge of the CTO (Cognitive Task Orchestrator) platform for AI-driven development workflows.
When to Use
- Running intake to generate tasks from PRDs
- Starting and monitoring Play workflows
- Understanding agent sequencing and responsibilities
- Debugging workflow issues
Intake Workflow
MCP Tool
mcp_cto_intake(project_name="my-project")
Flow
PRD Document → mcp_cto_intake() → Linear Project Created
→ PRD Issue with Morgan as delegate
→ PM Webhook → Argo Workflow
→ Morgan Pod generates tasks.json
→ Task issues created in Linear
Key Points
local: falsein cto-config.json = Argo workflow (production)local: true= local binary execution (debugging only)- Morgan auto-assigned triggers intake via PM webhook
Play Workflow
MCP Tools
mcp_cto_play() # Start workflow, auto-detects next task
mcp_cto_play_status() # Query progress
mcp_cto_jobs() # List running workflows
mcp_cto_stop_job() # Cancel workflow
Agent Sequence (Per Task)
┌─────────────────────────────────────────────────────────────────────────────┐
│ PLAY WORKFLOW (Per Task) │
│ │
│ 1. IMPLEMENTATION AGENT (based on task.agentHint) │
│ ├─ bolt (infrastructure - Task 1 ALWAYS, plus any infra tasks) │
│ ├─ rex (Rust: axum, tokio, sqlx) │
│ ├─ grizz (Go: chi, grpc, pgx) │
│ ├─ nova (Node.js/Bun: Elysia, Effect, Drizzle) │
│ ├─ blaze (React/Web: Next.js, shadcn/ui, TailwindCSS) │
│ ├─ tap (Mobile: Expo, React Native) │
│ └─ spark (Desktop: Electron) │
│ │
│ 2. SUPPORT AGENTS (sequential, run for EVERY task) │
│ ├─ Cleo (quality) ──── max 5 retries │
│ ├─ Cipher (security) ─ max 2 retries │
│ ├─ Tess (testing) ──── max 5 retries │
│ └─ Atlas (integration) final merge gate │
└─────────────────────────────────────────────────────────────────────────────┘
cto-config.json Structure
{
"version": "1.0",
"orgName": "5DLabs",
"defaults": {
"intake": {
"local": false, // MUST be false for production
"githubApp": "5DLabs-Morgan"
},
"linear": {
"teamId": "CTOPA",
"pmServerUrl": "http://localhost:8081"
},
"play": {
"repository": "5dlabs/cto",
"healerEndpoint": "http://localhost:8083",
"autoMerge": true, // Atlas auto-merges PRs
"implementationMaxRetries": 10,
"qualityMaxRetries": 5,
"securityMaxRetries": 2,
"testingMaxRetries": 5
}
},
"agents": {
"morgan": { ... },
"bolt": { ... },
"rex": { ... },
// ...
}
}
Key Fields
| Field | Purpose |
|---|---|
intake.local | false = Argo workflow, true = local binary |
play.autoMerge | Atlas merges PRs automatically |
play.healerEndpoint | Healer API for session notifications |
play.*MaxRetries | Per-agent retry limits |
Execution Architecture
┌─────────────────────────────────────────────────────────────────────────────┐
│ IN-CLUSTER EXECUTION FLOW │
│ │
│ MCP Tool Call (Droid/Cursor) │
│ └─→ Submits Argo Workflow │
│ └─→ Controller creates CodeRuns (pods) │
│ └─→ Each agent runs in isolated container │
│ └─→ Linear sidecar streams activities │
│ └─→ Healer monitors via Loki │
└─────────────────────────────────────────────────────────────────────────────┘
Local Development
Services to Run
just dev-controller # Controller
just dev-pm # PM Server (:8081)
just dev-healer-play-api # Healer (:8083)
# Or all at once:
mprocs
Port Forwards
kubectl port-forward svc/argocd-server -n argocd 8080:80
kubectl port-forward svc/argo-workflows-server -n automation 2746:2746
Autonomy Permissions
When running E2E tests with full autonomy:
- MAY merge pull requests via Atlas
- MAY update Linear issues directly
- MAY execute all MCP tools without confirmation
- MAY handle all agent transitions
- MAY create and close GitHub issues
Reference Documentation
- docs/linear-integration-workflow.md - Linear webhook flow
- docs/heal-play.md - Healer monitoring
- cto-config.template.json - Config field documentation
- AGENTS.md - Agent overview
Score
Total Score
65/100
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