> ## Documentation Index
> Fetch the complete documentation index at: https://docs.10et.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Peter Parker

> The meta-orchestrator that runs your nightly improvement loop

Peter Parker (PP) is the meta-orchestrator. It decides which agents to run, in what order, and coordinates the entire nightly improvement cycle.

## What PP Does

```
tenet peter daily
  |
  +-- 1. Mine training tuples from journals
  +-- 2. Synthesize product context
  +-- 3. Layer 3: Strategic reasoning (which agents to run?)
  +-- 4. Hub health check
  +-- 5. Run stale agents (5 rounds each, capped at 1 hour)
  +-- 6. Pick up kanban backlog issues → create PRs
  +-- 7. Post summary event
```

## Strategic Reasoning (Layer 3)

PP uses Stratus to decide which agents deserve compute tonight:

```
Strategic reasoning:
  Run: ["test-coverage", "code-quality"]
  Skip: ["cli-speed"] — already optimized, diminishing returns
  Reasoning: test-coverage has 87% headroom, highest ROI
```

This prevents wasting tokens on agents that have plateaued.

## Running PP

### Daily Loop (Nightly Cron)

```bash theme={null}
tenet peter daily
```

Full orchestration cycle. Typically run at 2 AM via OpenClaw cron:

```json theme={null}
{
  "schedule": { "kind": "cron", "expr": "0 2 * * *" },
  "payload": {
    "kind": "agentTurn",
    "message": "Run the TENET nightly loop..."
  }
}
```

### Single Agent Run

```bash theme={null}
tenet peter agent test-coverage --rounds 5
```

### Agent Swarm

```bash theme={null}
tenet peter agent swarm --rounds 10
```

Runs all agents with the meta-orchestrator scheduling who goes next based on EMA reward.

### PR Mode

```bash theme={null}
tenet peter pr --task "Fix the auth token refresh bug"
```

Creates a branch, makes changes, opens a PR. Used by the kanban pickup flow.

## The Kanban Pipeline

PP integrates with GitHub Issues for autonomous task execution:

```
Issue filed (tenet/backlog label)
  ↓ (every 30 min, flow: pick-up-linear-tasks)
PP picks up highest-priority issue
  ↓
Moves label: tenet/backlog → tenet/in-progress
  ↓
Spawns: tenet peter pr --task "GitHub #N: <title>"
  ↓
Agent makes changes, creates PR
  ↓
CI runs eval
  ↓
Score improves → auto-merge → close issue → tenet/done
Score regresses → request changes on PR
```

### Issue Labels

| Label               | Meaning                      |
| ------------------- | ---------------------------- |
| `tenet/backlog`     | Available for PP pickup      |
| `tenet/in-progress` | PP is working on it          |
| `tenet/eval`        | PR created, waiting for eval |
| `tenet/done`        | Merged and closed            |
| `scope:tenet-cli`   | Target repo hint             |

## Training Data Capture

Every PP action generates training tuples:

```json theme={null}
{
  "agent": "test-coverage",
  "state": { "composite_score": 0.1276 },
  "action": { "type": "add_tests", "description": "..." },
  "reward": { "composite_delta": 0.0031, "improved": true }
}
```

These feed the policy head for better action selection in future runs.

## PP Commands

```bash theme={null}
tenet peter daily                    # Full nightly loop
tenet peter agent list               # List configured agents
tenet peter agent <name> --rounds N  # Run specific agent
tenet peter agent swarm --rounds N   # Run all agents
tenet peter pr --task "<task>"       # Branch + change + PR
tenet peter status                   # Show status + recent events
tenet peter telemetry                # Run telemetry agent
tenet peter synthesize               # Regenerate product context
```