Checkmate

Checkmate

A deterministic Python loop (scripts/run.py) calls an LLM for worker and judge roles. Nothing leaves until it passes — and you stay in control at every checkpoint.

Requirements

• OpenClaw platform CLI (openclaw) — must be available in PATH. Used for:
  • openclaw gateway call sessions.list — resolve session UUID for turn injection
  • openclaw agent --session-id <UUID> — inject checkpoint messages into the live session
  • openclaw message send — fallback channel delivery (e.g. Telegram, Signal)
• Python 3 — run.py is pure stdlib; no pip packages required
• No separate API keys or env vars needed — routes through the gateway's existing OAuth

Security & Privilege Model

⚠️ This is a high-privilege skill. Read before using in batch/automated mode.

Spawned workers and judges inherit full host-agent runtime, including:

• exec (arbitrary shell commands)
• web_search, web_fetch
• All installed skills (including those with OAuth-bound credentials — Gmail, Drive, etc.)
• sessions_spawn (workers can spawn further sub-agents)

This means the task description you provide directly controls what the worker does — treat it like code you're about to run, not a message you're about to send.

Batch mode (--no-interactive) removes all human gates. In interactive mode (default), you approve criteria and each checkpoint before the loop continues. In batch mode, criteria are auto-approved and the loop runs to completion autonomously — only use this for tasks and environments you fully trust.

User-input bridging writes arbitrary content to disk. When you reply to a checkpoint, the main agent writes your reply verbatim to user-input.md in the workspace. The orchestrator reads it and acts on it. Don't relay untrusted third-party content as checkpoint replies.

When to Use

Use checkmate when correctness matters more than speed — when "good enough on the first try" isn't acceptable.

Good fits:

• Code that must pass tests or meet a spec
• Docs or reports that must hit a defined quality bar
• Research that must be thorough and cover specific ground
• Any task where you'd otherwise iterate manually until satisfied

Trigger phrases (say any of these):

• checkmate: TASK
• keep iterating until it passes
• don't stop until done
• until it passes
• quality loop: TASK
• iterate until satisfied
• judge and retry
• keep going until done

Architecture

scripts/run.py  (deterministic Python while loop — the orchestrator)
  ├─ Intake loop [up to max_intake_iter, default 5]:
  │    ├─ Draft criteria (intake prompt + task + refinement feedback)
  │    ├─ ⏸ USER REVIEW: show draft → wait for approval or feedback
  │    │     approved? → lock criteria.md
  │    │     feedback? → refine, next intake iteration
  │    └─ (non-interactive: criteria-judge gates instead of user)
  │
  ├─ ⏸ PRE-START GATE: show final task + criteria → user confirms "go"
  │         (edit task / cancel supported here)
  │
  └─ Main loop [up to max_iter, default 10]:
       ├─ Worker: spawn agent session → iter-N/output.md
       │          (full runtime: exec, web_search, all skills, OAuth auth)
       ├─ Judge:  spawn agent session → iter-N/verdict.md
       ├─ PASS?  → write final-output.md, notify user, exit
       └─ FAIL?  → extract gaps → ⏸ CHECKPOINT: show score + gaps to user
                     continue?  → next iteration (with judge gaps)
                     redirect:X → next iteration (with user direction appended)
                     stop?      → end loop, take best result so far

Interactive mode (default): user approves criteria, confirms pre-start, and reviews each FAIL checkpoint. Batch mode (--no-interactive): fully autonomous; criteria-judge gates intake, no checkpoints.

User Input Bridge

When the orchestrator needs user input, it:

1. Writes workspace/pending-input.json (kind + workspace path)
2. Sends a notification via --recipient and --channel
3. Polls workspace/user-input.md every 5s (up to --checkpoint-timeout minutes)

The main agent acts as the bridge: when pending-input.json exists and the user replies, the agent writes their response to user-input.md. The orchestrator picks it up automatically.

Each agent session is spawned via:

openclaw agent --session-id <isolated-id> --message <prompt> --timeout <N> --json

Routes through the gateway WebSocket using existing OAuth — no separate API key. Workers get full agent runtime: exec, web_search, web_fetch, all skills, sessions_spawn.

Your Job (main agent)

When checkmate is triggered:

1. Get your session UUID (for direct agent-turn injection):

   openclaw gateway call sessions.list --params '{"limit":1}' --json \
     | python3 -c "import json,sys; s=json.load(sys.stdin)['sessions'][0]; print(s['sessionId'])"
   

   Also note your --recipient (channel user/chat ID) and --channel as fallback.

2. Create workspace:

   bash <skill-path>/scripts/workspace.sh /tmp "TASK"
   

   Prints the workspace path. Write the full task to workspace/task.md if needed.

3. Run the orchestrator (background exec):

   python3 <skill-path>/scripts/run.py \
     --workspace /tmp/checkmate-TIMESTAMP \
     --task "FULL TASK DESCRIPTION" \
     --max-iter 10 \
     --session-uuid YOUR_SESSION_UUID \
     --recipient YOUR_RECIPIENT_ID \
     --channel <your-channel>
   

   Use exec with background=true. This runs for as long as needed. Add --no-interactive for fully autonomous runs (no user checkpoints).

4. Tell the user checkmate is running, what it's working on, and that they'll receive criteria drafts and checkpoint messages via your configured channel to review and approve.

5. Bridge user replies: When user responds to a checkpoint message, check for pending-input.json and write their response to workspace/user-input.md.

Bridging User Input

When a checkpoint message arrives (the orchestrator sent the user a criteria/approval/checkpoint request), bridge their reply:

# Find active pending input
cat <workspace-parent>/checkmate-*/pending-input.json 2>/dev/null

# Route user's reply
echo "USER REPLY HERE" > /path/to/workspace/user-input.md

The orchestrator polls for this file every 5 seconds. Once written, it resumes automatically and deletes the file.

Accepted replies at each gate:

Parameters

Workspace layout

memory/checkmate-YYYYMMDD-HHMMSS/
├── task.md               # task description (user may edit pre-start)
├── criteria.md           # locked after intake
├── feedback.md           # accumulated judge gaps + user direction
├── state.json            # {iteration, status} — resume support
├── pending-input.json    # written when waiting for user; deleted after response
├── user-input.md         # agent writes user's reply here; read + deleted by orchestrator
├── intake-01/
│   ├── criteria-draft.md
│   ├── criteria-verdict.md  (non-interactive only)
│   └── user-feedback.md     (interactive: user's review comments)
├── iter-01/
│   ├── output.md         # worker output
│   └── verdict.md        # judge verdict
└── final-output.md       # written on completion

Resume

If the script is interrupted, just re-run it with the same --workspace. It reads state.json and skips completed steps. Locked criteria.md is reused; completed iter-N/output.md files are not re-run.

Prompts

Active prompts called by run.py:

• prompts/intake.md — converts task → criteria draft
• prompts/criteria-judge.md — evaluates criteria quality (APPROVED / NEEDS_WORK) — used in non-interactive mode
• prompts/worker.md — worker prompt (variables: TASK, CRITERIA, FEEDBACK, ITERATION, MAX_ITER, OUTPUT_PATH)
• prompts/judge.md — evaluates output against criteria (PASS / FAIL)

Reference only (not called by run.py):

• prompts/orchestrator.md — architecture documentation explaining the design rationale