Looper

Use Looper as a loop design coach and scaffolder. During design, interview, critique, validate, and write files. After emission, offer to run the loop in the current session using RUN_IN_SESSION.md; keep run-loop.py as the advanced external runner.

Workflow

1. Resolve the target path and optional --template <name> from the /looper arguments. If no target is given, use ./looper-output. If the target contains an existing loop.yaml, treat the task as an edit/resume instead of a fresh scaffold. If a template was requested, follow Template Mode below instead of the blank-slate interview in step 3.
2. Load the relevant rubric only when entering that stage:
   • Goal stage: references/goal-rubric.md.
   • Verification stage: references/verification-rubric.md.
   • Council stage: references/council-rubric.md.
   • Control stage: references/control-rubric.md.
   • Model detection or privacy details: references/model-detection.md.
3. Interview in seven stages: goal, verification, host model, council, gates/control, confirmation flow preview, emit/run option. In the control stage, cover execution boundary, isolation, no-progress signals, state, and run logging.
4. Critique each stage before accepting it. Prefer concrete alternatives over vague warnings. Push weak goals toward outcome, scope, context, and done state. Push weak verification toward programmatic checks first, then judge rubrics, then human signoff.
5. Keep reviewer and judge roles distinct. A reviewer writes notes. A judge returns a structured verdict. revise_until_clean must name a judge member or human as verdict_source.
6. Require multiple termination guards: max_iterations, a revision cap on each gate, a no-progress stop, and either a budget cap or an explicit human stop point.
7. Before any cross-vendor council member is selected, state what context will leave the user's machine, which CLI receives it, which redaction globs apply, and that both execution paths require first-send consent.
8. Show an ASCII flow preview of the planned loop and ask for confirmation before final emission. Optimize for Claude Code CLI readability.
9. Emit these files into the target:
   • loop.yaml
   • loop.resolved.json
   • LOOP.md
   • RUN_IN_SESSION.md
   • run-loop.py
   • loop-workspace/
   • README.md
10. After writing loop.yaml, resolve the helper Python (see Helper Python below) and run: "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py compile <target>/loop.yaml --out <target>/loop.resolved.json --render <target>/LOOP.md --session-prompt <target>/RUN_IN_SESSION.md Then run "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py lint <target>/loop.yaml and relay the findings: fix any error[...] before continuing (the spec would not behave as written), and surface warning[...] lines to the user as design coaching they may accept or address.
11. Ask whether the user wants to run the loop now in this session. If yes, follow RUN_IN_SESSION.md directly as the active task. If no, explain that the same file is the easy restart path and run-loop.py is available for advanced external execution.

Template Mode

The pattern library lives at ${CLAUDE_SKILL_DIR}/templates/loops/ — one directory per template containing a complete, compilable loop.yaml (with {{PLACEHOLDER}} tokens marking project-specific slots), a README.md (use-when, placeholder table, customization notes), and optionally scripts/ with helper checkers. The catalog index is templates/loops/README.md.

A template is a pre-answered interview, not a bypass of design review:

0. If the target directory already contains a loop.yaml, the edit/resume rule in step 1 wins: do not overwrite it with a template. Say the directory already has a loop and ask the user to pick an empty target or confirm they want it replaced before continuing.
1. If --template has no name, an unknown name, or the user asks what is available, show the catalog table (template + use-when) and let them pick.
2. Read the template's loop.yaml and README.md. Use the template as the seed instead of a blank spec.
3. Run a compressed interview in place of the seven blank-slate stages: ask for each {{PLACEHOLDER}} slot named in the template README, run the host-model stage against detected CLIs (detect-models) and swap host / council invocations to what is actually installed and authed, then confirm target and workspace paths.
4. Everything after the interview still applies unchanged: critique each pre-filled stage against its rubric (step 4), the structural rules (steps 5–8) including the cross-vendor egress statement, the ASCII flow preview, confirmation, emission, and compile.
5. Never emit while any {{ token remains in loop.yaml. The compiler prints looper: warning: unresolved template placeholders remain ... for this case — treat that warning as a blocker, not advice.
6. At emission, copy the template's scripts/ directory (when present) into <target>/scripts/ alongside the standard emitted files, before running compile.

File Rules

• Write argv arrays, never shell command strings, for all model and check invocations.
• Do not write API keys, access tokens, passwords, or CLI auth material into loop.yaml, loop.resolved.json, or model registries.
• Default redaction globs are .env, .env.*, secrets/**, and **/*.key.
• Keep loop.yaml human-readable and commented where useful. The emitted runner reads only loop.resolved.json.
• Keep RUN_IN_SESSION.md as the default/easy execution handoff. It is meant for the current LLM session or a future pasted prompt.
• Copy templates/run-loop.py exactly unless the user explicitly asks to edit the external runner contract.

Helper Python

The installer creates a private venv inside the skill directory. Its Python lives at .venv/bin/python on macOS/Linux and .venv/Scripts/python.exe on Windows. Shell state does not persist between commands, so prefix every helper invocation below with this resolution (works in POSIX shells and Git Bash on Windows):

LOOPER_PYTHON="${CLAUDE_SKILL_DIR}/.venv/bin/python"; [ -x "$LOOPER_PYTHON" ] || LOOPER_PYTHON="${CLAUDE_SKILL_DIR}/.venv/Scripts/python.exe"; [ -x "$LOOPER_PYTHON" ] || { LOOPER_PYTHON=python3; "$LOOPER_PYTHON" -c "" >/dev/null 2>&1 || LOOPER_PYTHON=python; }

The final fallback executes the candidate rather than just locating it: on Windows, python3 on PATH is often the Microsoft Store alias stub, which exists but cannot run scripts. If no candidate can execute -c "", tell the user to rerun the Looper installer (it creates the venv).

Helper Scripts

Each command below assumes the Helper Python resolution is prefixed in the same shell invocation:

• Detect model CLIs: "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py detect-models --write
• Register a custom CLI (quote the whole invocation if it contains flags): "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py register-model <id> --invoke "<cmd> [args...]"
• Compile and render: "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py compile <target>/loop.yaml --out <target>/loop.resolved.json --render <target>/LOOP.md --session-prompt <target>/RUN_IN_SESSION.md
• Lint against the design-rubric anti-patterns (add --strict to fail on warnings, --json for tooling): "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py lint <target>/loop.yaml
• Render only the in-session handoff: "$LOOPER_PYTHON" ${CLAUDE_SKILL_DIR}/scripts/looper.py session-prompt <target>/loop.resolved.json --out <target>/RUN_IN_SESSION.md

Confirmation Flow Preview

Use this shape and customize labels:

+--------------------------------+
| 1. Goal + context              |
| read sources                   |
+--------------------------------+
               |
               v
+--------------------------------+
| 2. Draft plan.md               |
| state -> state.json            |
+--------------------------------+
               |
               v
+--------------------------------+
| 3. Plan gate                   |
| verdict: reviewer-1            |
+--------------------------------+
               | needs work -> revise <= 3 -> step 2
               | pass
               v
+--------------------------------+
| 4. Write delivery-N.md         |
| log -> run-log.md              |
+--------------------------------+
               |
               v
+--------------------------------+
| 5. Delivery gate               |
| verdict: reviewer-1            |
+--------------------------------+
               | needs work -> revise <= 3 -> step 4
               | pass
               v
+--------------------------------+
| 6. Final output                |
| all gates clean                |
+--------------------------------+

Stops: pass gates | max 12 iterations | no progress x2 | budget 30m, $5.0, 2000000 tokens

Emit Checklist

• The goal has a clear outcome, scope boundary, context sources, and done state.
• Verification criteria are typed as programmatic, judge, or human.
• At least one criterion is not purely vibe-based unless the user explicitly accepts that risk.
• Each revise_until_clean gate has a valid verdict_source.
• Every external invocation is an argv array with a timeout.
• Cross-vendor egress is scoped, redacted, and consent-gated.
• loop_control has iteration, revision, no-progress, and wall-clock or budget caps.
• Execution boundary and isolation are explicit, even when the choice is the current workspace.
• Observability names a run-log.md and state.json path.
• loop.resolved.json, LOOP.md, and RUN_IN_SESSION.md compile successfully before handoff.