looper

 _        ___   ___  ____  _____ ____
| |      / _ \ / _ \|  _ \| ____|  _ \
| |     | | | | | | | |_) |  _| | |_) |
| |___  | |_| | |_| |  __/| |___|  _ <
|_____|  \___/ \___/|_|   |_____|_| \_\

Example loop diagram

Looper turns a fuzzy automation idea into a reviewable loop shape before any
runner starts changing files. This example comes from
examples/ai-workflow-mapping.

flowchart TD
    G["Goal + context<br/>process notes + definition of done"] --> P["Draft plan.md<br/>host: codex / gpt-5"]
    P --> PG{"Plan gate<br/>judge: reviewer-1"}
    PG -- "revise <= 3" --> P
    PG -- "pass" --> D["Write delivery-N.md<br/>map the workflow"]
    D --> DG{"Delivery gate<br/>programmatic check + judge"}
    DG -- "revise <= 3" --> D
    DG -- "pass" --> F["Final output<br/>all gates clean"]

    S["State + log<br/>state.json + run-log.md"] -. "records" .-> P
    S -. "records" .-> D
    Stop["Stop guards<br/>max 12 iterations<br/>no progress x2<br/>budget caps"] -. "watch" .-> PG
    Stop -. "watch" .-> DG

A loop design coach for Claude Code. Looper is a skill that helps you design a good agent loop — a sharp goal, checkable verification, and a second model in the review seat — then lets you run it in the same session or save it as a portable spec. It is a design layer first: it writes files and hands the current session a clear execution prompt.

Invoke it with /looper. It interviews you, critiques your design against built-in best-practice rubrics, lets you wire in a cross-model reviewer or judge (including non-Claude models), shows you the loop as a terminal-friendly ASCII flow preview, and writes out RUN_IN_SESSION.md, loop.yaml, a compiled loop.resolved.json, a human-readable LOOP.md, a thin run-loop.py you own and edit, plus an empty loop-workspace/ and a README for the loop.

Maintainer: Kevin Simback · GitHub @ksimback · X @ksimback
License: MIT

Why not just use /goal or /loop?

Claude Code already ships two pieces that look loop-shaped. They're useful — and they operate at a different layer than Looper. The short version: /goal and /loop run a loop; Looper helps you design one that's worth running, then gives the current session or an external runner a clear spec to follow.

What /goal actually does

/goal sets a persistent objective for the session. Once set, Claude keeps it as a reference point, checks after each significant action whether the current state satisfies the goal, and keeps working until it does — so it doesn't stop and ask after every step.

That's genuinely useful for persistence. But three things are missing for serious work:

• No coaching. /goal takes whatever goal you type, however vague. It won't tell you the goal is unfalsifiable or that "done" was never defined. Garbage goal in, confidently-wrong loop out.
• Self-evaluation by one model. The goal condition is judged by the same model doing the work. That's precisely the blind spot a review council exists to close — the model grading its own homework. In practice this self-check skews unreliable (sometimes too lenient, sometimes too conservative).
• No structure to inspect or reuse. The goal lives in the session, not as a portable, versionable artifact. There's no typed verification, no explicit gates, no second model.

What /loop actually does

/loop is a scheduler. You give it an interval and a task; it turns that into a cron job, registers it, and re-fires the prompt or skill on that cadence — polling CI, watching a deploy, monitoring a background job. (Omit the interval and it self-paces.)

It's the right tool for "run this thing every five minutes until I say stop." It is not a loop designer: it doesn't help you decide what runs, define success criteria, or bring in a reviewer. It schedules; it doesn't critique.

Where Looper fits

Looper is the design layer that sits in front of both. It produces a well-specified loop — coached goal, typed verification, a cross-model gate — then gives you a default in-session handoff prompt plus a portable spec. The same design can be run immediately in the conversation, driven by /goal for persistence, fired on a schedule by /loop, or run later with Python. Looper doesn't replace them; it gives them something good to run.

The honest summary: if you already know your loop is well-designed and you just need it to persist or to fire on a schedule, /goal and /loop are the right reach. Looper exists for the part those don't touch — making sure the loop is worth persisting before you hand it off, and making sure something other than the author is checking the work.

Sources for the /goal and /loop behavior described above: Claude Code skills and commands documentation at code.claude.com/docs. Behavior and version gates change frequently; verify against upstream before shipping.

What Looper provides

Looper provides loop design discipline: a clear goal, context sources,
checkable verification, reviewer/judge gates, termination guards, a portable
spec, a same-session execution handoff, and lightweight run state/log files.

Looper does not provide durable orchestration. It does not schedule cron
jobs for you, persist step-level retries across process restarts, manage
sub-agent lifecycles, enforce concurrency controls, or store a production run
history. If you need those guarantees, use Looper to design the loop and hand
the resulting spec to an orchestrator built for durable execution.

Healthy loop checklist

Before running a loop, Looper pushes you to make these explicit:

• Goal: what outcome the loop is trying to produce.
• Context: which files, commands, issues, or external sources the loop may
  inspect.
• Actions: which model, tools, commands, or human handoffs may change state.
• Feedback: which programmatic checks, judges, reviewers, or humans decide
  whether work is good enough.
• State: where the loop records status, decisions, blockers, and outputs.
• Stop conditions: success, max iterations, revision caps, no-progress
  signals, and budget caps. The external runner enforces wall-clock caps;
  token/USD caps are operator-visible advisory limits unless you add accounting
  around the configured CLIs.
• Execution boundary: current workspace, branch/worktree, external runner,
  or a separate durable orchestrator.

Quick start

Install as a global personal skill and slash command.

On Windows PowerShell:

irm https://raw.githubusercontent.com/ksimback/looper/main/install.ps1 | iex

On macOS/Linux:

curl -fsSL https://raw.githubusercontent.com/ksimback/looper/main/install.sh | bash

If you prefer to inspect each step, use the manual install:

Windows PowerShell:

git clone https://github.com/ksimback/looper "$env:USERPROFILE\.claude\skills\looper"
New-Item -ItemType Directory -Force "$env:USERPROFILE\.claude\commands" | Out-Null
Copy-Item "$env:USERPROFILE\.claude\skills\looper\commands\looper.md" "$env:USERPROFILE\.claude\commands\looper.md" -Force

macOS/Linux:

git clone https://github.com/ksimback/looper "$HOME/.claude/skills/looper"
mkdir -p "$HOME/.claude/commands"
cp "$HOME/.claude/skills/looper/commands/looper.md" "$HOME/.claude/commands/looper.md"

Then, in Claude Code:

/looper

Looper interviews you, writes the artifacts into a folder called looper-output,
and shows you an ASCII flow preview to confirm before anything is finalized. The
installer also creates a private .venv inside the skill directory and installs
PyYAML, which the helper compiler needs to read loop.yaml. It
then offers to run the loop right there in the same Claude Code session.

If you want a different folder name, pass it after /looper, for example
/looper client-onboarding-loop.

Easy: run in the same session

The default path is to let Looper continue in the same conversation. It follows
the generated RUN_IN_SESSION.md handoff, writes plan.md,
delivery-N.md, review-N.md, state.json, and run-log.md into the loop
workspace, and stops when the gates pass, a cap is reached, or repeated
no-progress is detected.

Advanced: run outside the session

Use the Python runner when you want to run the loop later, repeatably, from
another terminal, or outside the LLM session:

python3 ./looper-output/run-loop.py

For local development, this repository root is the skill root. Edit and test it
here, then install or update the global skill by cloning or copying the repo to
$HOME/.claude/skills/looper and copying commands/looper.md to
$HOME/.claude/commands/looper.md.

If Claude Code says Unknown command: /looper, check both install locations:

• The skill must exist at your real home directory, for example
  C:\Users\<you>\.claude\skills\looper on Windows.
• The slash command must exist at C:\Users\<you>\.claude\commands\looper.md
  on Windows.
• If you see a literal folder named ~ inside your project, your shell did not
  expand ~; rerun the installer or manual PowerShell commands above.

What Looper writes on your machine

Looper is transparent about its footprint. Outside of the loop folders you ask
it to scaffold, it touches exactly four locations:

• ~/.claude/skills/looper — the skill itself (a git checkout).
• ~/.claude/commands/looper.md — the /looper slash command.
• ~/.claude/skills/looper/.venv — a private venv with PyYAML for the helper
  compiler.
• ~/.looper/models.json — the model registry written by detect-models /
  register-model. It stores invocation metadata only (command names and
  argv arrays), never API keys or other credentials; auth stays in each CLI's
  own config or keychain.

Uninstall

Windows PowerShell:

Remove-Item -Recurse -Force "$env:USERPROFILE\.claude\skills\looper"
Remove-Item -Force "$env:USERPROFILE\.claude\commands\looper.md"
Remove-Item -Recurse -Force "$env:USERPROFILE\.looper"  # optional: model registry

macOS/Linux:

rm -rf "$HOME/.claude/skills/looper"
rm -f "$HOME/.claude/commands/looper.md"
rm -rf "$HOME/.looper"  # optional: model registry

How it works

1. Goal — you state it; Looper critiques and tightens it.
2. Verification — Looper forces checkable criteria, classified as programmatic (a command returns pass/fail), judge (a model scores a rubric), or human (you sign off).
3. Host model — pick the model that drives the loop.
4. Council — add a reviewer (notes) or judge (verdict); Looper recommends a different model family than the host and explains why.
5. Gates & control — confirm where review happens, revision and iteration caps, no-progress signals, budget limits, human checkpoints, and execution boundaries. Looper won't emit a loop with no termination guard.
6. Confirm — review the loop as an ASCII flow preview.
7. Run or emit — Looper writes RUN_IN_SESSION.md, loop.yaml, loop.resolved.json, run-loop.py, an empty workspace, and a README. The default is to offer to run the loop in the current session; the Python runner is there for external control.

A council sends your project context to another model's CLI. Looper makes that explicit, applies default redactions, lets you scope what's sent, and asks for consent before the first cross-vendor send. Pick a local model (e.g. via ollama) to keep the council in-house.

License

MIT © Kevin Simback