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Cookbook

Runnable, copy-pasteable loop templates that show what LoopKing is good for beyond "fix this code." Each entry lives in cookbook/ as a self-contained directory: a .loop.md you can launch and a README.md that explains the idea, the flow, and the knobs to turn.

The format in 30 seconds

# Title

goal: One sentence describing the outcome.
max_iterations: 5

## Flow
- build:  command `make build`     -> pass: test,   fail: failed
- test:   command `pytest -q`      -> pass: success, fail: fix
- fix:    prompt  `prompts/fix.md` -> pass: build,   fail: failed
  • command steps route by process exit code (0 = pass).
  • prompt steps hand a prompt file to your coding agent; the outcome routes the same way. Prompt paths resolve relative to the .loop.md and cannot escape its directory.
  • Routes point at another step, or the stop tokens success / failed.
  • Every back-edge counts as one iteration against max_iterations.
  • Don't want to wire a graph by hand? Set mode: loose | in-depth | deterministic plus a check: command and LoopKing generates the planner/coder/reviewer/verifier steps for you.

Run any entry

uv run loop launch cookbook/<entry>/<entry>.loop.md            # dry-run, runs nothing
uv run loop launch cookbook/<entry>/<entry>.loop.md --execute  # real execution
uv run loop compile cookbook/<entry>/<entry>.loop.md           # emit a committable YAML spec

Entries

dependency-upgrade

Upgrade one dependency and prove build + types + tests + audit still pass, or fail so the bump can be reverted. A pure deterministic command pipeline — no agent — and the clearest demonstration that a loop is just auditable, exit-code-routed commands.

flaky-test-hunter

Find and fix the test that fails intermittently, then prove stability by running the suite 25× with randomized ordering. Uses the in-depth preset so the agent fixes and a repeat-run verifier gates — because "passed once" is not "not flaky".

data-contract

Keep an ETL output faithful to its schema contract; when it drifts, the agent repairs the transform (never the contract) and the loop re-validates. A hybrid of deterministic command gates and an agent repair step with result-based routing.

prompt-eval-optimizer

Raise an LLM prompt's offline eval score above a release gate without overfitting. The agent edits the prompt, a deterministic eval harness scores it against a held-out suite, and the loop stops only when the number clears threshold.

Why loops (and not a one-shot prompt)

Every entry shares one shape: act → verify with a real command → route on the result → stop only when the gate passes or the budget runs out. That makes the work auditable (each iteration writes a report), reproducible (the verifier is a deterministic command, not a vibe), and safe to run unattended (dry-run by default, hard max_iterations ceiling).