Fresh-context iterations against a PROMPT.md backlog with guardrails — the Ralph Wiggum technique (Geoffrey Huntley). The pattern behind most production harnesses.
The Ralph Wiggum loop — named by Geoffrey Huntley — is the brute-force pattern: the same prompt, a while true, and a fresh context window each pass, usually against a PROMPT.md backlog. It’s how people run Claude Code overnight, and it works better than it has any right to, provided the guardrails exist: an objective exit condition, an iteration cap, and a gate the agent can’t talk its way past. The Ralph-style loops below ship those guardrails as part of the prompt, not as an exercise for the reader. New to the technique? The ralph-wiggum-loop guide covers where it came from and when to use it over /loop or /goal; the agent-loop-safety guide covers what keeps an overnight loop from becoming an overnight bill.
# dare-to-be-stupid — Design (v2, refined) > A Claude Code plugin. One command, /dare , hands an idea or PRD to an autonomous > loop that authors specs, designs, builds, tests, secures, ships, fixes, and iterates > until the app passes an enterprise-production definition of done — or the budget dies. > > Named for the Weird Al song. The joke is that it runs the Ralph Loop on purpose , > with --dangerously-skip-permissions , and narrates the whole thing in the voice of an > '80s Junkion. Pre-production only. Never points at anything with users. This is v2. It keeps the strong core of the original spec (external reviewer, ratchet, guard hook, Junkion style) and adds the three phases the original left thin relative to the actual goal: PRD authoring, a design phase, and a real enterprise DoD including security, CI, docs/observability, and design quality (with quality plugins auto-installed). --- ## 0. The premise, in one paragraph The User builds documentation-first: spec → system docs → API contracts → CLAUDE.md → code. dare-to-be-stupid is the deliberate inverse, packaged as comedy that also solves two real engineering problems. It is a real build , not a joke ar
# AGENTS.md - Codex Ralph Vault Loop ## Mission codex-ralph-vault-loop is a Codex App/CLI native orchestration overlay for multi-agent engineering work. It keeps Codex main as the decision maker, uses external models only through MCP tools, verifies work through gates, and stores durable memory in the vault layer. ## Core Rules - Codex main decides. The primary Codex session owns final decisions, edits, synthesis, safety, and verification. - External models advise. Z.ai, MiniMax, and other non-OpenAI systems provide analysis or worker output only through MCP tools. - Gates verify. Tests, lint, security checks, scorecards, and migration checkpoints decide whether a phase can pass. - Vault remembers. Durable memory belongs in the approved Ralph/Codex memory paths, not in ad hoc repo files. - Do not bypass critical hooks. If prettier , gitleaks , semgrep , or pre-commit are missing from PATH , use the local machine binaries when present, install only with approval, or stop and report the blocker; do not use --no-verify to skip security or formatting gates unless the user explicitly orders that exact bypass. - Do not merge or close a PR until review feedback and automated Cap the run at 25 iterations; leave remaining work for the next session.
/ralph-loop "Read the brief at /redesign/briefs/clients-brief.md. Compare the current implementation to the brief's requirements (every section of the brief, including Recipe Context and Implementation Notes). Apply one focused improvement. Re-check against the brief. If all brief requirements are met, output <promise PAGE-POLISH-COMPLETE</promise ." --max-iterations 8 --completion-promise "PAGE-POLISH-COMPLETE
The four-settings loop template: a separate verifier model that never shares context with the writer, a hard stop rule, a state file re-read each cycle, and worktree isolation. Point it at a checkable backlog and let it run overnight.
GOAL: every test in [/tests/TARGET] passes, lint is clean, zero type errors.
EACH CYCLE:
1. run the suite, read every failure
2. pick the single highest-impact failure
3. write the smallest change that fixes it
4. re-run tests + lint + type check
VERIFY: a separate model instance checks the goal — never the writer. Verifier prompt: "You are a verifier. You did not write this code. GOAL: <the exact goal string>. Given the diff and the test output, answer ONLY: PASS — every condition in GOAL is objectively met, with evidence, or FAIL: <the specific condition not met, and the evidence>. Do not fix anything. If unsure, FAIL."
STOP WHEN: verify passes, OR after 10 iterations, OR $5 spent, OR no progress in 2 attempts.
ON BLOCKER: log it, skip to the next item, never halt the whole loop.
STATE: append done / failed / next to a state file, re-read it at the top of every cycle.
ISOLATION: one git worktree per subagent.
Email is the missing tool in your harness. ConnectMyEmail gives Claude Code and Codex a clean MCP into Gmail, Outlook, iCloud and IMAP — triage, drafts, follow-ups, on a loop.
--- description: Run the Ralph Wiggum loop for a spec (Claude Code) --- Use this command to run an autonomous Ralph loop for a spec: /ralph-loop:ralph-loop "Implement spec {spec-name} from specs/{spec-name}/spec.md. Complete ALL Completion Signal requirements. Output <promise>DONE</promise> when complete." --completion-promise "DONE" --max-iterations 30
# Ralph  Ralph is a minimal, file‑based agent loop for autonomous coding. Each iteration starts fresh, reads the same on‑disk state, and commits work for one story at a time. ## How it works Ralph treats files and git as memory, not the model context: - PRD (JSON) defines stories, gates, and status - Loop executes one story per iteration - State persists in .ralph/  ## Global CLI (recommended) Install and run Ralph from anywhere: bash npm i -g @iannuttall/ralph ralph prd # launches an interactive prompt ralph build 1 # one Ralph run ### Template hierarchy Ralph will look for templates in this order: 1. .agents/ralph/ in the current project (if present) 2. Bundled defaults shipped with this repo State and logs always go to .ralph/ in the project. ### Install templates into a project (optional overrides) bash ralph install This creates .agents/ralph/ in the current repo so you can customize prompts and loop behavior. During install, you’ll be asked if you want to add the required skills. ### Install required skills (optional) bash ralph install --skills You’ll be prom Cap the run at 25 iterations; leave remaining work for the next session.
A fill-in-the-brackets charter that turns any pile of small tasks into a self-checking loop: where the work is, how to check each item with evidence, a needs-me escalation list, a state file, and a per-run stop.
You are running as a loop, not answering one prompt. Here is your charter.
GOAL: [Describe the finished state in one or two sentences. Be specific about what DONE looks like, and make it measurable.]
WHERE THE WORK IS: [e.g. "Scan the /pages folder for files with old pricing" or "Read TODO.md and treat each unchecked box as a task."]
HOW TO WORK: Do one item at a time; finish it fully before starting the next. Match the patterns in existing files; do not invent new ones. If an item needs a decision only I can make (spending money, deleting things, emailing a person), stop on that item, add it to a "needs me" list, and move to the next one.
HOW TO CHECK YOURSELF: After each item, prove it is done before you mark it done — run the tests / re-read the file / open the link. Checking means evidence, not confidence. If the check fails, fix it and check again: maximum 3 attempts per item, then log it as blocked and move on.
HOW TO REMEMBER: Keep LOOP-STATE.md. After each item write the item name, status (done / blocked / needs me), what changed, and anything the next run should know. Read this file FIRST every run.
WHEN TO STOP: Stop when every item is done or logged as blocked, or when you have finished [N] items this run. Then report: what got done, what is blocked, what needs my call.
Start by reading LOOP-STATE.md if it exists, then find the work.
# Persistent Task Loop Task: $ARGUMENTS --- ## Loop Protocol You are in a persistent development loop. Work autonomously until the task is 100% complete. ### Each Iteration: 1. Assess - Track subtasks with the task tools (TaskCreate/TaskUpdate — TodoWrite no longer exists) - Check current state: git status , test results - Identify what remains 2. Execute - Do the next step - Follow the auto-loaded MeshForge rules (CLAUDE.md + .claude/rules/security.md ) - Walk .claude/rules/honest failure modes.md over every error path you write - Write tests for new functionality 3. Verify — capture real exit codes; never judge from truncated streams bash python3 scripts/lint.py --all 1>/tmp/lint.log 2>&1; echo LINT EXIT=$? python3 -m pytest tests/ -q 1>/tmp/pytest.log 2>&1; echo TEST EXIT=$? tail -5 /tmp/pytest.log 4. Continue - If not done, loop back to Assess - Mark completed tasks as you go --- ## Exit Conditions ALL must be true: - [ ] Task is 100% complete - [ ] scripts/lint.py --all exits 0 - [ ] All tests pass (exit code 0, not a "passed" line in a truncated stream) - [ ] Changes committed on main (solo workflow — PR/feature-branch flow retired 2026-04-19) - [ ] Pushed: git push origin main (then pull the fleet boxes) --- ## MeshForge Context Key paths: src/ (source) · tests/ · src/gateway/ · src/launcher tui/ · src/utils/ Security rules are auto-loaded from .claude/rules/security.md — don't restate, just follow them (lint + pre-commit hook enforce). --- ## Completion Signal When ALL exit conditions verified: <promise>DONE</promise> Do NOT output the promise until fully verified complete. --- "I'm in danger!" - Ralph Wiggum (but you're not, keep looping
Anthropic's first-party take on the Ralph loop: a Claude Code plugin that runs the iterate-fresh-context pattern with a managed stop and iteration mechanism built in.
Install the `ralph-wiggum` plugin from the anthropics/claude-code repo; it wraps the Ralph loop with a managed stop/iteration mechanism inside Claude Code.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
Refactor src/api/ to use dependency injection, keep all existing tests passing, add tests for the new DI container, and output completion promise when done.
/ralph-loop "Refactor src/api/ to use dependency injection. Keep all existing tests passing. Add tests for new DI container. Output <promise>COMPLETE</promise> when done and all tests pass." --max-iterations 20
The canonical Ralph Wiggum loop by Geoffrey Huntley: a bash while-loop that feeds Claude Code one fresh-context iteration at a time, using the filesystem and git as memory. Run it only in a sandboxed environment with permissions configured — never with permission checks disabled.
`while :; do cat PROMPT.md | claude -p ; done` — PROMPT.md holds the spec + "pick ONE task from the plan, implement, test, commit, exit." Fresh context every iteration; filesystem + git = memory.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Keep changes minimal and never touch files outside the task’s scope.
/ralph-loop "Refactor codebase to use TypeScript. Output COMPLETE when all files converted and tests pass." --completion-promise "COMPLETE" --max-iterations 100
Split a large mechanical job into 2–5 independent Codex lanes, each isolated in its own worktree with a frozen acceptance bar and binding judge, then block the final merge behind a full integration judge.
Dispatch a parallel Codex legion for a large mechanical job. Split the work into 2–5 genuinely independent lanes, one lane per piece.
First announce a muster table with:
- each lane,
- the exact files that lane may touch,
- the frozen acceptance check for that lane.
Do not proceed until I approve the split.
Before dispatch, the orchestrator must freeze and record each lane’s acceptance bar. After dispatch, each worker treats `.git` as read-only.
Each lane must run in its own git worktree with:
- a frozen acceptance bar recorded before code changes,
- a strictly disjoint may-touch manifest,
- its own sandbox,
- read-only `.git` state for the worker.
If any lane’s file footprint overlaps another lane, refuse the split and serialize the work instead.
When a lane finishes, run a fresh-context judge against that lane’s frozen bar. The judge must return binding PASS or FAIL. Allow at most 2 retries per lane; stop after 2 failed attempts, then escalate loudly.
Merge lanes in a fixed order. After merging, require a mandatory integration judge that reruns the full test suite across the combined result. Do not commit or merge unless the integration judge returns PASS.
Hard cap: 5 workers. If there are more than 5 pieces, run later waves. Never merge without the integration judge.
A Ralph-style loop that writes its own rules: when a check fails the same way twice, the failure pattern gets appended to a guardrails file that every later iteration reads first.
/loop read .ralph/guardrails.md before doing anything, then run the full check suite and fix the first failure; if a check fails twice with the same error, append the failure pattern and a one-line rule for avoiding it to .ralph/guardrails.md before retrying; stop when all checks pass or after 15 turns
Email is the missing tool in your harness. ConnectMyEmail gives Claude Code and Codex a clean MCP into Gmail, Outlook, iCloud and IMAP — triage, drafts, follow-ups, on a loop.
The spec-driven maturation of the Ralph loop: each fresh-context iteration reads PLAN.md, implements the highest-priority unchecked item, checks it off, and commits — so the spec file evolves alongside the codebase.
Ralph variant where the loop prompt is "read PLAN.md, pick highest-priority unchecked item, implement, check it off, commit, exit" — spec file evolves with the codebase.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Keep changes minimal and never touch files outside the task’s scope.
praetor is a Claude Code plugin that runs a plan → freeze acceptance bar → dispatch → independent fresh-context judge → resolve loop. Claude plans and judges, Codex executes; a FAIL from the judge cannot be overridden, with at most 2 retries before a loud takeover.
Plan the task and freeze the acceptance criteria in .codex/ACCEPTANCE.md before any work begins. Isolate on a throwaway branch, write a self-contained brief, then dispatch execution to Codex. When Codex finishes, spawn a fresh-context independent judge that runs every check in the frozen bar against the uncommitted working tree and returns a binding PASS or FAIL — a FAIL cannot be overridden. The judge never fixes anything and commits nothing; it touches manifest paths only. Resolve with at most 2 retries; on continued failure, hand back with a loud takeover. Commit only after the judge passes, then clean up and write the ledger. Iron laws: frozen bar before dispatch, binding judge, max 2 retries then loud takeover.
The canonical Ralph loop: each iteration starts fresh, reads the PRD and guardrails, ships exactly one backlog item end-to-end, and records what it learned.
/loop start each iteration with fresh context: read PROMPT.md, prd.json, and .ralph/guardrails.md; pick the single highest-priority item in prd.json not marked done, implement it with tests, run the full check suite, commit and mark it done only if green; if blocked or a check fails twice the same way, append the lesson to .ralph/guardrails.md and move on; stop when every item is done or after 30 turns
Iterate over a prioritized list of untested modules with fresh context each pass, writing real behavioral tests for one module at a time and banking lessons in a guardrails file.
/loop each iteration with fresh context: read .ralph/test-backlog.json and .ralph/guardrails.md, pick the top unfinished module, write behavioral tests for its public API (no snapshot-only tests), run the suite, and mark the module done only when its tests pass and coverage for it exceeds 80%; append any discovered testing gotcha (fixtures, mocking rules, async traps) to .ralph/guardrails.md; stop when the backlog is empty or after 25 turns
A design framework for AI agent loops built on three questions — when should it run (schedule), what does done mean (goal), and who does the isolated pieces (subagents) — with worked examples in Claude Code and Codex.
Design framework: choose schedule (when), goal (what done means), subagents (who does isolated pieces) — with worked examples in Claude Code and Codex.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
A self-check protocol embedded in CLAUDE.md that every loop iteration obeys before ending a turn: re-read the goal, diff the changes against it, run the verification command, and state what remains — a ritual that catches drift between iterations.
Preamble rules embedded in CLAUDE.md that every loop iteration obeys: before ending a turn, re-read the goal, diff your changes against it, run the verification command, and explicitly state what remains — a self-check ritual that catches drift between iterations. (Community template descended from Andrej Karpathy's circulated CLAUDE.md rules.)
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Keep changes minimal and never touch files outside the task’s scope.
Runs Claude Code/Codex as an autonomous multi-role dev team — planner → reviewer ↔ fixer → worker — across all of a user's GitHub repos, entirely driven by issue labels. Each loop runs in its own git worktree so multiple repos/issues proceed in parallel without collisions.
Register a repo with looper, then label an issue `looper:plan` and assign it to yourself. The planner reads the issue, explores the repo, drafts a spec, critiques and revises it, and opens a spec PR labeled `looper:spec-reviewing`. A reviewer re-reads the PR on every commit and posts inline review threads; a fixer pulls those threads, addresses them in its own worktree, and pushes, ping-ponging with the reviewer until every thread is resolved. Once labeled `looper:spec-ready`, a worker implements the spec, runs checks, and iterates on its own output until checks pass and the PR is ready for human review and merge. Every phase transition is gated on a GitHub label via `looperd`, so a human can pause or take over at any boundary.
Document one undocumented public module per fresh-context iteration, verifying every code sample compiles and accumulating style rules in guardrails so the docs read like one author wrote them.
/loop fresh context each iteration: read docs-backlog.json, docs/STYLE.md, and .ralph/guardrails.md; pick the top undocumented module, write its reference page with a runnable example, execute the example to prove it works, and mark the module done; add any style or structure decision to .ralph/guardrails.md; stop when the backlog is empty or after 20 turns
A cost-safety pattern that pairs every overnight loop with a second, dumber loop whose only job is stopping the first: spend alerts, a hard iteration cap, and a cron check that kills the worker when token burn spikes or the same command keeps repeating.
Pair every overnight loop with a watchdog: spend/usage alert thresholds, a hard `MAX_ITER`, and a cron check that kills the loop process if tokens-per-minute spikes or the same command repeats N times. The watchdog is a second, dumber loop whose only job is stopping the first one. Cap the run at 25 iterations; leave remaining work for the next session.
Email is the missing tool in your harness. ConnectMyEmail gives Claude Code and Codex a clean MCP into Gmail, Outlook, iCloud and IMAP — triage, drafts, follow-ups, on a loop.
A cron-fired skill that finds its own work (failed CI, new issues, recent commits), fans each finding out to an isolated worktree, gates every fix behind an adversarial reviewer that assumes the code is broken, and leaves draft PRs — merging stays human.
# .claude/skills/morning-triage/SKILL.md — fired by cron at 06:00
READ (discovery inputs): CI runs that failed since the last run (gh run list --status failure); issues opened in the last 24h; commits merged since yesterday; the previous ./state/triage.md.
JUDGE: for each candidate decide actionable NOW vs noise; blocks a release → P0; already tracked → skip. Keep only what is worth a worktree today — the loop picks, you don't hand it a list.
WRITE: append findings (finding | source | priority | status) to ./state/triage.md and commit it so tomorrow's run can read it.
HANDOFF: one git worktree per finding (git worktree add ../wt-<slug> -b fix/<slug>), MAX_PARALLEL=3 — capped by how many PRs a human can actually review, not by the machine.
VERIFY: a second subagent as adversarial reviewer — ROLE: adversarial code reviewer. ASSUME this code is BROKEN until proven otherwise; do not praise. CHECK in order: does it run (execute, don't read); run the tests and paste real output; edge cases the author skipped; does behavior match the ticket. VERDICT: PASS only if every check holds, otherwise REJECT with each reason listed. Maximum 3 attempts per finding, then log as blocked.
STOP (red lines): never merge, never delete, never push to main; anything uncertain goes to ./inbox/ for a human, NOT into a PR. Caps set before the first run: per-run timeout 45 minutes, daily budget $20. PRs open as drafts; merging stays human.
Break a large refactor into a JSON backlog of modules and let fresh-context iterations convert one module per pass, with guardrails capturing every pattern decision so the result stays consistent.
/loop fresh context each iteration: read refactor-plan.json and .ralph/guardrails.md, take the next module not marked converted, migrate it to the target pattern described in PROMPT.md, run tests and typecheck, and mark it converted only when green; record every convention decision you make (naming, file layout, error handling) in .ralph/guardrails.md so later modules match earlier ones; stop when all modules are converted or after 30 turns
Codex's native answer to scheduled agent loops: define a prompt plus a schedule in the Codex app and it runs in the cloud on cadence — nightly dependency audits, morning issue triage — with no terminal open. The Codex-side equivalent of Claude Code Routines.
Define an Automation in the Codex app: a prompt + schedule that runs in the cloud on cadence (e.g., nightly dependency audit, morning triage of new issues), no terminal open.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
Framework-agnostic (Claude Code, Codex, OpenClaw, or any LLM agent), markdown-only skill bundle (79+ skills) for running ML research unattended overnight: literature search, idea generation, experiment execution, and cross-model paper review, with a silent-death watchdog and a stall/pivot mechanism so a stuck loop changes approach instead of looping forever on minor variants.
Install the ARIS markdown-only skills, then run the overnight research loop: the agent reviews relevant literature, proposes and critiques experiment ideas, runs GPU experiments, updates a persistent Research Wiki, and has a second model cross-review the draft paper each round. A watchdog checks the state file's modification time and flags the run STALE/MISSING/COMPLETED if it goes silent. An iteration log counts new findings per round; at 2 consecutive stale rounds it forces a structural pivot (reframe and try a new direction), and at 4 it escalates to a human instead of continuing to retry near-identical variants. Cap the run at 25 iterations; leave remaining work for the next session.
# CLAUDE.md — Project Constitution Claude Code reads this file automatically at the start of every run. In a Ralph loop each iteration is a FRESH context, so this file is the only memory that survives. Treat every rule here as non-negotiable. ## What we are building An audit-grade verification gate : an inline API an AI agent calls before it commits a high-stakes output. It returns a verdict AND a signed, tamper- evident audit receipt that a compliance officer can hand to a regulator. The receipt — not the detection — is the product. Full spec: specs/verification-gate.md . ## The Ten Golden Rules (violating any is a failed iteration) 1. One task per loop. Read fix plan.md , pick the single highest-priority unchecked [ ] item, do ONLY that. Do not batch. 2. Tests are law. Never mark a task done unless the full test suite is green. Run it; do not assume. 3. Never weaken a test to pass it. Deleting, skipping, or loosening an assertion to get green is a critical failure. If a test is genuinely wrong, record why in the progress log and stop. 4. Small diffs. If your change touches more than ~3 files or ~150 lines, you have taken too much Cap the run at 25 iterations; leave remaining work for the next session.
First-party Cursor guidance for the iterate-until-green loop, with the key anti-reward-hacking clause: the agent may never modify the tests it is trying to satisfy. Works in Cursor, Claude Code /goal, and Codex.
"Write code that makes these tests pass. Do NOT modify the tests. Keep iterating — run the suite, fix failures, run again — until all tests pass." (paraphrase of Cursor's official agent best-practices guidance)
Anthropic's first-party file-as-memory harness for long-running agents: every fresh-context session recovers state from a progress file and the git log, does one unit of work, updates the file, commits, and exits.
Long-running agent harness: each fresh-context session starts by reading `claude-progress.txt` + git log to recover state, does one unit of work, updates the progress file, commits, exits. Initializer session sets up the file; coder sessions loop.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
A graduated path to unattended Ralph runs: start with a single bounded task, add a PROMPT.md spec file, add verification, and only then remove the human from the loop for overnight builds.
Staged path from basic prompt → PROMPT.md spec → overnight Ralph run: start with a single bounded task, add a spec file, add verification, only then remove the human from the loop. Cap the run at 25 iterations; leave remaining work for the next session.
An outer-planner/inner-coder loop from an official OpenAI recipe: an Agents SDK orchestrator plans and verifies while Codex CLI, wrapped as an MCP server, performs one bounded code change per turn.
Wrap Codex CLI as an MCP server and drive it from an OpenAI Agents SDK orchestrator loop — the outer agent plans/verifies, the inner Codex call does one bounded code change per turn.
Guardrails: Stop when the goal is verifiably met, or stop after 15 iterations, whichever comes first. Verify each pass by running the relevant tests or checks — self-reported success does not count. Keep changes minimal and never touch files outside the task’s scope.
Email is the missing tool in your harness. ConnectMyEmail gives Claude Code and Codex a clean MCP into Gmail, Outlook, iCloud and IMAP — triage, drafts, follow-ups, on a loop.