Grill With Docs Codex logo

Grill With Docs Codex

CommunityPopular
chaseai-yt
grill-with-docs-codex

SUPERSEDED by /claudex-loop (formerly /crucible; its docs-aware mode covers this variant) — prefer that skill unless you explicitly want this one. Two-act plan hardening with living documentation. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan, one question at a time, challenging it against your project's existing domain model and glossary (CONTEXT.md), sharpening fuzzy terms, stress-testing with concrete scenarios, cross-referencing code, and updating CONTEXT.md + ADRs inline as decisions crystallise. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md and OpenAI Codex adversarially reviews it in a read-only sandbox (VERDICT:APPROVED/REVISE), Claude revises and re-submits to the SAME Codex session until APPROVED or a MAX_ROUNDS cap, then you sign off before any code. Use when the user says "/grill-with-docs-codex", "grill me against the docs then have codex review", "stress-test this against our domain model then get a second model on it", or is about to build something high-stakes in a project with established terminology/ADRs and wants alignment, documentation, AND a cross-model sanity check. Builds on Matt Pocock's grill-with-docs (MIT). NOT for reviewing already-written code (use /codex:review) and NOT for trivial changes.

Overview

Publisherchaseai-yt
Repositoryclaudex-loop
Skill namegrill-with-docs-codex
Stars
2.1K
Forks
206
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by chaseai-yt on GitHub. Read the source before you install it.

Installation

Install the Grill With Docs Codex AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chaseai-yt/claudex-loop.git /tmp/claudex-loop
mkdir -p .claude/skills
cp -r /tmp/claudex-loop/legacy/grill-with-docs-codex .claude/skills/grill-with-docs-codex
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grill With Docs Codex in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Grill With Docs Codex on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Grill With Docs Codex is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Grill-with-Docs-Codex — Grill Against Your Domain, Then Get Reviewed

Two acts. Act 1 aligns intent and keeps your living docs honest; Act 2 has a different model attack the result.

  • Act 1 is Matt Pocock's grill-with-docs, used under MIT (see THIRD-PARTY-NOTICES.md). It interrogates you, challenges your plan against CONTEXT.md/ADRs, and updates them inline.
  • Act 2 is the original Codex adversarial review loop — cross-model, read-only, bounded.

You enter at two points: answering the grill, and signing off the converged plan.


ACT 1 — GRILL WITH DOCS (you ↔ Claude)

Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask the questions one at a time, waiting for feedback on each question before continuing.

If a question can be answered by exploring the codebase, explore the codebase instead.

Domain awareness

During codebase exploration, also look for existing documentation:

File structure

Most repos have a single context:

/
├── CONTEXT.md
├── docs/
│   └── adr/
│       ├── 0001-event-sourced-orders.md
│       └── 0002-postgres-for-write-model.md
└── src/

If a CONTEXT-MAP.md exists at the root, the repo has multiple contexts. The map points to where each one lives:

/
├── CONTEXT-MAP.md
├── docs/
│   └── adr/                          ← system-wide decisions
├── src/
│   ├── ordering/
│   │   ├── CONTEXT.md
│   │   └── docs/adr/                 ← context-specific decisions
│   └── billing/
│       ├── CONTEXT.md
│       └── docs/adr/

Create files lazily — only when you have something to write. If no CONTEXT.md exists, create one when the first term is resolved. If no docs/adr/ exists, create it when the first ADR is needed.

During the session

Challenge against the glossary

When the user uses a term that conflicts with the existing language in CONTEXT.md, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y — which is it?"

Sharpen fuzzy language

When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account' — do you mean the Customer or the User? Those are different things."

Discuss concrete scenarios

When domain relationships are being discussed, stress-test them with specific scenarios. Invent scenarios that probe edge cases and force the user to be precise about the boundaries between concepts.

Cross-reference with code

When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code cancels entire Orders, but you just said partial cancellation is possible — which is right?"

Update CONTEXT.md inline

When a term is resolved, update CONTEXT.md right there. Don't batch these up — capture them as they happen. Use the format in CONTEXT-FORMAT.md.

CONTEXT.md should be totally devoid of implementation details. Do not treat CONTEXT.md as a spec, a scratch pad, or a repository for implementation decisions. It is a glossary and nothing else.

Offer ADRs sparingly

Only offer to create an ADR when all three are true:

  1. Hard to reverse — the cost of changing your mind later is meaningful
  2. Surprising without context — a future reader will wonder "why did they do it this way?"
  3. The result of a real trade-off — there were genuine alternatives and you picked one for specific reasons

If any of the three is missing, skip the ADR. Use the format in ADR-FORMAT.md.

Handoff to Act 2

When the decision tree is resolved, the glossary/ADRs are updated, and we're aligned, write the agreed plan to PLAN.md (use the canonical terms from CONTEXT.md), then run Act 2:

markdown
# Plan: <task>
_Locked via grill-with-docs — by Claude + <user>. Terms per CONTEXT.md._

## Goal
<one paragraph, in the project's ubiquitous language>

## Approach
<numbered, concrete steps>

## Key decisions & tradeoffs
<the contestable choices the grill resolved  link any ADRs created>

## Risks / open questions
<anything still open>

## Out of scope
<bounds>

Initialize PLAN-REVIEW-LOG.md:

markdown
# Plan Review Log: <task>
Act 1 (grill-with-docs) complete — plan locked, CONTEXT.md/ADRs updated. MAX_ROUNDS=<n>.

ACT 2 — REVIEW (Claude ↔ Codex)

Hand the locked plan to Codex for adversarial review. Mechanics verified end-to-end (2026-06-04).

Prerequisites

  • codex --version ≥ 0.130 (older CLIs error on the default gpt-5.5 model).
  • Codex authenticated (codex login; ChatGPT account fine). On auth/model error, surface it — don't silently retry.
  • Do NOT pin -m (config default is used; gpt-5.x-codex variants 400 on ChatGPT-account auth).
  • Echo the active model before Round 1 so the user can confirm: read the model line from ~/.codex/config.toml (absent = "CLI default"); state it with the resolved tunables. If the user objects, stop before burning a round.

Tunables (args, else default)

VarDefaultMeaning
MAX_ROUNDS5Hard cap. Loop ALWAYS terminates here.
PLAN_FILEPLAN.mdThe plan from Act 1.
LOG_FILEPLAN-REVIEW-LOG.mdAppend-only argument transcript.

Invoked with e.g. rounds=3 → use it. Echo resolved values first.

Review prompt (each round)

You are an adversarial reviewer for an implementation plan. Be skeptical and specific — your job is to find what breaks, not to be agreeable. Read the plan at PLAN.md (and CONTEXT.md/ADRs for the domain language) and any repo files you need (you are read-only). Identify concrete flaws: security holes, race conditions, missing edge cases, schema conflicts, domain-language mismatches, wrong assumptions, observability gaps, simpler alternatives. For each, give a one-line fix. Do NOT modify any files. End with EXACTLY one line: VERDICT: APPROVED or VERDICT: REVISE.

Round 1 — fresh session (capture thread_id)

bash
codex exec -s read-only --json -o /tmp/codex-verdict.txt "$(cat REVIEW_PROMPT)" \
  < /dev/null 2>/dev/null | grep '"type":"thread.started"'

Parse thread_id from the thread.started line. Critique in /tmp/codex-verdict.txt. No verdict file + no thread.started = failed run (auth/model) → stop, tell the user. 2>/dev/null hides cosmetic MCP/auth noise. < /dev/null is mandatory: codex exec reads stdin in addition to the prompt arg, so under a non-interactive driver (Claude Code's Bash tool, CI, any non-TTY pipeline) it blocks forever waiting on stdin EOF — a silent ~0% CPU hang. The redirect gives it immediate EOF.

Rounds 2..MAX — resume SAME session

bash
# resume REJECTS -s. Force read-only via -c sandbox_mode, or Codex inherits
# config.toml (possibly danger-full-access) and could WRITE files. Critical
# safety line — verified 2026-06-04.
codex exec resume "$THREAD_ID" -c sandbox_mode="read-only" --json \
  -o /tmp/codex-verdict.txt \
  "I revised the plan. Re-review PLAN.md — check prior findings + flag anything new. End with VERDICT: APPROVED or VERDICT: REVISE." \
  < /dev/null 2>/dev/null >/dev/null

The < /dev/null redirect is required on the resume call too — same non-interactive stdin hang as Round 1.

Timeout guard (both rounds): run every codex exec / codex exec resume with a 10-minute ceiling so any future stall fails loud instead of hanging silently. Via Claude Code's Bash tool, pass timeout: 600000 on the tool call (the default 2-minute tool timeout is too short for real reviews and would kill them mid-run). In a plain shell, prefix the command with timeout 600 (Linux / Git Bash) or gtimeout 600 (macOS via coreutils — stock macOS has no timeout). If the ceiling trips, treat it as a failed run: stop and tell the user rather than retrying blind.

Each round

  1. Read verdict file; append ## Round <n> — Codex + critique to LOG_FILE.
  2. Last line verdict: APPROVED → Resolution (converged); REVISE → Claude decides what's worth acting on (final arbiter), revise PLAN_FILE, append ### Claude's response (what changed/rejected + why), increment.
  3. round > MAX_ROUNDS → Resolution (deadlock).

Resolution (you sign off)

  • APPROVED: present final plan + 3-bullet summary of what the two acts improved + round count. Ask: implement now — Codex builds it (/codex-build), Claude builds it, or stop? No code during either act.
  • Deadlock (cap hit, no APPROVED): list unresolved points + Claude's counter-position; hand to user. Don't fake convergence.
  • Act 3 (optional): user picks Codex → invoke the codex-build skill with SPEC_FILE=PLAN.md and the same LOG_FILE. Roles flip: Codex writes with full access, Claude reviews the diff + runs the proof; build rounds append to the same log.

Hard rules

  • Act 1 precedes Act 2. CONTEXT.md stays a glossary only — no implementation details.
  • Codex read-only EVERY round (-s read-only first, -c sandbox_mode="read-only" on resume — resume has no -s). Never writes.
  • Loop ALWAYS terminates at MAX_ROUNDS. Claude is final arbiter on REVISE (reject with logged reason). Code only after sign-off. LOG_FILE is the deliverable.

What NOT to do

  • Don't review already-written code (/codex:review). Don't pin -codex variants on ChatGPT auth. Don't let Codex edit files. Don't skip Act 1.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Grill With Docs Codex AI skill do?

SUPERSEDED by /claudex-loop (formerly /crucible; its docs-aware mode covers this variant) — prefer that skill unless you explicitly want this one. Two-act plan hardening with living documentation. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan, one question at a time, challenging it against your project's existing domain model and glossary (CONTEXT.md), sharpening fuzzy terms, stress-testing with concrete scenarios, cross-referencing code, and updating CONTEXT.md + ADRs inline as decisions crystallise. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md...

Why use Grill With Docs Codex on TypingMind?

Because you install it once and use it with any model. Grill With Docs Codex is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Grill With Docs Codex in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chaseai-yt/claudex-loop/tree/main/legacy/grill-with-docs-codex. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Grill With Docs Codex?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Grill With Docs Codex?

As many as you like. As long as a model supports skills, you can use Grill With Docs Codex with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Grill With Docs Codex AI skill free?

It is published on GitHub by chaseai-yt. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇