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Crack

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ethanplusai
crack

Lead substantial Codex builds with one configured implementation worker and lead acceptance. Use when the user requests codex-on-crack, names a model to delegate work to, asks to run subagents through Claude, or requests a lead/worker split. Keep host-only work direct; honor explicit external model choices. Workers must not invoke this orchestrator.

Overview

Publisherethanplusai
Repositorycodex-on-crack
Skill namecrack
Stars
752
Forks
73
Bundled files
52
LicenseMIT
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.

  • 52 bundled files

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

  • Open source

    Published by ethanplusai on GitHub. Read the source before you install it.

Installation

Install the Crack 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.
    https://github.com/ethanplusai/codex-on-crack/tree/main/plugins/codex-on-crack/skills/crack
  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/ethanplusai/codex-on-crack.git /tmp/codex-on-crack
mkdir -p .claude/skills
cp -r /tmp/codex-on-crack/plugins/codex-on-crack/skills/crack .claude/skills/crack
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Crack 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 Crack 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 Crack 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.

Scope, delegate, accept

Keep the user's selected lead model, effort, permissions, and provider. This skill does not change the composer model. Native workers must be configured and available through the host's actual delegation tools. Claude sessions use the explicit adapter below. No hidden model substitution, provider setup, unapproved coding CLI, or automatic council of reviewers.

Resolve the user's model request

The user names a model and a job, not an execution backend. Honor existing route preferences. For native models, use configured roles and actual host availability. For an explicit Opus 5.5 request, including “run subagents through Opus 5.5”, read the external lead reference and check that adapter's readiness before rejecting it as a missing native role. The user does not need to say “Claude subscription” or “external lead”.

Use the adapter's solo mode when Opus does the implementation, and external-lead when it plans and reviews work assigned to a native worker. An explicit model request authorizes that choice within the task's existing scope and budget; it does not authorize new account setup, an ambiguous billing route, or broader tool permissions. If multiple configured routes match and no preference resolves them, ask which route. If unavailable, report the exact gap. Do not invent support for another Claude model or silently substitute one.

Describe the resolved arrangement briefly, then proceed under existing approval. External Claude sessions are not native Codex child agents; use the adapter's lifecycle and usage records. Do not imply the composer changed or that Claude inherited Codex tools.

Prepare one useful bundle

Reuse the user's existing requirements and plans. Establish the outcome, acceptance checks, consequential design choices, and permitted scope. A short brief usually suffices. The lead owns architecture, material risk, and final acceptance; the worker owns in-scope discovery, implementation, tests, debugging, and verification with its available tools. Handle host-only work directly; explicit external model choices still use their resolved route.

For native delegation, run node scripts/doctor.mjs using the session's home/profile. It is a static check, not proof of serving identity. Reuse unchanged verified setup evidence. If roles need configuration, use $codex-on-crack:crack-setup. Do not fall back to an arbitrary default agent when readiness fails.

Choose a configured worker

Honor an explicitly selected role/model. With several approved roles, use node scripts/select.mjs --task implementation (or ui, review, research, tests); add --role NAME, --vision, or --min-context N when appropriate. Read model selection for selection semantics. The selector explains capability filters and user-declared task preferences; it does not know which model is universally best. Resolve needs-choice with the user rather than inventing a ranking. Never silently change the lead or effort.

Dispatch the selected exact native role with a compact brief: objective, acceptance, non-goals, workspace, existing edits, allowed paths, necessary interfaces, verification, and stop conditions. Include only needed context. One writer is the default. A named role does not create a worktree or an extra sandbox. Use the host's actual tool schema; don't invent delegation commands.

Let the worker finish; review once

Use native completion/wait mechanisms. Do not poll a healthy worker, duplicate its discovery, or edit its owned files. Continue only independent useful work. On completion, close/mark the child done as the host requires.

Review the actual changes, including untracked files, against the pre-dispatch workspace and acceptance. Check specification and quality/security together. Reuse trustworthy test evidence; independently verify concrete gaps or risks. Send one consolidated correction request to the same worker when needed. If a correction fails, diagnose the contract, environment, or capability before spending again. A configured fallback still needs user-authorized model/provider scope and an explained reason; it is never an automatic retry loop.

Report what works, the validation, unresolved limits, and observed routing. Do not auto-commit, push, publish, or change production. Keep checkpoints only at meaningful resumable boundaries; the optional template is available for long work. Planning and parallel-work helpers remain optional.

Record build outcomes only when requested

Use build records for scripts/trial.mjs. Ask the user to judge usability and rework; don't fill in their verdict for them. Keep intended model choices separate from observed session metadata. Missing usage is unknown. Never infer allowance savings from raw tokens or task counts.

Optional external project lead

When the user explicitly chooses Opus 5.5 or Claude Code for delegated work, read the external lead reference. Use the existing official subscription login; do not activate this route for unrelated requests: the host validates a structured request, keeps the selected Codex lead unchanged, and never executes lead-authored commands or signals a pid file. In external-lead mode the lead plans and accepts while a native worker implements; in solo mode the lead implements and the host only mediates scoped tools. Keep small host-only work direct. An explicit request for Opus implementation uses the adapter even though its mode is called solo.

To watch a host session, worker sessions, and external-lead runs in one local view, read the replay reference.

Keep the build visible

At the start of an orchestration workflow, if the optional panel plugin is available and the user has not declined a visual view, call its crack_panel tool once to open the embedded Codex on Crack view. Prefer that tool over launching a localhost browser. Reuse an already open view. The host decides where it opens; this is a workflow action, not a promise to open on every new chat.

Check the returned registration status. If this project or run is absent, say so and follow the panel skill's configuration guidance under the user's scope. Before substantial research or implementation, connect this host session using its exact JSONL path and the panel skill's connect command. Use the current thread id to locate only its matching filename; do not inspect unrelated logs. Confirm that crack_panel_status includes the host session. Add each native worker's exact session path and each external adapter run as it starts. If the host log cannot be located, state that coverage gap rather than treating an empty view as connected. Opening the view alone does not track this conversation. Never fill a real run view with demo records. If the plugin is absent, keep working and mention that the panel is an optional installation.

When the user asks to record the build or prepare a replay, load the optional panel's skill and start its recorder before implementation. Register this host session and each worker explicitly. Preserve baseline observations, milestones, available usage and final quality evidence. Report missing coverage; neither opening a panel nor dispatching a worker automatically captures every source.

For visual questions, follow the panel skill's asynchronous-feedback workflow. Reuse the open view and stable question ids. At natural work checkpoints, read current feedback via crack_panel_status and act on new replies within scope. Do not interrupt healthy workers simply to deliver feedback, and do not treat ordinary feedback as approval of a gated build.

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 Crack AI skill do?

Lead substantial Codex builds with one configured implementation worker and lead acceptance. Use when the user requests codex-on-crack, names a model to delegate work to, asks to run subagents through Claude, or requests a lead/worker split. Keep host-only work direct; honor explicit external model choices. Workers must not invoke this orchestrator.

Why use Crack on TypingMind?

Because you install it once and use it with any model. Crack 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 Crack in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ethanplusai/codex-on-crack/tree/main/plugins/codex-on-crack/skills/crack. 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 Crack?

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 Crack?

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

Is the Crack AI skill free?

Yes. It is published on GitHub by ethanplusai under the MIT license. 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.

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