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Init

Community
xiaolai
init

Initialize cc-suite for the current project — sets up the AGENTS.md bridge, registers Codex, Claude, and Antigravity MCP surfaces, and generates a .cc-suite.md config. Skill counterpart to /cc-suite:init.

Overview

Publisherxiaolai
Repositorycc-suite
Skill nameinit
Stars
56
Forks
21
Bundled files
1
LicenseISC
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Init 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/xiaolai/cc-suite/tree/main/skills/cc-suite/init
  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/xiaolai/cc-suite.git /tmp/cc-suite
mkdir -p .claude/skills
cp -r /tmp/cc-suite/skills/cc-suite/init .claude/skills/init
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Init 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 Init 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 Init 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.

Init

Bootstrap cc-suite in the current project.

When to Use

  • Setting up a project for the first time
  • Re-initializing after cloning a repo that already has AGENTS.md
  • Walks through the same setup flow as /cc-suite:init — useful when the agent should drive the steps in conversation rather than via a slash command

Workflow

Step 0: Resolve the plugin root

CLAUDE_PLUGIN_ROOT is set by Claude Code only — in a Codex or Antigravity session it is unset. Resolve the root from the bridged skills symlink (it points at <plugin-root>/skills/cc-suite):

bash
PLUGIN_ROOT="${CLAUDE_PLUGIN_ROOT:-$(dirname "$(dirname "$(readlink -f .claude/skills/cc-suite 2>/dev/null)")")}"
[ -d "${PLUGIN_ROOT}/scripts" ] || echo "! cannot resolve the cc-suite plugin root — ask the user to type /cc-suite:bridge-skills in Claude Code first, or export CLAUDE_PLUGIN_ROOT"

Every command below uses ${PLUGIN_ROOT}. Stop if it could not be resolved.

Step 1: Check for existing config

bash
[ -f .cc-suite.md ] && echo "exists" || echo "missing"

If it exists, read it and ask the user:

  • Show — display the file and stop
  • Regenerate — replace with a fresh config (proceed through questions)
  • Cancel — keep as-is and stop

Step 2: Detect project stack

Run these checks to build the stack description:

bash
[ -f package.json ]                                   && echo "node"
[ -f requirements.txt ] || [ -f pyproject.toml ]      && echo "python"
[ -f go.mod ]                                         && echo "go"
[ -f Cargo.toml ]                                     && echo "rust"
[ -f Gemfile ]                                        && echo "ruby"
[ -f pom.xml ] || [ -f build.gradle ]                 && echo "java"
ls *.csproj *.sln 2>/dev/null                         && echo "dotnet"
[ -f jest.config.js ] || [ -f vitest.config.ts ]      && echo "jest/vitest"
[ -f pytest.ini ] || [ -f conftest.py ]               && echo "pytest"
[ -d src ]  && echo "src/"
[ -d lib ]  && echo "lib/"
[ -d app ]  && echo "app/"

Detect the test command from what the project declares — a framework marker alone does not establish the command:

  • package.json: read scripts.test. Missing or the npm placeholder (Error: no test specified) → unknown. Otherwise pick the runner from the lockfile: pnpm-lock.yaml → pnpm test, yarn.lock → yarn test, bun.lock* → bun run test, else npm test.
  • pytest.ini / conftest.py → pytest
  • go.mod with *_test.go files → go test ./...
  • Cargo.toml → cargo test
  • Gemfile + spec/ → bundle exec rspec
  • Nothing reliable → write unknown rather than guessing.

Step 3: Discover the current Codex default

Run the shared Codex preflight before asking for configuration:

bash
bash "${PLUGIN_ROOT}/scripts/codex-preflight.sh"

Use default_model and the per-model reasoning_efforts (from the models_detail entry matching default_model; top-level reasoning_efforts for older preflight output) to phrase the questions in Step 4. The preflight dynamically prefers the latest available general-purpose model and deprioritizes review-only entries, so they are never the default while a general model exists.

Do not write the resolved default_model slug into the config unless the user explicitly pins it in Step 4 — the config records a policy (latest, or a deliberate pin), not a snapshot. If the preflight cannot discover models, skip the model question and write latest.

Step 4: Ask customization questions (conversational)

Ask the user four questions in sequence (three when preflight failed — skip the model policy question and write latest):

  1. Audit focus — balanced / security-first / performance-first / quality-first (default: balanced)
  2. Audit depth — mini (5 dimensions, faster) / full (9 dimensions) (default: mini)
  3. Model policy — track latest (default; currently resolves to {default_model}, never goes stale) / pin {default_model} (fixed until the user edits .cc-suite.md)
  4. Reasoning effort — offer the levels from the selected per-model reasoning_efforts list (default: high when available, otherwise the highest offered; fall back to high / medium / low when preflight failed or the list is empty)

Step 5: Generate .cc-suite.md

Write .cc-suite.md to the project root:

markdown
# CC-Suite Configuration

Project-specific settings for cc-suite commands.
Generated by `/cc-suite:init`. Edit freely — all fields are optional.

## Project

- **Stack**: {detected stack}
- **Test command**: {detected test command or "unknown"}
- **Source directories**: {detected dirs}

## Defaults

These override the built-in defaults for all commands in this project.
Remove a line to fall back to the built-in default.
`latest` is a policy, not a model name: Codex-delegating commands resolve it to
the newest general-purpose Codex model via preflight. Replace it with a concrete
slug (any entry from the preflight model list) only to pin — pins stop tracking
the catalog.

- **Default model**: {"latest", or the pinned slug if the user chose to pin}
- **Default effort**: {chosen effort}
- **Default audit type**: {chosen audit type}
- **Default sandbox**: workspace-write

## Audit Focus

{chosen focus}

Additional instructions appended to every audit's developer-instructions:

```text
{focus-specific instructions}
```

## Skip Patterns

Files and directories to always skip during audits (glob patterns):

```text
node_modules/
dist/
build/
coverage/
*.min.js
*.bundle.js
*.lock
vendor/
.git/
```

## Project-Specific Instructions

Custom instructions appended to Codex's developer-instructions for every command.

```text
{stack-derived instructions from Step 2 detection, e.g. "This is a {detected stack} project. Follow existing patterns in {detected dirs}." Leave empty when nothing was detected.}
```

Focus-specific instructions:

  • balanced: "Give equal attention to all audit dimensions."
  • security-first: "Prioritize security findings. Flag any auth bypass, injection, data exposure, or cryptographic weakness as Critical regardless of other severity heuristics."
  • performance-first: "Prioritize performance findings. Flag N+1 queries, O(n²) algorithms, memory leaks, and blocking I/O as High regardless of other severity heuristics."
  • quality-first: "Prioritize code quality findings. Flag untested critical paths, high cyclomatic complexity, and DRY violations as High regardless of other severity heuristics."

Step 6: Bridge init

bash
bash "${PLUGIN_ROOT}/scripts/init.sh"

Creates AGENTS.md, CLAUDE.md (@AGENTS.md), Codex scaffolding, the .agents/skills bridge, and the .gitignore block. (The generated .agents/mcp_config.json projection is written later, by bridge_mcp.sh in Step 8.) Antigravity CLI (agy) reads AGENTS.md natively and uses .agents/ workspace assets. It does not create new Gemini-era project scaffolding. Skips each artifact if already correct.

Step 7: Expose skills

bash
bash "${PLUGIN_ROOT}/scripts/bridge_skills.sh"

Step 8: Mirror the project MCP surface

bash
bash "${PLUGIN_ROOT}/scripts/prune_codex_mcp.sh"
bash "${PLUGIN_ROOT}/scripts/bridge_mcp.sh"

prune_codex_mcp.sh removes the dead codex-cli entry an older cc-suite (≤2.0.1) wrote into .mcp.json — it pointed at codex mcp-server, which Codex CLI no longer has. No-op when there is nothing to remove. cc-suite registers no replacement: Claude→Codex delegation runs codex exec through the CLI runner.

The bridge writes the Codex projection and the generated agy workspace MCP projection. It also registers the pinned claude-code server for agy → Claude.

Step 9: Register claude-code MCP server

bash
bash "${PLUGIN_ROOT}/scripts/mcp_claude.sh"

Step 10: Final status

bash
bash "${PLUGIN_ROOT}/scripts/status.sh"

Report:

cc-suite initialized

Bridge artifacts: {status summary}
Claude → Codex:  codex exec via the CLI runner (no MCP registration) ✓
Codex → Claude:  .codex/config.toml has claude-code registered ✓
agy → Claude:    .agents/mcp_config.json has claude-code when the projection is available ✓
Project config:  .cc-suite.md written ({focus}, {depth}, effort={effort})

Next steps:
  Edit AGENTS.md to add project-specific conventions
  Run /cc-suite:audit-fix to test the full cycle
  Type /cc-suite:diagnose in Claude Code (or use the diagnose skill here) to verify health at any time
  Commit AGENTS.md, .cc-suite.md, .mcp.json to share with your team

Example Invocations

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 Init AI skill do?

Initialize cc-suite for the current project — sets up the AGENTS.md bridge, registers Codex, Claude, and Antigravity MCP surfaces, and generates a .cc-suite.md config. Skill counterpart to /cc-suite:init.

Why use Init on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xiaolai/cc-suite/tree/main/skills/cc-suite/init. 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 Init?

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 Init?

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

Is the Init AI skill free?

Yes. It is published on GitHub by xiaolai under the ISC 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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