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Ce Setup

OrganizationPopular
EveryInc
ce-setup

Check Compound Engineering health and repo-local config, or scaffold a Compound Pack with `pack:<id>`.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-setup
Stars
25.1K
Forks
2.1K
Bundled files
9
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.

  • 9 bundled files

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

  • Open source

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

Installation

Install the Ce Setup 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/EveryInc/compound-engineering-plugin.git /tmp/compound-engineering-plugin
mkdir -p .claude/skills
cp -r /tmp/compound-engineering-plugin/skills/ce-setup .claude/skills/ce-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Setup 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 Ce Setup 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 Ce Setup 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.

Compound Engineering Setup

Interaction Method

Ask each question below using the host's blocking question tool already in the current tool list (match by capability, not by a host-specific name). Presence in the current tool list is proof the tool exists; never call a user-facing question tool to discover whether it exists. If a matching tool is listed but unloaded, use the host's tool-discovery primitive to load that capability — do not search for another host's tool name. Fall back to a numbered list on the host's user-visible chat surface only when no such tool is in the list or a real question call errors. Never silently skip or auto-configure.

ce-setup is a lightweight health check and repo-local config helper. It does not bulk-install every optional dependency. Missing tools are reported as optional capabilities so the user can install only the workflows they use.

Pack Scaffold

When the invocation names a Compound Pack to add, create, or scaffold (the pack:<id> argument, or the same request in words), read references/pack-scaffold.md from this skill's directory and follow it in place of Phases 1-2 (Diagnose and Fix Repo-Local Issues): it writes the pack and its config entry only after the user approves, runs the health check itself, and reports into Phase 3 (Summary).

Artifact Root Resolution

Every Compound Engineering skill that writes or reads an artifact directory (solutions, plans, ideation, and the other CE-owned trees) resolves its root through the rule below. ce-setup carries the canonical statement and reports the resolved root so an operator can confirm where artifacts land before running other skills.

Resolve the CE artifact root <root> before composing any artifact path.

  • Read docs_root from <repo-root>/.compound-engineering/config.yaml only (<repo-root> = git rev-parse --show-toplevel). Do not read it from config.local.yaml. Unset -> <root> is docs, exactly as before.
  • Validate a set value: a repo-relative directory whose real, symlink-resolved path stays inside the repo and is neither the repo root nor under .git/. Otherwise stop with an error naming docs_root and the value -- never fall back to docs.
  • Use <root> as the sole artifact location: create it if absent, compose each path as <root>/<subdir> with this skill's own subdirectory, and never also read docs.

Phase 1: Diagnose

Step 1: Determine Plugin Version

Detect the installed compound-engineering plugin version by reading the plugin metadata or manifest when the platform exposes it. If the version cannot be determined, skip this step.

If a version is found, pass it to the check script via --version. Otherwise omit the flag.

Step 2: Run the Health Check

Before running the script, display:

text
Compound Engineering -- checking your environment...

Run the bundled check script. Set SKILL_DIR to the absolute directory you loaded this ce-setup SKILL.md from — the Bash tool's CWD is the user's project, not the skill dir, so a bare scripts/ path will not resolve:

bash
SKILL_DIR="<absolute path of the directory containing this SKILL.md>";
if [ -f "$SKILL_DIR/scripts/check-health" ]; then bash "$SKILL_DIR/scripts/check-health" --version VERSION; else echo "Bundled health script not found at $SKILL_DIR/scripts/check-health; run the inline checks from ce-setup instead."; fi

Use the same command without --version VERSION if Step 1 could not determine a version.

If the script is unavailable, run the inline equivalent listed in references/repo-fixes.md.

Display the diagnostic output to the user. Missing optional tools are not setup failures. The health report includes the resolved artifact root and which config layer supplied it (per Artifact Root Resolution above); show that line so the operator can confirm where CE artifacts will be written. Missing config.yaml is a reported absence, not a project issue.

Step 3: Decide Whether Fixes Are Needed

Repo-local fixes the health report names apply only to the checkout that report diagnosed. If Phase 2 will write to a different writable checkout, diagnose that checkout first. Session-level findings such as plugin version and optional tools still come from this session's Phase 1.

After the health report, decide Phase 2 from writable-checkout availability:

  • If this session has a writable git checkout, run Phase 2 locally, including when project_issues is 0. Phase 2 always refreshes the example and always offers to create config.yaml when that file is missing.
  • If this session has no writable checkout, but the user named a repository and the harness exposes a remote repo-work surface with a writable checkout, run Phase 2 on that surface instead and report the remote repo-local fixes in Phase 3.
  • Otherwise skip Phase 2 and go to Phase 3, saying repo-local writes were skipped because no writable checkout is available.

If the report names a legacy Compound Codex tool map, offer to remove it following references/legacy-codex-tool-map.md from this skill's directory. That block lives in the user's Codex home, not the checkout, so the offer stands whether or not Phase 2 runs.

Also remediate these project issues when the report names them:

  • obsolete compound-engineering.local.md
  • .compound-engineering/config.local.yaml exists but is not safely gitignored
  • .compound-engineering/config.example.yaml is missing or outdated
  • the health report marks the ce-work skill implementation engine unavailable or invalid, detects retired scalar routing keys, or reports malformed dormant work_engine_preferences
  • the health report marks docs_root invalid (Invalid docs_root ...) — CE artifacts will not be written until it is fixed

If optional tools are missing, do not offer a bulk install. The diagnostic already printed the relevant install command or project URL. Say: "Install optional tools only for the workflows you use."

Phase 2: Fix Repo-Local Issues

Read references/repo-fixes.md from this skill's directory before making any repo-local change. It carries Steps 4-9: removing the obsolete compound-engineering.local.md, refreshing the example config, offering to create config.yaml, repairing invalid work_engine_preferences and docs_root, the two .gitignore offers, and the agent-instructions offers (a knowledge-store mention, the compounding directive, and the chat-register directive for ce-noslop).

All paths there resolve from the repository root (git rev-parse --show-toplevel), not the current working directory. Maintaining the generated example files is the work Phase 2 does on its own — refreshing config.example.yaml and removing the superseded config.local.example.yaml. Every change to a user-owned file is offered and applied only if the user approves.

Phase 3: Summary

User-runnable invocation rendering. In setup summaries, default to /ce-setup; use $ce-setup only when the active host is Codex or explicitly documents dollar-prefixed skill invocation. On oh-my-pi (omp), use /skill:ce-setup. Render only the invocation as inline code and output one form only.

Display a brief summary:

text
✅ Compound Engineering setup complete

Fixed:     <fixes applied, or none>
Skipped:   <fixes declined, or none>
Optional:  <missing optional tools, or all available>

Run `<rendered invocation>` anytime to re-check.

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 Ce Setup AI skill do?

Check Compound Engineering health and repo-local config, or scaffold a Compound Pack with `pack:<id>`.

Why use Ce Setup on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EveryInc/compound-engineering-plugin/tree/main/skills/ce-setup. 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 Ce Setup?

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 Ce Setup?

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

Is the Ce Setup AI skill free?

Yes. It is published on GitHub by EveryInc 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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