Ce Work logo

Ce Work

OrganizationPopular
EveryInc
ce-work

Execute a plan or concrete work prompt end-to-end. Use when implementing from a plan document, a spec path, or a clear build request; use ce-debug for open-ended bugs. Use when an outer orchestrator needs implementation and local verification only, without the shipping tail.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namece-work
Stars
25.1K
Forks
2.1K
Bundled files
25
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.

  • 25 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 Work 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-work .claude/skills/ce-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ce Work 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 Work 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 Work 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.

Work Execution Command

Outcome

  • Result: A fully implemented, locally verified change set from a plan, specification, or concrete work prompt.
  • Next consumer: In standalone use, the shipping workflow takes the verified change through review and delivery. In Return-to-Caller Mode, the invoking workflow receives the structured implementation and verification result and owns its remaining gates.
  • Done: Every in-scope task is complete, required verification evidence is recorded, relevant checks pass, and the run reaches either its owned shipping handoff (with a code-review receipt or explicit skip phrase — see Phase 3-4), a complete return result, or an explicit blocker.
  • Intent: Finish the requested feature without renegotiating the plan or transferring canonical integration authority. Workers receive bounded units; the host orchestrator inspects actual changes and owns authoritative verification and canonical commits.

Execution Workflow

Bundled references must be read, never approximated. Resolve each reference or script path named below from this skill's loaded SKILL.md directory, using the full skill path the harness supplied, and never glob the target repository to find a bundled file. Read each reference when you enter the phase it governs; a read made before that phase does not satisfy it, and a reference this file says to read again is read again at its step even when already in context. If the harness does not expose the skill directory, or a required file cannot be read, stop before the action it governs and report which file is missing. Do not reconstruct its rules from memory; report the missing reference instead of continuing natively.

Phase 0: Input Triage

Recovery activation comes first. Before classifying the input as a plan, a path, a blank, or a bare prompt, recognize requests to resume, inspect, reap, or clean up an existing run. Recovery never dispatches a new worker, selects a new route, discovers another plan, reruns completed verification, or enters either shipping path. If the run id is missing, ask for it; never guess one.

Before any other input decision, read references/input-triage.md. It decides source resolution, control tokens, recovery, read-only discovery, plan readiness, non-code routing, blank input, and bare-prompt sizing. Three rules from it hold here:

  • A bare prompt that is Trivial — one or two files, no behavioral change — skips the task list but still resolves its execution engine before writing. A purely mechanical diff also ships without a post-PR watch. When either is uncertain, take the fuller route.
  • A bare prompt that ce-plan already sized in this session is executed, not planned again. A decision the user would weigh is asked as a question, never as a route back to ce-plan or ce-brainstorm.
  • If that reference cannot be read, stop; never treat control tokens or a non-executable artifact as code work.

When triage selects Return-to-Caller Mode, read references/return-to-caller.md immediately and record that it governs how this run ends. If it cannot be read, stop before any mutation; do not fall back to standalone behavior.

Phase 1: Quick Start

  1. Establish the workspace. Before moving branches, editing, dispatching, or committing, read references/workspace-setup.md. It decides the writable checkout, plan clarification, branch placement, the pre-work inventory, already-dirty files, and task setup. Never write without a writable canonical checkout, and never write on the real default branch unless the user explicitly directed that in this session.

    Do not commit or publish anything the user did not offer. When a unit needs a file that was already dirty, standalone mode asks once whether to include or exclude that file. Return-to-Caller Mode neither asks nor edits it; it returns blocked, naming the collision and how to recover.

  2. Resolve the engine, then strategy. After bounded plan intake and task derivation, but before selecting a unit for execution, writing, dispatching, or committing, read references/execution-engines.md and complete its route selection. It applies with or without a typed binding; native execution is eligible only when that reference selects it or exhausts an allowed fallback. The engine choice never changes which reference governs how the run ends.

    If cross-model execution is selected, read references/cross-model-execution.md before any content or authority crosses to the other model. It defines controller initialization, the post-init engine lock, bounded egress, transactions, recovery, and receipts.

    Before choosing inline, serial, or parallel execution, and before dispatching any worker, read references/execution-strategy.md. It decides scheduling, isolation, the packet each worker receives, worker lifecycle, and integration. The host orchestrator keeps authoritative verification and makes the canonical commits.

Phase 2: Execute

Before the first implementation write, including on the Trivial route, read references/implementation-loop.md. It decides how evidence is chosen, verification, when to stop a unit, incremental commits, following existing patterns, continuous testing, where simplification stops, UI work, progress tracking, and settled decisions.

The commit rule from this file stays in force throughout: every implementation commit names only that unit's owned files. A bare git commit can absorb the user's pre-existing index, so it is forbidden.

Phase 3-4: Quality Check and Finishing Work

After the tasks and local verification are complete, standalone mode reads references/shipping-workflow.md before any quality check or delivery. It decides simplification, code-review receipts and fallbacks, leftover findings, final validation, and delivery.

Code-review completion gate (standalone only). Code review must actually happen before shipping. The run is not done, must not call a commit or shipping skill, and must not report that shipping is complete until the shipping reference has recorded either an actual completed ce-code-review receipt or one of its exact authorized skip states. Never substitute a mental self-review or findings already applied earlier. This rule does not apply in Return-to-Caller Mode.

Return-to-Caller Mode

Return-to-Caller Mode performs implementation and local verification only. It must not enter Phase 3-4 or run final simplification, code review, PR creation, CI watching, babysitting, or any other standalone shipping action; the caller owns those steps.

Immediately before emitting the result, read references/return-to-caller.md again. It alone defines the full return result, the check that evidence is complete, the route and model records, recovery semantics, and standalone_shipping_skipped: true. Do not build a complete result from this file.

If that required read fails after planning or implementation created state, preserve every changed file, commit, workspace, and controller record. Return the minimum blocked result from this file: status: blocked, plan_path, run_id when known, changed_state, blockers naming the missing reference, and recovery_path. Do not erase partial state, report success, or fall into the standalone shipping path.

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 Work AI skill do?

Execute a plan or concrete work prompt end-to-end. Use when implementing from a plan document, a spec path, or a clear build request; use ce-debug for open-ended bugs. Use when an outer orchestrator needs implementation and local verification only, without the shipping tail.

Why use Ce Work on TypingMind?

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

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

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 Work?

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

Is the Ce Work 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.

View all

Set up your own AI workspace now

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