Lfg logo

Lfg

OrganizationPopular
EveryInc
lfg

Take a request all the way to done, hands-off, through the right Compound Engineering skills. A code change ends as an open pull request, pushed without stopping. Use only when the user explicitly asks for autonomous end-to-end work or invokes lfg directly. Use ce-plan, ce-work, ce-debug, or ce-commit-push-pr for work the user reviews step by step.

Overview

PublisherEveryInc
Repositorycompound-engineering-plugin
Skill namelfg
Stars
25.1K
Forks
2.1K
Bundled files
10
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.

  • 10 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 Lfg 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/lfg .claude/skills/lfg
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lfg 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 Lfg 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 Lfg 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.

Outcome. The request reaches the end state its shape calls for, produced by the Compound Engineering skill whose job that is, with everything unresolved recorded where the user will see it. Most requests end with that skill's result and no branch. A change to the code is the one shape with more steps of its own: it ends in an open pull request whose URL you hold, with CI decided, after being implemented, simplified, reviewed with the eligible findings applied and the rest recorded, any durable learning captured, committed, and pushed. Merging stays with the user unless they granted it for this run.

Work source. On the routes that change the code, nothing is implemented without a work source verified this run, and only two things qualify: an implementation-ready plan, which ce-work implements; or a fixed return from ce-debug, where the fix is the implementation. The plan is one the session identifies, as references/intake.md defines. Never search the plans directory for a candidate or act on a file you discovered. Never plan from scratch over an existing plan.

Route by what the request is. Match the request to the skill whose job it is; the host's skill list is the catalog. Read references/intake.md before choosing; it alone defines the routes, their precedence, what each child skill is passed, and how each return is read. In short:

  • A plan path, or a plan ce-plan wrote this session, takes the plan route.
  • A concrete report of failing or wrong behavior goes to ce-debug; reproducing it is that skill's job, not intake's.
  • A change that hangs on a judgment the user did not settle goes to ce-pov first, and only a verdict that supports the change continues; a judgment with nothing to build ends at the verdict.
  • Product shape with more than one plausible reading goes to ce-brainstorm when a human is present, and to ce-plan in pipeline mode when not.
  • A request whose result is not a code change goes to the skill that owns that result (ce-explain, ce-prototype, ce-pov, ce-ideate, and so on); that invocation is the whole run: return its result and end.
  • Any other change to the code goes to ce-plan.

When unsure, take the route that asks more of the evidence.

Interaction. Ask the user only through ce-brainstorm, and only when a human is present. Everything else proceeds without waiting: reversible work is done and shown for the user to correct afterward, and only an irreversible action outside what they granted (a merge they did not grant, a force-push, deleting data) stops instead. lfg runs from schedulers, loops, and nested orchestrators. The one other question is the routing disambiguation references/stage-routing.md defines: when the conversation assigns planning or implementation to a model or harness, read that file before routing; it alone carries the carrier strings and the sanitization.

Stop, and say why, when any of these holds:

  • The work source cannot be produced: planning returned blocked, the diagnosis found no safe fix, the fix would be divergent, ce-pov did not support the change, or a stage assignment cannot be passed on.
  • A child return is anything but complete and evidenced.
  • A settled decision is invalidated.
  • A project-defined shipping process falls short.

A stop leaves nothing pushed that was not already pushed.

Resolve every skill named here against the host's available-skills list and invoke that exact entry; some hosts namespace it (compound-engineering:ce-plan). Read references/task-visibility.md before starting: it defines the stage view published through the platform's task-tracking capability, the chat narration, and the completion rule that a step is done only after it ran, a child skill's return resumes the next numbered step in the same turn, and the turn does not end before DONE or a stop.

The run (routes that change the code)

  1. Produce the work source per references/intake.md. On the plan route, read references/plan-brief.md first; it alone defines the settled-decisions brief and the artifact-root rule. Any explicit status: blocked return, including settled-decision-invalidated, stops the run. Blocked status outranks an existing artifact and is never retried. Only absence of both a blocker and a plan file ce-plan reported writing this run invokes ce-plan a second time with the same arguments, reusing the composed brief verbatim; the plan must then pass the readiness check in references/plan-brief.md. On the plan route the plan_model:<alias> carrier rides beside the request when a planning-stage directive resolved. Record the work source for every later step. LFG never launches /goal directly; ce-work owns any goal-mode choice and returns control.

  2. Read references/work-return.md first, then invoke the ce-work skill with mode:return-to-caller <plan-path-from-step-1>. On the defect route this step does not run: ce-debug already implemented and committed, and references/debug-return.md was its gate. Only a valid status: complete may advance; every other status or malformed return stops the pipeline.

  3. Read references/review-followup.md now; it governs steps 3 through 7. Invoke the ce-simplify-code skill on the branch diff; skip only the invocation for a docs-only or roughly sub-10-line change.

  4. Invoke the ce-code-review skill with mode:agent plan:<plan-path-from-step-1>; on the defect route omit plan:. A settled_conflict finding whose evidence shows the settled decision cannot work (infeasible, wrong-thing, or destructive) stops the pipeline as blocked, with the finding reported, before the shipping precondition.

Shipping precondition (every push from step 5 on). Run git remote once. No remote means local-only: make every commit the steps call for, but skip every push, PR create/edit, and CI-watch action, including step 10 in full. That is terminal, not an error.

  1. Apply and persist review fixes as that file defines. Do not proceed to the residual handoff, run browser tests, or output DONE while eligible review fixes remain only in the working tree uncommitted.

  2. Autonomous residual handoff: whenever an unapplied actionable finding, a settled_conflict stamp from step 4, or a proceeded-and-flagged settled_decision_conflicts entry from step 2 exists, record it durably per that file: in the PR body, or in tickets or the DONE report when no PR will exist. Skip only when none of the three exists. Do not output DONE until the residuals are durable. Never block DONE on tracker filing failures once the report states them. Do not prompt the user.

  3. Invoke the ce-compound skill with mode:non-interactive when the run produced durable reasoning the code, tests, and plan do not carry; that file states the full condition. Documentation skipped is success; running here puts the learning in the PR at open.

  4. Invoke the ce-test-browser skill with mode:pipeline.

  5. Read references/shipping.md first; it governs steps 9 through 11. Then invoke the ce-commit-push-pr skill with mode:pipeline branding:on.

  6. Watch the PR to CI-decided with ce-babysit-pr mode:pipeline <pr-url> when an open PR exists, as references/shipping.md decides. Do not reimplement CI-watching here.

  7. Output <promise>DONE</promise> after the close-out in references/shipping.md.

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 Lfg AI skill do?

Take a request all the way to done, hands-off, through the right Compound Engineering skills. A code change ends as an open pull request, pushed without stopping. Use only when the user explicitly asks for autonomous end-to-end work or invokes lfg directly. Use ce-plan, ce-work, ce-debug, or ce-commit-push-pr for work the user reviews step by step.

Why use Lfg on TypingMind?

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

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

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 Lfg?

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

Is the Lfg 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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