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Finn Build

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finna
finn-build

Claim the next safe agent-ready issue from Linear, implement it, and open a PR. Use when asked to run Finn-loop's builder, work the approved queue, or fix Finn-loop review feedback. Designed for /loop; one pass does one unit of work.

Overview

Publisherfinna
RepositoryFinn-loop
Skill namefinn-build
Stars
321
Forks
62
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Finn Build 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/finna/Finn-loop/tree/main/skills/finn-build
  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/finna/Finn-loop.git /tmp/Finn-loop
mkdir -p .claude/skills
cp -r /tmp/Finn-loop/skills/finn-build .claude/skills/finn-build
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finn Build 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 Finn Build 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 Finn Build 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.

Finn-loop builder

One pass = one unit of work: fix review feedback on one existing PR, or build one issue end to end. Under /loop, each iteration runs this skill once.

0. Preflight

Before changing Linear, GitHub, branches, or files:

  • Confirm this is the intended GitHub repository and origin is reachable.
  • Detect the repository's default branch with gh repo view --json defaultBranchRef --jq .defaultBranchRef.name; never assume it is main.
  • Require a clean working tree (git status --porcelain must be empty). If it is dirty, report the paths and end the pass. Never stash, reset, overwrite, or commit unrelated work.

1. Review feedback first

List open PRs labeled loop-changes-requested, including their labels:

bash
gh pr list --state open --label loop-changes-requested --json number,title,headRefName,headRefOid,labels,updatedAt,url

Skip every PR carrying needs-human-review; it has left the automated repair queue until a human resolves the escalation.

If any PR remains, choose the least recently updated one. Read its linked Linear issue and latest Finn-loop review of COMMIT_SHA verdict. Check out its branch, fix only the "Must fix before merge" items, run the relevant checks, push, remove loop-changes-requested, and comment with what changed. End this pass.

If a proposed fix would cross an issue non-goal or requires a product decision, do not implement it. Comment the exact conflict, add needs-human-review, remove loop-changes-requested, and end the pass. This prevents the next loop iteration from retrying a decision only a human can make.

2. Pick

Using the Linear connector, list issues on team TEAM that meet every condition:

  • labeled agent-ready
  • unassigned
  • not labeled blocked
  • no unresolved blocker relation

Sort by priority, then oldest first. If the queue is empty, say so and end the pass. Do not invent work and do not pick a blocked issue.

3. Claim (the cooperative lock)

Assign yourself and move the issue to the team's started workflow state (prefer In Progress when available). Claim before reading deeply or writing code. Re-fetch the issue immediately after the update; if it is blocked, assigned to somebody else, or no longer agent-ready, do not work it and return to step 2.

The assignee prevents different people from taking the same issue. It is not an atomic lock between simultaneous sessions authenticated as the same Linear user, so only one builder loop may run per team.

4. Read

Fetch the full issue including comments and relations. Implement only its acceptance criteria. Non-goals are binding. Compare every AC-N against every NG-N before editing. No unrelated changes and no opportunistic refactors.

If an acceptance criterion is ambiguous, conflicts with a non-goal, or depends on an unresolved blocker, go to step 8. Never guess.

5. Build

  • Fetch the latest default branch from origin and create or resume a branch named TEAM-NNN-short-slug, using the issue's real identifier.
  • Implement the acceptance criteria using the repository's existing style, architecture, and naming.
  • Add or update tests when the change affects logic, data flow, permissions, integrations, or user-visible behavior.
  • Preserve behavior outside the issue contract.

6. Verify

Run the project's relevant lint, typecheck, build, and narrowest useful tests. All checks attributable to this change must pass before opening a PR. If a broad check has a pre-existing unrelated failure, run the relevant targeted check, preserve the evidence, and disclose both results in the PR.

Review git diff and git status before shipping. Stop if the diff contains unrelated work or generated secrets.

7. Ship

Push and open a PR with gh pr create. Its description must include:

  • What changed and why
  • Closes TEAM-NNN, using the real Linear issue identifier
  • A scope ledger: one evidence line per AC-N, one preservation line per NG-N, and Other behavior changes: None
  • Numbered manual test steps matching what was actually built
  • Automated checks run and their results
  • Risk: Low / Medium / High

If Other behavior changes: None is not true, stop and get the Linear issue amended before opening the PR.

Comment the PR URL on the Linear issue. Move it to the team's review state if one exists; otherwise leave it in the started state for the Linear-GitHub integration to manage. Never merge and never enable auto-merge. End the pass.

8. Blocked

Comment one specific question a human can answer asynchronously, apply the blocked label, and unassign yourself. Leave agent-ready in place: the pick query explicitly excludes blocked, so the issue safely reappears only after a human answers and removes that label.

Never use "this is unclear" as the question. State the exact decision, the available options, and which acceptance criterion it affects. End the pass so the next iteration can pick different work.

Frequently asked questions

What does the Finn Build AI skill do?

Claim the next safe agent-ready issue from Linear, implement it, and open a PR. Use when asked to run Finn-loop's builder, work the approved queue, or fix Finn-loop review feedback. Designed for /loop; one pass does one unit of work.

Why use Finn Build on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/finna/Finn-loop/tree/main/skills/finn-build. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finn Build?

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 Finn Build?

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

Is the Finn Build AI skill free?

Yes. It is published on GitHub by finna 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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