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Devin Cli

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scarletkc
devin-cli

Delegate bounded coding tasks to the local Devin CLI over ACP from a supervising agent such as Claude Code or Codex. Use when the user asks to hand work to Devin, when an independent implementation, port, review, or investigation can run in parallel with your own work, or when an earlier Devin session needs a follow-up. Includes a blocking run helper with single-use permission answers, a deny policy, progress summaries, and session resume.

Overview

Publisherscarletkc
Repositoryagents
Skill namedevin-cli
Stars
225
Forks
12
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Devin Cli 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/scarletkc/agents/tree/main/skills/devin-cli
  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/scarletkc/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/devin-cli .claude/skills/devin-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devin Cli 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 Devin Cli 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 Devin Cli 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.

Devin CLI

Devin CLI is a separate coding agent on the same machine. devin acp speaks the Agent Client Protocol over stdio; the bundled helper scripts/devin_acp.py drives one turn, answers permission requests from a local policy, and records everything in a run directory. It uses only the Python standard library (3.10+). Resolve the script relative to this installed skill, not the project being worked on.

Devin must already be installed and signed in. Signing in opens a browser, so the user does it with devin auth login; the helper never authenticates and never changes Devin's configuration. If devin is missing, signed out, or out of quota, report that and the work that remains instead of quietly doing the task yourself.

Run a task

sh
python <script> run --cwd <absolute-workspace> --prompt-file <brief.md> [--run-dir <dir>]
python <script> progress --run-dir <dir>
python <script> run --cwd <absolute-workspace> --resume <session_id> --prompt-file <follow-up.md>

run blocks until Devin ends its turn, so start it with your own background facility (a background shell command, nohup ... &, Start-Process) when you have other work. It prints JSON lines: started with the run directory, session with Devin's session ID, and finished with the summary. It exits 0 only when the turn ended normally. Each run gets its own owner-only run directory holding final.md (Devin's final message), summary.json, events.jsonl, and devin.stderr.log; run refuses a directory that already holds a run. The files contain the brief and Devin's output, so keep them out of shared or tracked locations. --help lists every flag; references/protocol.md covers the ACP details.

Write the brief

Devin sees none of your conversation. A brief that works on the first try states, in this order:

  1. The goal and why it matters, in a paragraph.
  2. The current state: what already exists, what is committed, where.
  3. Files to read first, including the project's conventions.
  4. The environment: absolute paths to interpreters and tools, environment variables, and the exact commands that build, test, and validate.
  5. Scope: the files or modules this task owns, and what other workers own.
  6. Rules: leave changes uncommitted; no branch, stash, reset, or push; the directories it must not touch.
  7. Done means: commands that must pass and numbers that must hold (for example, the test count must not drop).
  8. What the final message must contain: files changed, counts, every intentional deviation, and anything left unfinished.

Use a UTF-8 prompt file rather than shell-quoted text. Size each task so its "done" can be checked in one pass; split a large port into steps whose acceptance does not depend on work still in flight.

Isolate and integrate

  • Give every writing task its own git worktree, created from a commit that contains what Devin needs. If the base has uncommitted work, copy it in (git ls-files -m -o --exclude-standard lists it; in Git Bash on Windows pass --force-local to tar so C: paths are not read as remote hosts).
  • Never let two writers edit the same files. Parallel tasks get explicit ownership lists in their briefs, and a module both need is ported first, on its own.
  • Devin leaves its changes uncommitted. Review them, copy them into the integration tree, and commit there. Files changed on both sides need a manual merge.
  • Restore or discard only named paths. git checkout -- . or git reset --hard in a tree with other uncommitted work destroys it.
  • Remove the worktree and its branch after integrating.

Permissions

The helper answers each request with a single-use option: allow_once or reject_once. It never selects allow_session, allow_always, allow_always_global, or switch_bypass, because those persist in Devin's configuration or change the session's mode. By default it rejects git operations that rewrite history or switch branches and worktrees, publishing through gh, and edit, delete, or move requests whose target is outside --cwd or missing. Adjust with --deny <regex>, --no-default-deny, and --allow-write-root <dir>.

Shell commands are matched only against the deny patterns; a command can write anywhere, so no path check applies to it. For a task that must not change anything, pass --read-only: it rejects every edit, delete, move, and shell-command request and starts the session in ask mode (plan is also accepted).

The policy only sees what Devin asks about. In accept-edits, Devin's usual default, it applies workspace edits and runs read-only tools without asking. Pick the mode for the job with --mode: ask answers without editing, plan plans first, smart auto-approves more, and bypass approves everything. Use bypass only when the user explicitly authorizes it. None of this is an OS sandbox; isolation comes from the worktree.

permissions_denied in the summary lists what Devin tried outside the policy. Read it: a denial usually means the brief left a gap.

Model and effort

--model takes the names devin models list shows; many include the effort as a suffix (for example -high). Without it, Devin's configured default applies. The summary's config reports the effective model, thought_level, and speed; confirm them after the run. Use --agent-type review for Devin's code-review agent, which reads and runs shell commands but does not edit.

Monitor

progress reports the tool-call count, recent tool titles, the latest plan, denials, failed tools, the length of the answer streamed so far, seconds_since_last_event, and the summary once finished. Large tasks routinely run for an hour or more; judge a stall by seconds_since_last_event, not by elapsed time. --timeout (default 3600 seconds) cancels the turn; if Devin does not acknowledge, the helper stops its process tree. Cancelling does not undo edits already made.

Follow-ups

run --resume <session_id> continues the same conversation: Devin reloads its history and final.md contains only the new turn. Prefer it for corrections and the next step of the same task; it is cheaper and more accurate than a new brief. The user can also open the session in Devin's own interface with devin --resume <session_id>. devin list is an interactive picker; do not call it from automation.

Verify before accepting

completed means Devin ended its turn, not that the work is right. Run the "done" checks yourself in the worktree, read the diff, look for files outside the task's scope, and compare the final message's claims (counts, files) with what is there. When a test suite is claimed to catch regressions, break one input on purpose, confirm the suite fails, and restore it. Report what Devin produced and what you verified.

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 Devin Cli AI skill do?

Delegate bounded coding tasks to the local Devin CLI over ACP from a supervising agent such as Claude Code or Codex. Use when the user asks to hand work to Devin, when an independent implementation, port, review, or investigation can run in parallel with your own work, or when an earlier Devin session needs a follow-up. Includes a blocking run helper with single-use permission answers, a deny policy, progress summaries, and session resume.

Why use Devin Cli on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/scarletkc/agents/tree/main/skills/devin-cli. 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 Devin Cli?

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 Devin Cli?

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

Is the Devin Cli AI skill free?

Yes. It is published on GitHub by scarletkc under the Apache-2.0 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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