Code Task logo

Code Task

OrganizationPopular
TokenRhythm
code-task

PREFERRED way to change code in a REAL repository: fix a GitHub issue, fix a bug, add/implement a function or feature, or make any edit to a project the user names by a filesystem path (e.g. /tmp/foo, ~/code/bar) or a git URL. Clones the repo, runs an OpenSquilla agent on the host to make the change on a task branch, then independently VERIFIES it with a red→green→regression test loop and reports a structured result. STRONGLY prefer this over hand-editing the user's files yourself in this session: editing files directly skips the isolation and the runner-verified red→green proof, so it is not equivalent. Use it whenever the request names a real on-disk repo/path or a repo URL and asks to fix/add/implement/change code. Examples: 'fix issue 412 in github.com/acme/widgets', '给 /tmp/calc 加个 average 函数', '帮我改一下 ~/proj 里的 X', 'implement CSV export in my project'. Docker-free host execution; treat the target repo as TRUSTED. GitHub issue mode needs the `gh` CLI. For self-contained, TESTABLE code from scratch when NO repo is named (e.g. 'write a function that maps A-Z to pitches'), use `--verification-mode scratch` with no --repo: it scaffolds a throwaway project, writes the code plus a test, and verifies it green. Only truly trivial one-liners or conceptual/non-testable questions are answered inline.

Overview

PublisherTokenRhythm
Repositoryopensquilla
Skill namecode-task
Stars
7K
Forks
566
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Code Task 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/TokenRhythm/opensquilla.git /tmp/opensquilla
mkdir -p .claude/skills
cp -r /tmp/opensquilla/src/opensquilla/skills/bundled/code-task .claude/skills/code-task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Task 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 Code Task 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 Code Task 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.

code-task

Solve a real-repository coding task end to end: clone the repo to a disposable working directory, run an OpenSquilla agent to make the change on a task branch, then independently verify it with a red→green→regression loop. Host mode (no Docker) in v1.

Use this — do not hand-edit the repo yourself

When the user asks to fix/add/implement/change code in a repository they name by path or URL, route it through opensquilla code-task solve — even if the change looks small enough to do by hand. Editing the files yourself in this session is not equivalent: it skips the disposable clone, the task branch, and (most importantly) the runner-verified red→green→regression proof, so neither you nor the user gets evidence the change actually works. Answer inline (no code-task) ONLY for truly trivial one-liners, pseudocode, or conceptual / non-deterministic questions. For self-contained TESTABLE code from scratch with no repo named, use code-task solve --task "..." --verification-mode scratch (no --repo): it writes the code plus a test and verifies it green-only (no red/regression -- there is nothing pre-existing to regress). When a real repo is named, prefer code-task red-green.

Translating the user's request

The user speaks naturally ("fix issue 412 in github.com/acme/widgets", "add CSV BOM support to my project at ~/code/foo"). Map that to the command:

opensquilla code-task solve --repo <url-or-path> ( --issue N | --task "<text>" | --task-file <path> ) [--yes]

Invocation: do NOT assume a bare opensquilla (or bare python) is on PATH — the gateway commonly runs from an absolute interpreter path. When coding mode is active it injects the EXACT, resolved, PATH-independent command: use ONLY that. Otherwise invoke via an ABSOLUTE interpreter, e.g. /abs/path/python -P -m opensquilla.cli.main code-task solve .... Never pip install OpenSquilla or run an installer to "get" the command; if it cannot be run, stop and report the environment is broken.

  • A GitHub issue--issue N (needs gh; see below).
  • A short request in the message--task "<their request>".
  • A long spec, or pasted from Jira/GitLab/内网 → save it to a file and use --task-file <path>.
  • Pass --repo for changes to an EXISTING repo. Omit it for --verification-mode scratch (from-scratch code) and for a from-scratch --verification-mode build (a brand-new app) — both scaffold their own repo. If the user is already in a local checkout, use that path; otherwise the URL.
  • Pass --yes to skip the interactive trusted-host confirmation (you are acting on the user's behalf), but only after the safety check below.

Before you run — two checks

  1. Trusted repo: code-task runs an agent on the host that may install dependencies and execute the repo's code. It is NOT a sandbox. Only run it against repositories the user trusts. If the repo's provenance is unclear, ask first.
  2. Enough information: you must be able to state the expected behavior change ("what is wrong/missing now, what should be true after"). If the request is too vague to write an acceptance test for, ask the user to clarify BEFORE running — do not burn a run on a guess.
    • Build-from-scratch (--verification-mode build) has no acceptance test. Decide by whether you know WHAT THE APP SHOULD DO, not just its kind. If the request is only a broad app type/goal with no concrete features, target user, or scope (e.g. "make me an English-learning app", "a drawing app"), ask 1-2 focused questions (core features/screens and who it's for), then STOP — do not run code-task until answered. If it already names concrete features, scope, or target users, do NOT ask — build it with sensible defaults and state your assumptions. Never ask about platform/framework/styling. At most 2 questions; never interrogate.

GitHub issue mode needs gh

--issue shells out to the GitHub CLI (gh). If gh is missing or not authenticated, tell the user to gh auth login, or fall back: have them paste the issue text and use --task / --task-file instead. The issue body AND comments are pulled in (comments often hold the repro steps).

While it runs — watch the run dir, not the source repo

code-task clones the --repo source into an isolated run directory and does all its work there. The source repo stays empty until a run finishes and VERIFIES, at which point (build mode, local source) the change is committed back. Therefore:

  • Do NOT judge progress by the source repo's contents, and do NOT conclude the run is "stuck" because the source still looks empty — that is expected.
  • A run takes several minutes. Let it finish: process(action="wait") on the background session. Do NOT kill it, do NOT "clean and retry", and do NOT launch the same task again while one is still running.
  • The run prints its run directory on startup and writes a live <run_dir>/status.json (phase = preparing → agent_running → collecting_change → verifying → completed). Watch that if you want progress.
  • Decide success only from the returned result state and build.installer_path (which points into the run dir, not the source).

Reading the result

--json prints a result object; key fields:

  • state: verified (acceptance test went red→green, no regressions), already_satisfied (the behavior already held on the base commit), not_testable (work done but not expressible as a test), environment_blocked (could not build/test the repo), invalid_acceptance_test (agent produced no valid verification manifest), failed (acceptance not green or a regression appeared).
  • branch, commits, files_changed, diffstat, patch_path.
  • acceptance: each test with beforeafter (e.g. failpass).
  • regression: existing-suite result and new_failures.
  • assumptions: surface these to the user — a wrong assumption means a wrong fix.
  • usage: cost / tokens (aggregated across internal retry attempts).
  • attempts / max_attempts / retry_exhausted: code-task RETRIES internally when its own verification fails -- it re-runs the agent on the SAME prepared repo (no re-clone, no re-explore) with the concrete failure fed back, up to max_attempts. A returned result is FINAL across those internal attempts.
  • relaunch_recommended is always false and final_failure_reason explains a failure: do NOT re-launch the same task yourself on a failed result -- the internal retries are already exhausted. Surface the failure to the user.

What to tell the user

  1. Up front: cloning + dependency install + the agent loop can take several minutes; you'll report when done.
  2. After: report state, what changed (diffstat), the acceptance red→green evidence, any assumptions, the cost, and where the branch/diff lives.
  3. On failed / environment_blocked: quote error / final_failure_reason and point at the agent_stdout.log. Do NOT relaunch the same task yourself -- code-task already retried internally (see retry_exhausted).

Constraints

  • Runs on the gateway host — git, the toolchain, and disk all come from there. Works the same from TUI, Web UI, or any channel.
  • v1 is host-only and always clones fresh (no --in-place). For untrusted repositories, a Docker-isolated backend is planned but not in v1.

Verification modes

code-task solve defaults to --verification-mode red-green: the agent writes acceptance tests, the runner proves red on the base and green on the change, then runs regression.

For building an app or UI from scratch (e.g. an Electron + Vite + React desktop app) there is no red->green test loop. Use --verification-mode build: the runner owns a fixed checklist (npm ci -> npm run build -> npx electron-builder for the HOST OS, with the target pinned — --mac dmg -> .dmg, --win nsis -> .exe, --linux AppImage -> .AppImage) and state=verified means the app actually builds and packages into an installer. Each OS only builds its own installer, so run on each OS (or a CI matrix) to collect all three. The result carries verification_kind=build and build.installer_path(s). Preview/launch is intentionally out of scope (no GUI is run).

For self-contained, testable code when the user has not named a repo, use --verification-mode scratch with --task or --task-file and no --repo. The runner creates an empty git repo, asks the agent to write code plus pytest coverage, and independently reruns the declared acceptance command. This mode is green-only and returns verification_kind=scratch.

Frequently asked questions

What does the Code Task AI skill do?

PREFERRED way to change code in a REAL repository: fix a GitHub issue, fix a bug, add/implement a function or feature, or make any edit to a project the user names by a filesystem path (e.g. /tmp/foo, ~/code/bar) or a git URL. Clones the repo, runs an OpenSquilla agent on the host to make the change on a task branch, then independently VERIFIES it with a red→green→regression test loop and reports a structured result. STRONGLY prefer this over hand-editing the user's files yourself in this session: editing files directly skips the isolation and the runner-verified red→green proof, so it is n...

Why use Code Task on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TokenRhythm/opensquilla/tree/main/src/opensquilla/skills/bundled/code-task. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code Task?

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 Code Task?

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

Is the Code Task AI skill free?

Yes. It is published on GitHub by TokenRhythm 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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