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Squid Implement Task

Community
iusztinpaul
squid-implement-task

Implement one task — or a whole list / an approved Tasks Plan — via the inner SWE↔Tester loop, committing each task on PASS. Use when one or more GROOMED tasks are ready to build. To plan a feature first use /squid-plan; for the full end-to-end pipeline use /squid-implement-night.

Overview

Publisheriusztinpaul
Repositorysquid
Skill namesquid-implement-task
Stars
207
Forks
34
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 iusztinpaul on GitHub. Read the source before you install it.

Installation

Install the Squid Implement 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.
    https://github.com/iusztinpaul/squid/tree/main/skills/squid-implement-task
  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/iusztinpaul/squid.git /tmp/squid
mkdir -p .claude/skills
cp -r /tmp/squid/skills/squid-implement-task .claude/skills/squid-implement-task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Squid Implement 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 Squid Implement 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 Squid Implement 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.

Implement Task — inner SWE↔Tester loop (1 or N tasks)

Take a task (or a list of tasks / an approved Tasks Plan) and drive each one through the inner SWE → Tester loop, committing each task once it passes. Runs in whatever working tree it's invoked in — the feature worktree when /squid-implement-night invokes it (pass Working directory: {path} to every agent), your current branch when standalone. No worktrees are created here (that's /squid-plan's job) and no human gates run here (those live in /squid-plan and at the end of /squid-implement-night).

$ARGUMENTS is one of: a single task ref (NNN-slug / #N), a free-form task description, the feature's pending task files (tasks/<NNN>-*.md, status: pending), or several refs. If empty, ask the human what to implement.

You are the orchestrator — a MANAGER, not an implementer. You launch agents, enforce the Tester gate, and commit on green. You do NOT write code, run tests, or review the diff yourself beyond inspection (git diff, git log).

Read AGENTS.md first to confirm the active tracker mode (file or gh) and the project's stack + test commands.

Critical rules:

  • Never rubber-stamp the Tester. Spot-check that each AC marked PASS has real evidence (test name, file:line, command output) and that the e2e adversarial section actually attempted break paths. Re-launch with concrete feedback if not.
  • One agent per task. Never bundle multiple tasks into one agent call.
  • Commit each task on PASS — do NOT push. Pushing, PR creation, acceptance, and review are /squid-review's job.

Step 1 — Resolve the task list

Build an ordered list of tasks from $ARGUMENTS:

  • Tracker ref(s) (NNN-slug / #N) → load each task's tasks/<NNN>-<slug>.md.
  • Approved Tasks Plan → the feature's tasks/<NNN>-*.md files with status: pending, in NNN order (gh mode: the feature's open issues).
  • Free-form description → a single ephemeral task; don't create a task file unless the human asks.
  • Empty → ask the human what to implement.

Surface the resolved task list back in one short block before starting.


Step 2 — Per task, in order: SWE → Tester → commit

For each task in the list, run the loop:

2a. SWE implements

Agent(
  subagent_type="squid:software-engineer",
  prompt="""Implement task {ID}. Read AGENTS.md first. Follow your role definition.
  {Working directory: {path}  — include this line only when orchestrated by /squid-implement-night.}
  Stay on the current feature branch — do NOT create a per-task branch (no `feat/{ID}-…`); if you are on `main`, create ONE `feat/{slug}` branch first per your Branch section. Each task is one commit on that shared feature branch.
  Write code AND tests. Run the project's format-fix + lint-fix + pre-commit + unit-tests until clean.
  DO NOT commit yet — the Tester goes first. Append a SWE log entry (or include it in your final message for ephemeral tasks)."""
)

2b. Tester verifies

Agent(
  subagent_type="squid:tester",
  prompt="""QA task {ID}. Read AGENTS.md first. Follow your role definition — your headline duty is the e2e adversarial pass.
  {Working directory: {path}.}
  SWE summary: {hand-off message}.
  Verify every acceptance criterion with evidence. Append a Tester log entry. Verdict: PASS or FAIL."""
)

2c. Handle the verdict — Fails?

  • FAIL (or a rubber-stamped PASS) → relay concrete feedback to the SWE and re-run 2b on the same task:
    Agent(
      subagent_type="squid:software-engineer",
      prompt="QA failed on task {ID}. {Working directory: {path}.} Concrete feedback: {failed ACs + break-path failures + fixes}. Apply the fixes, re-run the local QA loop, append a log entry. DO NOT commit."
    )
  • PASS (verified) → go to 2d.

Retry cap — Tester FAIL max 5 per task. On the 5th FAIL without a PASS: mark the task blocked, surface USER ACTION REQUIRED with the failing AC + last Tester report, and STOP — do not continue to later tasks (the foundation is broken). The counter resets when the task changes.

2d. Commit the task (on PASS only)

Agent(
  subagent_type="squid:software-engineer",
  prompt="""Tester PASSED task {ID}. {Working directory: {path}.} Commit JUST this task per your Commit section
  (generator, specific files, task reference, file-mode `status: done` + `git mv` to tasks/done/). DO NOT push."""
)

2e. Finished tasks?

  • More tasks remain → next task (back to 2a).
  • All tasks done → STOP; see Output.

Output

Code + tests, committed per task (no push). Report a short summary: tasks done, the commit subject + ref for each, and any task that hit the retry cap. When invoked by /squid-implement-night, hand this summary back so it can proceed to /squid-review.


Notes

  • Rollup / fix tasks handed in by /squid-review (or by a human) are just more tasks — feed them in and the same loop applies.

Frequently asked questions

What does the Squid Implement Task AI skill do?

Implement one task — or a whole list / an approved Tasks Plan — via the inner SWE↔Tester loop, committing each task on PASS. Use when one or more GROOMED tasks are ready to build. To plan a feature first use /squid-plan; for the full end-to-end pipeline use /squid-implement-night.

Why use Squid Implement Task on TypingMind?

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

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

Which AI models can use Squid Implement 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 Squid Implement Task?

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

Is the Squid Implement Task AI skill free?

Yes. It is published on GitHub by iusztinpaul 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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