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Squid Plan

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iusztinpaul
squid-plan

Turn a raw feature spec into an approved Tasks Plan — grill the spec, have the Product Architect groom draft tasks (+ optional ADR and glossary additions), then run ONE human gate that decides everything touching the repo: tasks + storage, ADR, glossary, worktree, and which build to run.

Overview

Publisheriusztinpaul
Repositorysquid
Skill namesquid-plan
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 Plan 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-plan
  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-plan .claude/skills/squid-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Squid Plan 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 Plan 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 Plan 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.

Plan — feature spec → per-task files (+ optional ADR, + worktree)

Anchor a feature in shared understanding and prior decisions, then produce the artifact /squid-implement-night consumes. Nothing touches the repo until the human gate.

$ARGUMENTS is the raw feature spec — free-form text, a path to a spec file, or a tracker reference.

You are the orchestrator — a MANAGER. You drive the grilling, launch the Product Architect (PA), offer another grilling round and present the final plan, run the single human gate, set up the workspace, write the approved artifacts, and kick off the chosen build. You do NOT groom, write code, or implement anything yourself.

Read AGENTS.md first to confirm the active tracker mode (file → tasks/<NNN>-<slug>.md files; gh → one GitHub Issue per task).

Input: raw feature spec. Output: decided at the human gate — by default one tasks/<NNN>-<slug>.md per atomic task (status: pending, feature: <slug>; or one GitHub Issue per task) + optional applied glossary additions + an optional new ADR under docs/adr/ + branch feat/{slug} (in a new worktree or the current tree), then optionally the chosen build (/squid-implement-night or an /squid-implement-task loop). This is exactly what the downstream pipeline consumes.


Step 0 — Resolve the feature spec

If $ARGUMENTS is empty, ask the human for the feature (free-form, path, or tracker ref). Otherwise resolve it: cat a spec file, load a tracker record, or use free-form text. Surface the resolved spec back in one paragraph.


Step 1 — Grill the spec (Human ↔ /squid-grilling)

Before grooming, sharpen the raw spec with the human. Invoke the squid-grilling skill — interview the human relentlessly, in rounds of up to 5 questions, each with a recommended answer, until scope, edge cases, non-goals, constraints, and any decisions that warrant an ADR are clear. Anchor the questions in what already exists: read docs/adr/ (settled decisions — don't re-open them) and docs/glossary.md (use its terms; flag any the spec uses differently) when present, and use the context7 plugin for authoritative library/API facts. Anything answerable by reading the codebase or those sources — explore instead of asking. The output is a grilled spec; carry it into Step 2.


Step 2 — PA grooms → DRAFTS the plan (no writes yet)

Launch ONE Product Architect. It drafts everything and hands it back as its final message — it writes nothing to disk; the orchestrator writes the approved artifacts into the chosen workspace in Step 5.

Agent(
  subagent_type="squid:product-architect",
  prompt="""Feature-level grooming. Read AGENTS.md first. Follow your feature-grooming role.
  Feature (grilled): {grilled spec from Step 1}.
  Decompose into atomic, independently-shippable tasks, numbered (NNN) in dependency order. For EACH task, draft the
  FULL tasks/<NNN>-<slug>.md content (`feature: {slug}`, `status: pending`) per the tracker-workflow spec.
  Also draft: (a) any new docs/glossary.md terms, and (b) IF the feature warrants non-obvious architectural decisions,
  ONE proposed ADR for the WHOLE feature per your ADR rule.
  DO NOT WRITE ANYTHING TO DISK — hand everything back as drafts; the human approves and the orchestrator writes them.
  Use the context7 plugin for authoritative library/API usage wherever the feature touches an external framework.
  Return: (1) the ordered task files with their full content, (2) the glossary additions (or 'none'), (3) the proposed
  ADR draft (or 'none')."""
)

Verify before the gate: the drafts are atomic, ordered by NNN in dependency order, and each has acceptance criteria. Re-launch the PA with the gap as feedback if not.


Step 3 — Offer another grilling round, then output the final plan (Human ↔ /squid-grilling)

Before /squid-plan moves toward implementation, give the human one explicit chance to sharpen further — then lock and show the final plan.

Ask once with AskUserQuestion: "Another grilling round to sharpen this, or is the plan final?" → More grilling / It's final.

  • More grilling → re-invoke the squid-grilling skill on the points the drafts left open, feed the sharpened spec back to the PA (Step 2), and return here. Repeat until the human picks It's final. Keep it to genuinely-open questions — good: "should deleting an Order cascade to its line-items?"; bad: re-opening a settled choice like "maybe switch datastores after all" (that's a fresh /squid-plan, not another round).
  • It's final → output the final plan in full so the human reads exactly what will be built before anything is written: every task (NNN — title, scope, acceptance criteria, out-of-scope), the glossary additions, and the proposed ADR. This is the human's last look before tasks are created — render it complete, not a teaser.

Why this step: catching a wrong-shaped plan on screen costs one more grilling round; catching it after tasks, a branch, and a build already exist costs the whole downstream pipeline.


Step 4 — HUMAN GATE (blocking)

This is the only gate, and it is mandatory. It decides everything that touches the repo. Recap the decision inputs, then ask:

Feature: {title}
Tasks ({N}):
  1. {NNN-slug} — {title}
  2. ...
Glossary additions: {M new terms, or "none"}
Proposed ADR: {ADR-NNNN "title" — 1-line summary, or "none"}
Task storage (project default): {file → local tasks/*.md | gh → GitHub Issues}   ← from AGENTS.md TRACKER_MODE

Then ask with AskUserQuestion. The decisions come in two back-to-back asks — still ONE gate, with no repo writes between them: Part 2 only matters once the plan is approved, and AskUserQuestion caps at four questions per call.

Part 1 — the plan and its artifacts (ask together, then act on Q1):

  1. Approve the plan? — write these {N} tasks? → Approve / Edit / Cancel
  2. Store the tasks where? — → Local files (tasks/<NNN>-<slug>.md, committed to the repo) / GitHub Issues (one issue per task). Pre-select the project default (AGENTS.md TRACKER_MODE) and mark it Recommended.
  3. Create the ADR? — write the proposed ADR to docs/adr/? → Create / Skip (omit this question entirely if no ADR was proposed)
  4. Update the glossary? — apply the {M} drafted term(s) to docs/glossary.md? → Apply / Skip (omit this question entirely if the PA drafted no glossary additions)
  • Cancel → stop and discard the drafts; nothing has been written or branched. Do not ask Part 2.
  • Edit → ask what to add / remove / reorder / re-split; re-launch the PA (Step 2); loop back to this gate.
  • Approve → ask Part 2, then go to Step 5 carrying every answer.

Part 2 — workspace and build (only on Approve):

  1. Workspace? — where should branch feat/{slug} live? → New worktree (isolated ../{repo}-{slug} dir) / Current working tree (branch in place)
  2. Build now? — what runs after setup? → /squid-implement-night (end-to-end to a validated PR) / /squid-implement-task loop (build + commit the tasks only, no review/CI) / Stop after planning

Step 5 — Execute the decisions (only after the final plan + Approve)

Do these in order.

A. Set up the workspace (Q5).

  • New worktree:
    bash
    git rev-parse --abbrev-ref HEAD          # expect main; if not, ask the human how to proceed
    git pull
    WORKTREE_PATH="$(git rev-parse --show-toplevel)/../$(basename $(git rev-parse --show-toplevel))-{slug}"
    git worktree add -b feat/{slug} "$WORKTREE_PATH" main
    If it already exists (re-running after an abort): tell the human, ask reuse (r) / recreate (d) — default reuse.
  • Current working tree:
    bash
    git pull
    git switch -c feat/{slug}                # if already on a feat/* branch, reuse it instead
    WORKTREE_PATH = the repo root.

B. Write the approved artifacts into the workspace. Write to absolute paths under $WORKTREE_PATH (for a new worktree the orchestrator's cwd is still the main repo — do NOT write the tasks into main):

  • Tasks (Q2). Local files → one $WORKTREE_PATH/tasks/<NNN>-<slug>.md per task (status: pending) from the Step 2 drafts. GitHub Issues → gh issue create one issue per task from the same drafts, in NNN order (titles NNN — {slug}, body = the groomed spec). The chosen mode is this feature's tracker for the rest of the pipeline; if it differs from AGENTS.md TRACKER_MODE, it's a one-off override for this plan — don't rewrite AGENTS.md.
  • Glossary (Q4). If Apply: apply the drafted additions to $WORKTREE_PATH/docs/glossary.md. If Skip (or none were drafted): discard them.
  • ADR (Q3). If Create: write $WORKTREE_PATH/docs/adr/NNNN-<slug>.md (Status: Accepted) from the ADR draft. If Skip: discard the draft.
  • Verify: in Local files mode, ls "$WORKTREE_PATH/tasks" lists every expected file, each with status: pending + acceptance criteria; in GitHub Issues mode, gh issue list shows one issue per task. If anything is missing, write it now — do not hand off an empty plan.

C. Kick off the build (Q6).

  • /squid-implement-night → invoke /squid-implement-night with: feature {slug}, Working directory: $WORKTREE_PATH.
  • /squid-implement-task loop → invoke /squid-implement-task with: the feature's pending tasks (tasks/<NNN>-*.md, status: pending), Working directory: $WORKTREE_PATH.
  • Stop after planning → hand off and stop:
    Plan approved. {N} tasks in tasks/ (status: pending) on `feat/{slug}` ({worktree at $WORKTREE_PATH | current working tree}).
    Next: run `/squid-implement-night` (builds every pending task to a validated PR), or `/squid-implement-task` for individual tasks.

/squid-plan ends here.


Notes

  • Task-file shape: see the tracker-workflow spec (squid-scaffold/specs/tracker-workflow.md); ADR rules live in the product-architect agent contract.

Frequently asked questions

What does the Squid Plan AI skill do?

Turn a raw feature spec into an approved Tasks Plan — grill the spec, have the Product Architect groom draft tasks (+ optional ADR and glossary additions), then run ONE human gate that decides everything touching the repo: tasks + storage, ADR, glossary, worktree, and which build to run.

Why use Squid Plan on TypingMind?

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

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

Which AI models can use Squid Plan?

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 Plan?

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

Is the Squid Plan 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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