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Task Decomposition

Community
adamentwistle
task-decomposition

Planning discipline for multi-step work — acceptance check first, read the terrain, dependency-ordered steps, de-risk the riskiest step early, re-plan on surprise. Use at the start of any task touching 3+ files or steps, for "implement", "build", "migrate" or "add feature" requests, and mid-task when a discovery invalidates the approach. Not for single-file edits with an obvious fix.

Overview

Publisheradamentwistle
Repositoryfable-skills
Skill nametask-decomposition
Stars
98
Forks
11
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 adamentwistle on GitHub. Read the source before you install it.

Installation

Install the Task Decomposition 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/adamentwistle/fable-skills/tree/main/task-decomposition
  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/adamentwistle/fable-skills.git /tmp/fable-skills
mkdir -p .claude/skills
cp -r /tmp/fable-skills/task-decomposition .claude/skills/task-decomposition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Task Decomposition 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 Task Decomposition 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 Task Decomposition 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.

Task Decomposition

Weak runs start editing in the first minute and discover the real shape of the task by breaking things. Strong runs spend the first 10–20% of the task front-loading understanding, commit to a sequence, and change the sequence the moment reality contradicts it.

1. Restate the goal as an acceptance check

One or two sentences plus the exact command or observation that proves it: "Done means npm test -- export passes and the CSV opens with headers." Run that check once now. Expected result: it fails or does not exist yet. If it already passes, the task is misunderstood; reread the request. If the request is genuinely ambiguous on a decision the user owns, ask ONE batched set of questions now, not a drip mid-task. Ambiguity you can resolve from the code is yours to resolve.

2. Read the terrain before planning the route

Before proposing steps:

  • Read the project instructions (CLAUDE.md, AGENTS.md) for constraints that override defaults.
  • Open every file you expect to edit, plus one caller or consumer of each. Blast radius is defined by callers, not by the file you were pointed at.
  • Find the last similar change (git log, the newest analog in the tree) and plan to imitate it, not invent.

3. Write the step list, dependency-ordered

3–8 concrete steps, each independently verifiable, ordered so the build stays green as long as possible: types and schemas, then implementations, then call sites, then UI. For each step write the files touched and the command that verifies it. "Verify" without a command is not a step.

More than 8 steps means the task is two tasks; split it into phases and finish one before starting the next. Track the list visibly (todo list) for 3+ steps; mark exactly one step in progress at a time, and run its verification before marking it done.

4. Identify the risky step and de-risk it first

One step usually carries the real uncertainty: the unfamiliar API, the migration, the concurrency. Probe THAT step first with a spike, a dry run, or a one-file prototype, before investing in the safe steps. Discovering step 5 is impossible after finishing steps 1–4 is the expensive failure.

5. Re-plan on surprise, don't push through

When reality contradicts the plan (an API doesn't exist, a test reveals hidden coupling, a file is generated, a second attempt at the same step fails), STOP editing. Rewrite the plan explicitly: what changed, which steps die, which are added. Pushing the original plan through contradicting evidence produces half-finished hybrid states. If the surprise changes the scope the user asked for, surface it with options instead of silently redefining the task.

6. Keep a ledger on long tasks

For work spanning many steps or likely to hit context limits: maintain a short scratchpad file with goal, decisions made and why, steps done, steps remaining, current blockers. Update it at each milestone.

Exit condition

The task is done when the step list is empty AND the acceptance check from step 1 passes, run again after the last edit and pasted into the report. Verified, not assumed.

Frequently asked questions

What does the Task Decomposition AI skill do?

Planning discipline for multi-step work — acceptance check first, read the terrain, dependency-ordered steps, de-risk the riskiest step early, re-plan on surprise. Use at the start of any task touching 3+ files or steps, for "implement", "build", "migrate" or "add feature" requests, and mid-task when a discovery invalidates the approach. Not for single-file edits with an obvious fix.

Why use Task Decomposition on TypingMind?

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

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

Which AI models can use Task Decomposition?

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 Task Decomposition?

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

Is the Task Decomposition AI skill free?

Yes. It is published on GitHub by adamentwistle 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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