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Scope Work

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danielvm-git
scope-work

PLANNING SPINE STEP 1 of 3 — Scope the work: define what is in and out of scope and save as specs/product/SCOPE_LATEST.yaml. Use before slice-tasks or plan-release on any new initiative. Not a substitute for slice-tasks (step 2) or plan-work (step 3).

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namescope-work
Stars
206
Forks
18
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 danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Scope Work 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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/scope-work .claude/skills/scope-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scope Work 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 Scope Work 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 Scope Work 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.

story: e03s01

story: e24s02

Scope Work

Spine position: Step 1 — scope-work → slice-tasks → plan-work.

Turn the current conversation into a bounded PRD at specs/product/SCOPE_LATEST.yaml. Without a scope boundary, implementation drifts — stories expand, estimates blow up, and "done" becomes undefined.

Pre-flight

  • Do you have a clear user need or problem statement? If not, run elaborate-spec first.
  • Does specs/product/VISION_LATEST.yaml exist? If yes, read it for north-star alignment.
  • Is there an existing SCOPE_LATEST.yaml? If yes, you're refining, not creating from scratch.

Process

  1. Read planning-context.yaml — If specs/planning-context.yaml exists, read it before doing anything else:

    bash
    test -f specs/planning-context.yaml && echo "Context found" || echo "No context — starting fresh"

    Pre-populate feature_name, constraints, and out_of_scope from the file. Skip re-asking questions already answered by elaborate-spec. If the file is absent, proceed normally.

  2. Gather context — Read existing specs/ artifacts (release-plan.yaml, plans/TECH_STACK_LATEST.md, requirements/VISION_LATEST.yaml if any). Understand what the project is building and why.

  3. Interview (if needed) — Clarify: What is the goal? Who are the users? What is definitely in scope? What is explicitly out of scope? What constraints exist (time, budget, tech)? How will success be measured?

  4. Write specs/product/SCOPE_LATEST.yaml with these fields:

    • core_value — one-sentence value proposition
    • summary — 2-3 paragraph scope overview
    • in_scope[] — list of what this initiative covers (each maps to an epic/story)
    • out_of_scope[] — explicit exclusions (prevents scope creep)
    • constraints — tech, time, resource boundaries
    • success_criteria — observable outcomes that prove the scope is delivered
    • references — links to related specs, ADRs, or documents
  5. Lightweight trade-off analysis — For each out_of_scope item, note why it's excluded (deferred, not valuable, too risky, depends on external factor). This protects against "what about X?" questions later.

  6. Run research-first if external dependencies are proposed — verify the dependency exists, is maintained, and fits the scope before committing to it.

HARD GATE — Every in_scope item must map to a future epic/story ID or explicit deferred note in out_of_scope. If an item can't be mapped, the scope is too vague — refine before proceeding.

HARD GATE — Do NOT include implementation details in SCOPE_LATEST.yaml. Scope is what and why, not how. Implementation detail belongs in epic capsules and slice-tasks.

Common Anti-Patterns

  • "Everything is in scope" — If nothing is out of scope, you haven't defined a scope. You've described a universe. Cut aggressively.
  • "We'll figure it out later" — Ambiguity in scope propagates to every downstream decision. Resolve now or explicitly defer in writing.
  • Scope as architecture — Saying "we need a PostgreSQL database" is architecture, not scope. Scope says "we need to store user profiles and transaction history."

Output

specs/product/SCOPE_LATEST.yaml — the bounded PRD. Subsequent skills (slice-tasks, plan-work) reference this as the source of truth for what to build.

Verify

→ verify: test -f specs/product/SCOPE_LATEST.yaml && grep -q out_of_scope specs/product/SCOPE_LATEST.yaml

Frequently asked questions

What does the Scope Work AI skill do?

PLANNING SPINE STEP 1 of 3 — Scope the work: define what is in and out of scope and save as specs/product/SCOPE_LATEST.yaml. Use before slice-tasks or plan-release on any new initiative. Not a substitute for slice-tasks (step 2) or plan-work (step 3).

Why use Scope Work on TypingMind?

Because you install it once and use it with any model. Scope Work 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 Scope Work in TypingMind?

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

Which AI models can use Scope Work?

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 Scope Work?

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

Is the Scope Work AI skill free?

Yes. It is published on GitHub by danielvm-git 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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