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Elaborate Spec

Community
danielvm-git
elaborate-spec

Refine a rough idea into a clear, detailed specification through dialogue. Does not produce code. Use when user has a vague idea, wants to think through a feature before planning, or needs to turn "I want X" into a concrete spec.

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill nameelaborate-spec
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 Elaborate Spec 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/elaborate-spec .claude/skills/elaborate-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Elaborate Spec 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 Elaborate Spec 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 Elaborate Spec 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.

Elaborate Spec

Turn a rough idea into a clear specification through focused dialogue. No code is written during this skill — the output is shared understanding and a refined problem statement.

HARD GATE — Do NOT proceed with planning or implementation until the problem space is clearly understood. Success criteria, actors, and scope must be explicit before drafting a plan.

Process

1. Listen first

Let the user describe their idea in their own words. Do not interrupt or redirect. Take notes on:

  • The core problem they're trying to solve
  • Who is affected (actors)
  • What success looks like to them
  • Any constraints they've already identified

2. Ask clarifying questions

Ask one question at a time. Work through these areas:

Problem clarity

  • What is the current behavior (or lack of behavior) that prompted this?
  • Who experiences this problem? How often?
  • What's the cost of not solving it?

Solution boundaries

  • What is explicitly IN scope?
  • What is explicitly OUT of scope?
  • Are there existing solutions (internal or external) this replaces or integrates with?

Success criteria

  • How will you know this is done?
  • What does the happy path look like end-to-end?
  • What are the key failure modes to handle?

Constraints

  • Any performance requirements?
  • Any compatibility constraints (existing APIs, data formats)?
  • Any non-negotiable implementation decisions already made?

2.5. Multiple Interpretations (HARD GATE)

HARD GATE — If the request admits ≥2 valid interpretations, do NOT guess. You must list them and ask the user to choose before proceeding. Proceeding with unresolved ambiguity is a failure of integrity.

Present the options clearly:

"I see two ways to read this:

  1. [Interpretation A] — my recommendation because [reason]
  2. [Interpretation B] Which is closer to what you mean?"

3. Surface hidden assumptions

Once the user has answered the main questions, probe for assumptions:

  • "You mentioned X — does that mean Y is also true?"
  • "What happens when Z fails?"
  • "Is this for internal users, external users, or both?"

4. Synthesize and confirm

Summarize your understanding in 3–5 bullet points aligned with countable-story-format.md:

  • The problem (feeds into §1 Business narrative)
  • The solution and main flow (feeds into §5)
  • The key constraints and alternative flows (feeds into §6)
  • The success criteria (feeds into §17 Gherkin)
  • What's out of scope (feeds into §18)

Ask: "Is this an accurate summary? Anything missing or wrong?"

5. Write specs/planning-context.yaml

After the user confirms the summary in step 4, persist the key decisions:

yaml
# specs/planning-context.yaml — written by elaborate-spec; consumed by scope-work and slice-tasks
feature_name: "<from step 1>"
problem_statement: "<one paragraph>"
constraints:
  - "<constraint 1>"
out_of_scope:
  - "<excluded item 1>"
key_decisions:
  - decision: "<what was decided>"
    rationale: "<why>"

If specs/planning-context.yaml already exists, ask: "Planning context from a prior session exists. Update it? [Y/n]". Overwrite on Y; leave unchanged on N.

6. Suggest next skill

Once the spec is clear, recommend the next step:

  • If domain model needs work → model-domain
  • If ready to plan → plan-release (creates epic capsules with epic.yaml + story .md + -tasks.yaml) then plan-work per story
  • If a spike is needed first → spike-prototype
  • If architecture decisions are needed → deepen-architecture or grill-me
  • If the plan depends on a specific library or API → grill-me in docs mode

Frequently asked questions

What does the Elaborate Spec AI skill do?

Refine a rough idea into a clear, detailed specification through dialogue. Does not produce code. Use when user has a vague idea, wants to think through a feature before planning, or needs to turn "I want X" into a concrete spec.

Why use Elaborate Spec on TypingMind?

Because you install it once and use it with any model. Elaborate Spec 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 Elaborate Spec in TypingMind?

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

Which AI models can use Elaborate Spec?

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 Elaborate Spec?

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

Is the Elaborate Spec 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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