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Spec

Organization
codewithmukesh
spec

Turn a vague feature or product idea into an agreed, persisted specification through relentless structured questioning. Never assumes — every gap, ambiguity, or "probably" becomes a question to the developer, and the spec cannot be approved while open questions remain. Produces docs/specs/<NNN>-<slug>.md with acceptance criteria that /plan, /scaffold, and /tdd consume. Use when: "spec", "write a spec", "spec this out", "requirements", "PRD", "acceptance criteria", "define the feature", "user stories", "what should we build", or before planning any feature too big to describe in one sentence.

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill namespec
Stars
721
Forks
170
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 codewithmukesh on GitHub. Read the source before you install it.

Installation

Install the 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/codewithmukesh/dotnet-claude-kit.git /tmp/dotnet-claude-kit
mkdir -p .claude/skills
cp -r /tmp/dotnet-claude-kit/skills/spec .claude/skills/spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

/spec — Relentless Specification Workflow

What

Converts an idea into a written, versioned specification that both the developer and Claude explicitly agree on — before any planning or code. The contract:

  • Never assume. Every gap in the idea becomes a question. If Claude catches itself thinking "probably", "presumably", or "the usual way" — that thought is a question to ask, not a decision to make.
  • Relentless, but structured. Questions come in focused rounds (3–5 at a time) across nine dimensions — not one overwhelming dump, and not a single polite round that stops early.
  • Agreement is explicit. Specs have a status lifecycle: Draft → In Review → Approved. Implementation never starts from a Draft. Approval requires the developer to read the final document and say so.
  • Specs are files, not chat. Output persists to docs/specs/<NNN>-<slug>.md and survives the session. Plans, tests, and commits reference it.

When

  • Any feature too big to describe completely in one sentence
  • New product or module ideas ("I want to add team workspaces")
  • Before /plan for non-trivial features — plan consumes the approved spec
  • When requirements feel fuzzy mid-implementation: stop, /spec, re-plan
  • Trigger phrases: "spec", "requirements", "PRD", "define the feature", "acceptance criteria"

Skip for: bug fixes, refactors, single-endpoint CRUD where the entity is obvious.

How

Step 1: Capture and Restate

Take the raw idea and restate it in one paragraph: what Claude understood, in its own words. End with: "Is this the idea? What did I get wrong?" Do not begin questioning until the developer confirms the restatement — questioning the wrong idea wastes everyone's time.

Step 2: Questioning Rounds

Work through the nine dimensions in order. Each round: pick the 3–5 most load-bearing unanswered questions (answers that reshape later questions come first). Where the harness supports selectable options, present choices with trade-offs — and a recommendation — but the developer chooses; a recommendation is never silently applied.

#DimensionWhat to pin down
1Problem & usersWho hurts today, how they work around it, what success looks like
2ScopeWhat is IN this iteration, what is explicitly OUT, where the MVP line sits
3Domain & dataEntities, relationships, lifecycle (create→archive→delete?), retention
4API contractResources, endpoints, request/response shapes, pagination, versioning
5AuthorizationWho can do what, role/claim model, tenant boundaries
6Edge cases & failure modesConcurrency, duplicates, idempotency, partial failure, limits
7Non-functionalsExpected volume, latency budget, growth assumptions
8IntegrationsExternal services, published events, webhooks, side effects
9Acceptance criteriaTestable Given/When/Then for every behavior in scope

Rules of relentless questioning:

  • Record every answer in the draft spec immediately — answers are requirements, not conversation.
  • Challenge contradictions on the spot: "In round 1 you said X; this answer implies not-X. Which wins?"
  • "I don't know" is a legal answer → moves to Deferred Decisions with an explicit fallback the developer chooses now ("default to soft-delete until decided"). Silent deferral is forbidden.
  • A dimension is done when a follow-up round generates zero new questions for it.
  • The questioning phase is done when ALL nine dimensions are done. Do not stop because the conversation feels long — stopping early is how assumptions sneak in.

Step 3: Draft the Spec File

Determine the next number from existing files in docs/specs/ (create the directory if missing). Write docs/specs/<NNN>-<slug>.md:

markdown
# Spec NNN: <Title>

**Status:** Draft
**Date:** <today>

## Problem            <!-- who hurts, why now -->
## Scope              <!-- ### In / ### Out — both explicit -->
## Domain Model       <!-- entities, relationships, lifecycle -->
## API Contract       <!-- endpoints table: verb, route, request, response, errors -->
## Authorization      <!-- who can do what -->
## Edge Cases & Failure Modes
## Non-Functional Requirements
## Integrations
## Acceptance Criteria  <!-- Given/When/Then, numbered AC-1, AC-2, ... -->
## Deferred Decisions   <!-- decision, chosen fallback, revisit trigger -->
## Open Questions       <!-- MUST be empty before approval -->

Step 4: Review Loop

Set status to In Review. Present the complete spec and ask: "Read this end-to-end. What is wrong, missing, or over-engineered?" Fold corrections in and re-present. Repeat until the developer has no further changes. New answers may spawn new questions — that is the process working, not a failure to converge.

Step 5: The Agreement Gate

Approval is a deliberate act, never inferred from silence or "looks good" in passing. Ask explicitly: "Do you approve this spec? After approval, code follows the spec — changes go through the spec first." On approval, set **Status:** Approved (<date>).

  • Open Questions must be empty. If any remain, the spec cannot be approved — resolve or defer each one explicitly.
  • If implementation later reveals a wrong assumption: stop, set status back to In Review, fix the spec with the developer, then resume. Code never silently diverges from an approved spec.

Step 6: Handoff

  • /plan reads the approved spec and maps acceptance criteria to implementation steps
  • /tdd turns acceptance criteria into the first failing tests (AC-n → test name)
  • Commits for the feature reference the spec: feat: team workspaces (spec 004)

Example

User: /spec users should be able to invite teammates

Claude: Here's what I understood: an existing user can invite another person
  to join their... workspace? account? That's already my first gap.
  Is this the idea? What did I get wrong?

User: yes, invite to their organization

Claude: Round 1 — Scope & Users:
  1. Can invitees be people without an existing account, or only registered users?
  2. Who can invite — any member, or only admins/owners?
  3. Is there a limit on pending invites (per org, per plan)?
  4. Out of scope check: bulk invites and SSO-provisioned members — both OUT for now?

User: 1: both. 2: admins only. 3: no limit. 4: correct, both out.

Claude: Round 2 — Edge cases (your "both" in Q1 raises these):
  1. Inviting an email that already has a pending invite — error, or resend?
  2. Invite expiry — 7 days, 30 days, never?
  3. Invitee already belongs to another org — multi-org membership or rejection?
  ...

[…rounds continue until all nine dimensions are exhausted…]

Claude: Spec written to docs/specs/004-team-invitations.md (In Review).
  Read it end-to-end. What is wrong, missing, or over-engineered?

User: looks right. approved.

Claude: Status: Approved (2026-06-12). Next: /plan to map AC-1..AC-9
  to implementation steps.

Related

  • /plan — Consumes the approved spec; never plan a spec-worthy feature without one
  • /tdd — Acceptance criteria become the first failing tests
  • /scaffold — Generates the slices the plan calls for
  • architecture-advisor — Load during Step 2 if the feature forces architectural decisions

Frequently asked questions

What does the Spec AI skill do?

Turn a vague feature or product idea into an agreed, persisted specification through relentless structured questioning. Never assumes — every gap, ambiguity, or "probably" becomes a question to the developer, and the spec cannot be approved while open questions remain. Produces docs/specs/<NNN>-<slug>.md with acceptance criteria that /plan, /scaffold, and /tdd consume. Use when: "spec", "write a spec", "spec this out", "requirements", "PRD", "acceptance criteria", "define the feature", "user stories", "what should we build", or before planning any feature too big to describe in one sentence.

Why use Spec on TypingMind?

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

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

Which AI models can use 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 Spec?

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

Is the Spec AI skill free?

Yes. It is published on GitHub by codewithmukesh 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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