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Feature Spec

Community
minicoohei
feature-spec

PRD(プロダクト要件定義書)の作成、機能仕様の策定、受け入れ基準の定義を行うスキル。 「PRDを書いて」「機能仕様を作って」「要件定義して」等のリクエストで発動。

Overview

Publisherminicoohei
Repositoryai-agent-camp
Skill namefeature-spec
Stars
346
Forks
13
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by minicoohei on GitHub. Read the source before you install it.

Installation

Install the Feature 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.
    https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/feature-spec
  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/minicoohei/ai-agent-camp.git /tmp/ai-agent-camp
mkdir -p .claude/skills
cp -r /tmp/ai-agent-camp/.claude/skills/feature-spec .claude/skills/feature-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feature 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 Feature 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 Feature 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.

Feature Spec Skill

You are an expert at writing product requirements documents (PRDs) and feature specifications. You help product managers define what to build, why, and how to measure success.

PRD Structure

A well-structured PRD follows this template:

1. Problem Statement

  • Describe the user problem in 2-3 sentences
  • Who experiences this problem and how often
  • What is the cost of not solving it (user pain, business impact, competitive risk)
  • Ground this in evidence: user research, support data, metrics, or customer feedback

2. Goals

  • 3-5 specific, measurable outcomes this feature should achieve
  • Each goal should answer: "How will we know this succeeded?"
  • Distinguish between user goals (what users get) and business goals (what the company gets)
  • Goals should be outcomes, not outputs ("reduce time to first value by 50%" not "build onboarding wizard")

3. Non-Goals

  • 3-5 things this feature explicitly will NOT do
  • Adjacent capabilities that are out of scope for this version
  • For each non-goal, briefly explain why it is out of scope (not enough impact, too complex, separate initiative, premature)
  • Non-goals prevent scope creep during implementation and set expectations with stakeholders

4. User Stories

Write user stories in standard format: "As a [user type], I want [capability] so that [benefit]"

Guidelines:

  • The user type should be specific enough to be meaningful ("enterprise admin" not just "user")
  • The capability should describe what they want to accomplish, not how
  • The benefit should explain the "why" — what value does this deliver
  • Include edge cases: error states, empty states, boundary conditions
  • Include different user types if the feature serves multiple personas
  • Order by priority — most important stories first

Example:

  • "As a team admin, I want to configure SSO for my organization so that my team members can log in with their corporate credentials"
  • "As a team member, I want to be automatically redirected to my company's SSO login so that I do not need to remember a separate password"
  • "As a team admin, I want to see which members have logged in via SSO so that I can verify the rollout is working"

5. Requirements

Must-Have (P0): The feature cannot ship without these. These represent the minimum viable version of the feature. Ask: "If we cut this, does the feature still solve the core problem?" If no, it is P0.

Nice-to-Have (P1): Significantly improves the experience but the core use case works without them. These often become fast follow-ups after launch.

Future Considerations (P2): Explicitly out of scope for v1 but we want to design in a way that supports them later. Documenting these prevents accidental architectural decisions that make them hard later.

For each requirement:

  • Write a clear, unambiguous description of the expected behavior
  • Include acceptance criteria (see below)
  • Note any technical considerations or constraints
  • Flag dependencies on other teams or systems

6. Success Metrics

See the success metrics section below for detailed guidance.

7. Open Questions

  • Questions that need answers before or during implementation
  • Tag each with who should answer (engineering, design, legal, data, stakeholder)
  • Distinguish between blocking questions (must answer before starting) and non-blocking (can resolve during implementation)

8. Timeline Considerations

  • Hard deadlines (contractual commitments, events, compliance dates)
  • Dependencies on other teams' work or releases
  • Suggested phasing if the feature is too large for one release

User Story Writing

Good user stories are:

  • Independent: Can be developed and delivered on their own
  • Negotiable: Details can be discussed, the story is not a contract
  • Valuable: Delivers value to the user (not just the team)
  • Estimable: The team can roughly estimate the effort
  • Small: Can be completed in one sprint/iteration
  • Testable: There is a clear way to verify it works

Common Mistakes in User Stories

  • Too vague: "As a user, I want the product to be faster" — what specifically should be faster?
  • Solution-prescriptive: "As a user, I want a dropdown menu" — describe the need, not the UI widget
  • No benefit: "As a user, I want to click a button" — why? What does it accomplish?
  • Too large: "As a user, I want to manage my team" — break this into specific capabilities
  • Internal focus: "As the engineering team, we want to refactor the database" — this is a task, not a user story

Requirements Categorization

MoSCoW Framework

  • Must have: Without these, the feature is not viable. Non-negotiable.
  • Should have: Important but not critical for launch. High-priority fast follows.
  • Could have: Desirable if time permits. Will not delay delivery if cut.
  • Won't have (this time): Explicitly out of scope. May revisit in future versions.

Tips for Categorization

  • Be ruthless about P0s. The tighter the must-have list, the faster you ship and learn.
  • If everything is P0, nothing is P0. Challenge every must-have: "Would we really not ship without this?"
  • P1s should be things you are confident you will build soon, not a wish list.
  • P2s are architectural insurance — they guide design decisions even though you are not building them now.

Success Metrics Definition

Leading Indicators

Metrics that change quickly after launch (days to weeks):

  • Adoption rate: % of eligible users who try the feature
  • Activation rate: % of users who complete the core action
  • Task completion rate: % of users who successfully accomplish their goal
  • Time to complete: How long the core workflow takes
  • Error rate: How often users encounter errors or dead ends
  • Feature usage frequency: How often users return to use the feature

Lagging Indicators

Metrics that take time to develop (weeks to months):

  • Retention impact: Does this feature improve user retention?
  • Revenue impact: Does this drive upgrades, expansion, or new revenue?
  • NPS / satisfaction change: Does this improve how users feel about the product?
  • Support ticket reduction: Does this reduce support load?
  • Competitive win rate: Does this help win more deals?

Setting Targets

  • Targets should be specific: "50% adoption within 30 days" not "high adoption"
  • Base targets on comparable features, industry benchmarks, or explicit hypotheses
  • Set a "success" threshold and a "stretch" target
  • Define the measurement method: what tool, what query, what time window
  • Specify when you will evaluate: 1 week, 1 month, 1 quarter post-launch

Acceptance Criteria

Write acceptance criteria in Given/When/Then format or as a checklist:

Given/When/Then:

  • Given [precondition or context]
  • When [action the user takes]
  • Then [expected outcome]

Example:

  • Given the admin has configured SSO for their organization
  • When a team member visits the login page
  • Then they are automatically redirected to the organization's SSO provider

Checklist format:

  • Admin can enter SSO provider URL in organization settings
  • Team members see "Log in with SSO" button on login page
  • SSO login creates a new account if one does not exist
  • SSO login links to existing account if email matches
  • Failed SSO attempts show a clear error message

Tips for Acceptance Criteria

  • Cover the happy path, error cases, and edge cases
  • Be specific about the expected behavior, not the implementation
  • Include what should NOT happen (negative test cases)
  • Each criterion should be independently testable
  • Avoid ambiguous words: "fast", "user-friendly", "intuitive" — define what these mean concretely

Scope Management

Recognizing Scope Creep

Scope creep happens when:

  • Requirements keep getting added after the spec is approved
  • "Small" additions accumulate into a significantly larger project
  • The team is building features no user asked for ("while we're at it...")
  • The launch date keeps moving without explicit re-scoping
  • Stakeholders add requirements without removing anything

Preventing Scope Creep

  • Write explicit non-goals in every spec
  • Require that any scope addition comes with a scope removal or timeline extension
  • Separate "v1" from "v2" clearly in the spec
  • Review the spec against the original problem statement — does everything serve it?
  • Time-box investigations: "If we cannot figure out X in 2 days, we cut it"
  • Create a "parking lot" for good ideas that are not in scope

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Feature Spec AI skill do?

PRD(プロダクト要件定義書)の作成、機能仕様の策定、受け入れ基準の定義を行うスキル。 「PRDを書いて」「機能仕様を作って」「要件定義して」等のリクエストで発動。

Why use Feature Spec on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/minicoohei/ai-agent-camp/tree/main/.claude/skills/feature-spec. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Feature 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 Feature Spec?

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

Is the Feature Spec AI skill free?

It is published on GitHub by minicoohei. Check the repository for licensing terms. 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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