feature-forge

jeffallan/claude-skills · updated Apr 8, 2026

$npx skills add https://github.com/jeffallan/claude-skills --skill feature-forge
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summary

Structured requirements workshops that produce EARS-format specifications, user stories, acceptance criteria, and implementation checklists.

  • Conducts systematic discovery interviews from both product and engineering perspectives, using structured questioning to elicit requirements before writing specifications
  • Outputs comprehensive specifications including functional requirements in EARS format, non-functional requirements, Given/When/Then acceptance criteria, error handling tables, and
skill.md

Feature Forge

Requirements specialist conducting structured workshops to define comprehensive feature specifications.

Role Definition

Operate with two perspectives:

  • PM Hat: Focused on user value, business goals, success metrics
  • Dev Hat: Focused on technical feasibility, security, performance, edge cases

When to Use This Skill

  • Defining new features from scratch
  • Gathering comprehensive requirements
  • Writing specifications in EARS format
  • Creating acceptance criteria
  • Planning implementation TODO lists

Core Workflow

  1. Discover - Use AskUserQuestions to understand the feature goal, target users, and user value. Present structured choices where possible (e.g., user types, priority level).
  2. Interview - Systematic questioning from both PM and Dev perspectives using AskUserQuestions for structured choices and open-ended follow-ups. Use multi-agent discovery with Task subagents when the feature spans multiple domains (see interview-questions.md for guidance).
  3. Document - Write EARS-format requirements
  4. Validate - Use AskUserQuestions to review acceptance criteria with stakeholder, presenting key trade-offs as structured choices
  5. Plan - Create implementation checklist

Reference Guide

Load detailed guidance based on context:

Topic Reference Load When
EARS Syntax references/ears-syntax.md Writing functional requirements
Interview Questions references/interview-questions.md Gathering requirements
Specification Template references/specification-template.md Writing final spec document
Acceptance Criteria references/acceptance-criteria.md Given/When/Then format
Pre-Discovery Subagents references/pre-discovery-subagents.md Multi-domain features needing front-loaded context

Constraints

MUST DO

  • Use AskUserQuestions tool for structured elicitation (priority, scope, format choices)
  • Use open-ended questions only when choices cannot be predetermined
  • Conduct thorough interview before writing spec
  • Use EARS format for all functional requirements
  • Include non-functional requirements (performance, security)
  • Provide testable acceptance criteria
  • Include implementation TODO checklist
  • Ask for clarification on ambiguous requirements

MUST NOT DO

  • Output interview questions as plain text when AskUserQuestions can provide structured options
  • Generate spec without conducting interview
  • Accept vague requirements ("make it fast")
  • Skip security considerations
  • Forget error handling requirements
  • Write untestable acceptance criteria

Output Templates

The final specification must include:

  1. Overview and user value
  2. Functional requirements (EARS format)
  3. Non-functional requirements
  4. Acceptance criteria (Given/When/Then)
  5. Error handling table
  6. Implementation TODO checklist

Inline EARS format examples (load references/ears-syntax.md for full syntax):

When <trigger>, the <system> shall <response>.
Where <feature> is active, the <system> shall <behaviour>.
The <system> shall <action> within <measure>.

Inline acceptance criteria example (load references/acceptance-criteria.md for full format):

Given a registered user is on the login page,
When they submit valid credentials,
Then they are redirected to the dashboard within 2 seconds.

Save as: specs/{feature_name}.spec.md

Discussion

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general reviews

Ratings

4.571 reviews
  • Noor Gupta· Dec 24, 2024

    feature-forge reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Dhruvi Jain· Dec 8, 2024

    I recommend feature-forge for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Ren Martin· Dec 8, 2024

    We added feature-forge from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Carlos Torres· Dec 8, 2024

    I recommend feature-forge for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Neel Mehta· Dec 4, 2024

    feature-forge reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Oshnikdeep· Nov 27, 2024

    Solid pick for teams standardizing on skills: feature-forge is focused, and the summary matches what you get after install.

  • Noor Iyer· Nov 27, 2024

    feature-forge fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Min Robinson· Nov 27, 2024

    Solid pick for teams standardizing on skills: feature-forge is focused, and the summary matches what you get after install.

  • Anika Shah· Nov 23, 2024

    feature-forge has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Jin Sanchez· Nov 15, 2024

    feature-forge has been reliable in day-to-day use. Documentation quality is above average for community skills.

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