feature-forge

A guided requirements-writing process for turning a feature idea into a specification, user stories, acceptance checks, and an implementation checklist.

In plain words
What is it for?
Running requirements discussions, documenting expected behaviour, defining acceptance criteria, and planning development work.
Why use it?
It reduces misunderstandings between people defining a feature and developers building it.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/jeffallan/claude-skills/feature-forge
Any agent
npx skills add Jeffallan/claude-skills --skill feature-forge
Clone the repo
git clone --depth 1 https://github.com/Jeffallan/claude-skills

Made for: Claude Code, Codex.

Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 884 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00070 $0.00884
Opus 5 $0.00035 $0.00442
Sonnet 5 $0.00014 $0.00177
Haiku 4.5 $0.00007 $0.00088

Measured yesterday against content hash 3c8b8c363767, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feature-forge scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/feature-forge/SKILL.md · 101 lines

How it starts

The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 101 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 101 lines · 70 tokens per session scan A 3c8b8c363767

Subscribe to this mod's changes

feature-forge is a skill published in the GitHub repository Jeffallan/claude-skills (11,250 stars, last pushed 24d ago), licensed MIT. It adds 70 tokens to every session and 884 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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