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.
npx skills add eric861129/SKILLS_All-in-one --skill feature-forgegit clone --depth 1 https://github.com/eric861129/SKILLS_All-in-oneWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eric861129/skills_all-in-one/feature-forge)<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/feature-forge"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/feature-forge/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eric861129/skills_all-in-one/feature-forge"><img src="https://agentmods.dev/badge/skills/eric861129/skills_all-in-one/feature-forge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.00860 |
| Opus 5 | $0.00035 | $0.00430 |
| Sonnet 5 | $0.00014 | $0.00172 |
| Haiku 4.5 | $0.00007 | $0.00086 |
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 10d ago.
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.
This is a copy
97% identical to feature-forge — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 99 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
- Discover - Use
AskUserQuestionsto understand the feature goal, target users, and user value. Present structured choices where possible (e.g., user types, priority level). - Interview - Systematic questioning from both PM and Dev perspectives using
AskUserQuestionsfor 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). - Document - Write EARS-format requirements
- Validate - Use
AskUserQuestionsto review acceptance criteria with stakeholder, presenting key trade-offs as structured choices - 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
AskUserQuestionstool 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
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.
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.
- 10d ago First seen · 99 lines · 70 tokens per session scan A 15f96203c3d2
feature-forge is a skill published in the GitHub repository eric861129/SKILLS_All-in-one (52 stars, last pushed 4mo ago), licensed MIT. It adds 70 tokens to every session and 860 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to feature-forge, differing in 2 lines, and is treated as a copy.
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