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 bobchao/pm-skills-rfp-to-stories --skill story-refinergit clone --depth 1 https://github.com/bobchao/pm-skills-rfp-to-storiesWrote 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/bobchao/pm-skills-rfp-to-stories/story-refiner)<a href="https://agentmods.dev/skills/bobchao/pm-skills-rfp-to-stories/story-refiner"><img src="https://agentmods.dev/badge/skills/bobchao/pm-skills-rfp-to-stories/story-refiner/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/bobchao/pm-skills-rfp-to-stories/story-refiner"><img src="https://agentmods.dev/badge/skills/bobchao/pm-skills-rfp-to-stories/story-refiner.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 | $0.00041 | $0.03533 |
| Opus 5 | $0.00020 | $0.01767 |
| Sonnet 5 | $0.00008 | $0.00707 |
| Haiku 4.5 | $0.00004 | $0.00353 |
Grade A, and why
Story Refiner 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Story Refiner Skill
Language Preference
Default: Respond in the same language as the user's input or as explicitly requested by the user.
If the user specifies a preferred language (e.g., "請用中文回答", "Reply in Japanese"), use that language for all outputs. Otherwise, match the language of the provided Stories.
Role Definition
You simultaneously play three roles to review User Stories:
- Senior Developer: Evaluates technical feasibility and estimation clarity
- QA Engineer: Evaluates testability and acceptance criteria clarity
- Product Stakeholder: Evaluates requirement coverage and value clarity
Core Principles
Correction Over Reporting
- Don't just point out problems, directly fix them
- Every flagged issue must have a corresponding improved version
- Humans only need final confirmation, not manual correction
Conservative Correction
- Only correct Stories with "obvious problems"
- Don't correct for the sake of correcting
- Stories that already pass don't need changes
Transparent Annotation
- Clearly explain why corrections were made
- Provide original vs. improved version comparison
- Let humans choose to accept or keep original version
Input Format
This Skill accepts the following inputs:
- Story Writer output (recommended)
- Any format User Stories list
- Original RFP + Stories (can cross-reference coverage)
Evaluation Criteria Reference
All scoring and evaluation must follow the standards defined in references/evaluation-criteria.md.
This document defines:
- Three scoring dimensions (Development Clarity, Testability, Value Clarity)
- Detailed scoring criteria for each dimension (1-5 points)
- Specific checkpoints and common deduction patterns
- Final score calculation method
Important: Both Quick Scan (Phase 1) and Detailed Evaluation (Phase 2) use these same criteria, with different levels of depth.
Evaluation Flow
Phase 1: Quick Scan
Score each Story initially (1-5 points) using the three dimensions from references/evaluation-criteria.md:
What ships with it
2 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.
- 12d ago First seen · 470 lines · 41 tokens per session scan A cf63ad510411
Story Refiner is a skill published in the GitHub repository bobchao/pm-skills-rfp-to-stories (28 stars, last pushed 8mo ago), licensed MIT. It adds 41 tokens to every session and 3,533 once invoked, about $0.0002 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.
Other skills, from other repositories
dummy-dataset
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
outcome-roadmap
Transform an output-focused roadmap into an outcome-focused one that communicates strategic intent. Rewrites initiatives as outcome statements reflecting user and business impacts. Use when shifting to outcome roadmaps, making a roadmap more strategic, or rewriting feature lists as outcomes.
pre-mortem
Run a pre-mortem risk analysis on a PRD or launch plan. Categorizes risks as Tigers (real problems), Paper Tigers (overblown concerns), and Elephants (unspoken worries), then classifies as launch-blocking, fast-follow, or track. Use when preparing for launch, stress-testing a product plan, or identifying what could go…
retro
Facilitate a structured sprint retrospective — what went well, what didn't, and prioritized action items with owners and deadlines. Use when running a retrospective, reflecting on a sprint, creating action items from team feedback, or learning how to run effective retros.
release-notes
Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.
shipping-artifacts
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded…