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 agentmods add skills/on-deck-society/love-stack/prd-generatornpx skills add on-deck-society/love-stack --skill prd-generatorgit clone --depth 1 https://github.com/on-deck-society/love-stackWrote 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/on-deck-society/love-stack/prd-generator)<a href="https://agentmods.dev/skills/on-deck-society/love-stack/prd-generator"><img src="https://agentmods.dev/badge/skills/on-deck-society/love-stack/prd-generator.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.03463 |
| Opus 5 | $0.00023 | $0.01732 |
| Sonnet 5 | $0.00009 | $0.00693 |
| Haiku 4.5 | $0.00005 | $0.00346 |
Grade A, and why
prd-generator 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 5d 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
100% identical to prd-generator — 0 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 — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Generator
Overview
Generate comprehensive, well-structured Product Requirements Documents (PRDs) that follow industry best practices. This skill helps product managers create clear, actionable requirements documents that align stakeholders and guide development teams.
Core Workflow
When a user requests to create a PRD (e.g., "create a PRD for a user authentication feature"), follow this workflow:
Step 1: Gather Context
Before generating the PRD, collect essential information through a discovery conversation:
Required Information:
- Feature/Product Name: What are we building?
- Problem Statement: What problem does this solve?
- Target Users: Who is this for?
- Business Goals: What are we trying to achieve?
- Success Metrics: How will we measure success?
- Timeline/Constraints: Any deadlines or limitations?
Discovery Questions to Ask:
1. What problem are you trying to solve?
2. Who is the primary user/audience for this feature?
3. What are the key business objectives?
4. Are there any technical constraints we should be aware of?
5. What does success look like? How will you measure it?
6. What's the timeline for this feature?
7. What's explicitly out of scope?
Note: If the user provides a detailed brief or requirements upfront, you can skip some questions. Always ask for clarification on missing critical information.
Step 2: Generate PRD Structure
Use the standard PRD template from references/prd_template.md to create a well-structured document. The PRD should include:
- Executive Summary - High-level overview (2-3 paragraphs)
- Problem Statement - Clear articulation of the problem
- Goals & Objectives - What we're trying to achieve
- User Personas - Who we're building for
- User Stories & Requirements - Detailed functional requirements
- Success Metrics - KPIs and measurement criteria
- Scope - What's in and out of scope
- Technical Considerations - Architecture, dependencies, constraints
- Design & UX Requirements - UI/UX considerations
- Timeline & Milestones - Key dates and phases
- Risks & Mitigation - Potential issues and solutions
- Dependencies & Assumptions - What we're relying on
- Open Questions - Unresolved items
What ships with it
1 file 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.
- 5d ago First seen · 477 lines · 47 tokens per session scan A 37024f403238
prd-generator is a skill published in the GitHub repository on-deck-society/love-stack (5 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 3,463 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prd-generator, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…