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 vinnie357/claude-skills --skill prdgit clone --depth 1 https://github.com/vinnie357/claude-skillsWrote 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/vinnie357/claude-skills/prd)<a href="https://agentmods.dev/skills/vinnie357/claude-skills/prd"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/prd/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/vinnie357/claude-skills/prd"><img src="https://agentmods.dev/badge/skills/vinnie357/claude-skills/prd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00066 | $0.01127 |
| Opus 5.5 | $0.00026 | $0.00451 |
| Sonnet 5.5 | $0.00013 | $0.00225 |
| Haiku 4.5 | $0.00007 | $0.00113 |
Grade A, and why
prd 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 2mo 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PRD Authoring
Author a Product Requirements Document that serves as the handoff contract between product intent and an implementing team — human or AI agent. The PRD states WHAT the feature does and WHY it exists; it never prescribes HOW to build it.
PRD as Contract
A PRD is complete when the implementing team needs no follow-up questions to start work. Treat every unanswerable question as a defect in the document, not a gap the implementer fills in.
Non-goals are as firm as goals. State exclusions positively and explicitly — "does not support X" — never by silent omission. An agent implementer cannot infer a boundary from absence: omitting "do not implement X" from the PRD is how an agent ends up building X. A human team asks follow-up questions; an agent team does not, so write for the stricter reader.
The PRD stays at the WHAT/WHY altitude. A PRD is not a software requirements specification. Writing a PRD ensures user-centric context; a separate, downstream implementation spec — authored by a PM or engineer AFTER this PRD, never inside it — is where SRS-level HOW-level agent operating instructions (file layout, framework choice, API contracts) belong.
Completeness test: before calling a PRD done, walk every section and confirm a different engineering team with a different tech stack could implement it without contacting the author.
Two Modes
/pm:prd runs pm-prd-author in one of two modes:
- Interactive (default) — the
/pm:prdcommand session runs the questionnaire (problem, users, goals, non-goals, stories, constraints, phasing) viaAskUserQuestion, since the spawnedpm-prd-authoragent has no channel to the operator. The agent then composes the PRD from the collected answers, each tagged[operator-stated]. - Harvest-grounded (
--inventory=<path>) — the feature inventory from/pm:spec-harvestis the grounding document. One PRD is authored per major feature area named in the inventory; requirements are lifted from inventory entries and carry their original confidence tags. Sections the inventory cannot answer become Open Questions rows rather than invented content.
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.
- 2mo ago First seen · 80 lines · 66 tokens per session scan A c0d674fa2e1d
prd is a skill published in the GitHub repository vinnie357/claude-skills (27 stars, last pushed 14d ago), licensed MIT. It adds 66 tokens to every session and 1,127 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
prd compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| prdvinnie357/claude-skills | Skill | 27 | 14d ago | MIT | A | 66 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 2d ago | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | today | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 6d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |