PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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 mohitagw15856/pm-claude-skills --skill ai-feature-prdgit clone --depth 1 https://github.com/mohitagw15856/pm-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/mohitagw15856/pm-claude-skills/ai-feature-prd)<a href="https://agentmods.dev/skills/mohitagw15856/pm-claude-skills/ai-feature-prd"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-feature-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/mohitagw15856/pm-claude-skills/ai-feature-prd"><img src="https://agentmods.dev/badge/skills/mohitagw15856/pm-claude-skills/ai-feature-prd.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 | $0.00100 | $0.01068 |
| Opus 5.5 | $0.00040 | $0.00427 |
| Sonnet 5.5 | $0.00020 | $0.00214 |
| Haiku 4.5 | $0.00010 | $0.00107 |
Grade A, and why
ai-feature-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 2d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Feature PRD Skill
AI features break the normal PRD because the system is probabilistic: it will be wrong sometimes, and the product must be designed around that, not in denial of it. This skill extends a standard PRD with the AI-specific sections that decide whether the feature is trustworthy — the UX of uncertainty, the eval bar, guardrails, and what happens when the model is wrong.
Required Inputs
Ask for these only if they aren't already provided:
- The user problem and why an AI/probabilistic approach fits it (vs. deterministic rules).
- What "good" looks like to the user, and the cost of a wrong answer (low-stakes vs. high-stakes).
- Inputs available — context/data the model can use; privacy constraints.
- Trust level needed — can the user verify the output, or must it be near-perfect?
Reads from / Writes to the Brain
If a professional-brain exists, read context.md (product, users, voice)
and knowledge/strategy.md first; write the feature to entities/ and any scoping decision to decisions/,
each provenance-tagged.
Output Format
AI Feature PRD: [feature]
1. Problem & why AI — the user problem, and why a model (not rules) is the right tool. If rules would do, say so.
2. Experience — the core flow, and crucially the UX of uncertainty: how confidence is shown, how the user verifies/edits, and how errors are made cheap to recover from. AI features live or die here.
3. Model approach — prompt / fine-tune / RAG / agent (link rag-design-doc or agent-spec), the model tier, and why.
4. Quality bar & evaluation — the metrics and the explicit ship threshold; reference an ai-eval-plan. State the acceptable error rate given the stakes.
5. Guardrails & safety — what the feature must never do, input/output filtering, and handling of harmful/PII/out-of-scope inputs.
6. Fallback behaviour — what happens when the model is unsure, wrong, slow, or down: graceful degradation, "I'm not sure" states, human handoff. No silent confident errors.
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.
- 2d ago First seen · 76 lines · 100 tokens per session scan A 5ec4c5213c91
ai-feature-prd is a skill published in the GitHub repository mohitagw15856/pm-claude-skills (1,449 stars, last pushed today), licensed MIT. It adds 100 tokens to every session and 1,068 once invoked, about $0.0004 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 2026-10-12.
Other skills, from other repositories
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…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…