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 pmprompt/claude-plugin-product-management --skill agent-spec-generatorgit clone --depth 1 https://github.com/pmprompt/claude-plugin-product-managementWrote 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/pmprompt/claude-plugin-product-management/agent-spec-generator)<a href="https://agentmods.dev/skills/pmprompt/claude-plugin-product-management/agent-spec-generator"><img src="https://agentmods.dev/badge/skills/pmprompt/claude-plugin-product-management/agent-spec-generator/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/pmprompt/claude-plugin-product-management/agent-spec-generator"><img src="https://agentmods.dev/badge/skills/pmprompt/claude-plugin-product-management/agent-spec-generator.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 | $0.00071 | $0.02669 |
| Opus 5.5 | $0.00028 | $0.01068 |
| Sonnet 5.5 | $0.00014 | $0.00534 |
| Haiku 4.5 | $0.00007 | $0.00267 |
Grade A, and why
agent-spec-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 yesterday.
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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Context
This skill implements a proven product management framework. The approach combines best practices from industry leaders and is designed for practical application in day-to-day PM work.
Input Requirements
- Context about your product, feature, or problem
- Relevant data, research, or constraints (recommended but optional)
- Clear articulation of what you're trying to achieve
Agent Spec Generator
An agent is software that decides its own next step and then takes it. That makes the spec different from a PRD: the hard part is not the screens, it is deciding what the agent is allowed to do without a person, when it must stop, and how you will know it is behaving. This skill interviews you first, then fills in a spec template that an engineering team can build against and a reviewer can sign.
When to Use
- You are scoping a new AI agent (support, ops, sales, coding, research, internal tooling)
- An existing assistant is about to gain the ability to take actions, not just answer
- Security, legal, or leadership asks "what exactly can this thing do on its own?"
- You need a shared definition of done before engineering starts
- An agent misbehaved in production and you need to tighten its boundaries
What This Skill Does
Asks a short set of interview questions, then produces a fillable agent spec covering purpose and scope, users, tools and permissions, autonomy levels with criteria, approval boundaries, stop rules, escalation paths, memory and state, data access, failure modes and fallbacks, must-never-do rules, evals and acceptance criteria, observability, and rollout. Gaps are marked as open questions instead of guessed.
Instructions
Step 1 — Interview first
Ask these before writing anything. Group them, keep it to one round if you can, and accept "don't know yet" as an answer (it becomes an open question in the spec).
Job and scope
- What job does the agent do, in one sentence, and for whom?
- What does a successful run look like? What does the user have at the end?
- What is explicitly out of scope, even if the agent could technically do it?
- What happens today without the agent (manual process, existing tool, nothing)?
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.
- yesterday First seen · 215 lines · 71 tokens per session scan A 71bcbea9ca80
agent-spec-generator is a skill published in the GitHub repository pmprompt/claude-plugin-product-management (53 stars, last pushed 2d ago), licensed MIT. It adds 71 tokens to every session and 2,669 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 2026-10-23.
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