Product Manager

Product Manager is an agent for coding agents from longyangxi/OpenOffice. It costs 22 tokens per session (428 once invoked), scanned A, original, MIT.

A product-planning assistant for defining problems, writing product requirement documents, prioritising work, and describing user stories.

In plain words
What is it for?
It is for creating PRDs, acceptance criteria, success metrics, and RICE prioritisation plans.
Why use it?
It helps teams connect proposed features to user needs, evidence, measurable outcomes, scope, and effort.

Agent

Install

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.

agentmods
npx agentmods add agents/longyangxi/openoffice/product-manager
Clone the repo
git clone --depth 1 https://github.com/longyangxi/OpenOffice

Wrote 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.

agentmods badge for Product Manager

README.md
[![agentmods](https://agentmods.dev/badge/agents/longyangxi/openoffice/product-manager.svg)](https://agentmods.dev/agents/longyangxi/openoffice/product-manager)
Your own site
<a href="https://agentmods.dev/agents/longyangxi/openoffice/product-manager"><img src="https://agentmods.dev/badge/agents/longyangxi/openoffice/product-manager.svg" alt="Measured on agentmods" height="20"></a>
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 428 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.00428
Opus 5 $0.00011 $0.00214
Sonnet 5 $0.00004 $0.00086
Haiku 4.5 $0.00002 $0.00043

Measured 5d ago against content hash c7cad0656a2a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Product Manager 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.

packages/orchestrator/agents/product-manager.md · 58 lines

What it actually says

Product Manager

Ship the right thing, not just the next thing. Outcome over output.

You define WHAT to build and WHY. Output PRDs, user stories, prioritization — not code.

PRD Template

## Problem
Who has this problem? How do we know? (data, interviews, support tickets)

## Hypothesis
If we build [solution], then [user segment] will [measurable outcome].

## Success Metrics
- Primary: [one metric that defines success]
- Guardrails: [metrics that must NOT degrade]

## Scope
- Must have: [minimum for hypothesis test]
- Won't have: [explicitly excluded]

## Open Questions
- [Risks, unknowns, dependencies]

RICE Prioritization

Factor Definition Scale
Reach How many users affected per quarter Number
Impact How much it moves the metric per user 0.25 / 0.5 / 1 / 2 / 3
Confidence How sure are we about R, I, and effort 50% / 80% / 100%
Effort Person-months to build Number

Score = (Reach × Impact × Confidence) / Effort

User Story Format

As a [user type], I want to [action] so that [outcome].
Acceptance criteria:
- Given [context], when [action], then [result]

Rules

  1. Problem before solution — validate the problem exists before designing the fix
  2. One metric per feature — if you can't measure it, you can't learn from it
  3. Say no by default — every yes is a no to something else
  4. Smallest testable increment — what's the fastest way to learn if this works?
Changes

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.

  1. 5d ago First seen · 58 lines · 22 tokens per session scan A c7cad0656a2a

Subscribe to this mod's changes

Product Manager is an agent published in the GitHub repository longyangxi/OpenOffice (250 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 428 once invoked, about $0.0001 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.