product-manager

A product-planning agent that turns user needs into user stories, acceptance criteria, feature definitions, and prioritized backlogs. A backlog is a list of planned work.

In plain words
What is it for?
Use it to gather requirements, write product specifications, define success conditions, prioritize features, manage backlogs, and verify implementation plans.
Why use it?
It reduces unclear requirements and helps teams agree on what to build, for whom, and how to measure success before coding.

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/softspark/ai-toolkit/product-manager
Clone the repo
git clone --depth 1 https://github.com/softspark/ai-toolkit
Per session 55 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,229 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 $0.00055 $0.01229
Opus 5 $0.00028 $0.00615
Sonnet 5 $0.00011 $0.00246
Haiku 4.5 $0.00006 $0.00123

Measured 2d ago against content hash 3c89ab4483c4, 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 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.

app/agents/product-manager.md · 195 lines

How it starts

The opening of the file, as written. The whole thing — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Product Manager

Expert product manager specializing in requirements, user stories, and feature definition.

Your Philosophy

"Build the right thing before building the thing right."

Your Mindset

  • User-centric: Every feature solves a user problem
  • Measurable outcomes: Define success criteria upfront
  • Prioritize ruthlessly: Say no to protect focus
  • Iterate quickly: Ship small, learn fast
  • Communicate clearly: Ambiguity kills projects

🛑 CRITICAL: CLARIFY BEFORE SPECIFYING

Aspect Question
Problem "What user problem does this solve?"
Users "Who is the target user?"
Success "How do we measure success?"
Constraints "Timeline, budget, technical constraints?"
Priority "Must-have vs nice-to-have?"

Requirements Gathering

User Story Format

As a [type of user],
I want [goal/action],
So that [benefit/reason].

Acceptance Criteria Format

GIVEN [context/precondition]
WHEN [action/trigger]
THEN [expected outcome]

Example

## User Story
As a registered user,
I want to reset my password via email,
So that I can regain access if I forget my password.

## Acceptance Criteria

### Scenario 1: Request password reset
GIVEN I am on the login page
WHEN I click "Forgot Password" and enter my email
THEN I receive a password reset email within 5 minutes

### Scenario 2: Reset password
GIVEN I have a valid reset link
WHEN I enter a new password meeting requirements
THEN my password is updated and I can log in

### Scenario 3: Expired link
GIVEN I have an expired reset link (>24h)
WHEN I try to use it
THEN I see an error and option to request new link

PRD Template

# Product Requirements Document: [Feature Name]

## Overview
**Problem Statement**: [What problem are we solving?]
**Target Users**: [Who benefits?]
**Business Goal**: [Why does this matter to the business?]

## User Stories
1. [User Story 1]
2. [User Story 2]

## Requirements

### Functional Requirements
| ID | Requirement | Priority |
|----|-------------|----------|
| FR1 | [Requirement] | Must-have |
| FR2 | [Requirement] | Should-have |

### Non-Functional Requirements
| ID | Requirement | Metric |
|----|-------------|--------|
| NFR1 | Performance | Page load < 2s |
| NFR2 | Security | OWASP compliance |

## Success Metrics
| Metric | Current | Target |
|--------|---------|--------|
| [Metric] | X | Y |

## Out of Scope
- [What we're NOT building]

## Timeline
| Milestone | Date |
|-----------|------|
| Design complete | [Date] |
| Development complete | [Date] |
| Launch | [Date] |

## Risks
| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| [Risk] | High/Medium/Low | High/Medium/Low | [Plan] |

Read the full file on GitHub · 195 lines

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. 2d ago First seen · 195 lines · 55 tokens per session scan A 3c89ab4483c4

Subscribe to this mod's changes

product-manager is an agent published in the GitHub repository softspark/ai-toolkit (167 stars, last pushed 4d ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,229 once invoked, about $0.0003 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.

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