pm-skills-mcp: Instructions file for Codex

AGENTS.md

pm-skills-mcp AGENTS.md is an instructions file for Codex, OpenCode from product-on-purpose/pm-skills-mcp. It costs 1,109 tokens per session, scanned A, original, Apache-2.0.

Instructions for an MCP server that gives AI agents access to 24 product-management tools. Product management is the work of understanding customer problems, defining solutions, and deciding what a product should build.

In plain words
What is it for?
Use it to summarize user research, compare competitors, define problems and success measures, map opportunities, or create solution briefs.
Why use it?
It provides a structured way to turn interviews, stakeholder input, competitors, and hypotheses into product decisions and development plans.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md.

This is product-on-purpose/pm-skills-mcp's own configuration. It tells Codex and OpenCode how to work on pm-skills-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything pm-skills-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to product-on-purpose/pm-skills-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/product-on-purpose/pm-skills-mcp/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/product-on-purpose/pm-skills-mcp

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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Per session 1,109 This file is loaded in full into every session.
When invoked 1,109 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.01109 $0.01109
Opus 5 $0.00554 $0.00554
Sonnet 5 $0.00222 $0.00222
Haiku 4.5 $0.00111 $0.00111

Measured 11d ago against content hash f3cd52e8f498, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

pm-skills-mcp AGENTS.md 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 11d 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.

AGENTS.md · 185 lines

How it starts

The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PM-Skills MCP

MCP server exposing 24 professional PM skills as tools, resources, and prompts

This MCP server provides programmatic access to the PM-Skills library via the Model Context Protocol. AI agents can invoke skill tools, read skill resources, and use guided prompts.

Tools

Discover Phase

pm_interview_synthesis

Turn user research interviews into actionable insights, patterns, and recommendations.

pm_competitive_analysis

Create structured competitive analysis comparing features, positioning, and strategy.

pm_stakeholder_summary

Document stakeholder needs, concerns, and influence for a project or initiative.


Define Phase

pm_problem_statement

Create a clear problem framing document with user impact, business context, and success criteria.

pm_hypothesis

Define a testable hypothesis with clear success metrics and validation approach.

pm_opportunity_tree

Create an opportunity solution tree mapping outcomes to opportunities and solutions.

pm_jtbd_canvas

Create a Jobs to be Done canvas capturing functional, emotional, and social dimensions.


Develop Phase

pm_solution_brief

Create a concise one-page solution overview with approach, decisions, and trade-offs.

pm_spike_summary

Document results of a time-boxed technical or design exploration (spike).

pm_adr

Create an Architecture Decision Record following the Nygard format.

pm_design_rationale

Document reasoning behind design decisions including alternatives and trade-offs.


Deliver Phase

pm_prd

Create a comprehensive Product Requirements Document for engineering handoff.

pm_user_stories

Generate user stories with clear acceptance criteria from product requirements.

pm_edge_cases

Document edge cases, error states, boundary conditions, and recovery paths.

pm_launch_checklist

Create comprehensive pre-launch checklist covering all readiness areas.

Read the full file on GitHub · 185 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. 11d ago First seen · 185 lines · 1,109 tokens per session scan A f3cd52e8f498

Subscribe to this mod's changes

pm-skills-mcp AGENTS.md is an instructions file published in the GitHub repository product-on-purpose/pm-skills-mcp (20 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 1,109 tokens to every session, about $0.0055 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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