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Problem Statement

Checked
product-on-purpose·v1.0.0·backend·Apache-2.0

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->

Install on your platform

Showing OpenClaw.

  1. 1

    Run this in your terminal

    terminal
    $

What we know about this skill

Publisher

product-on-purpose

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned product-on-purpose/pm-skills at 1cef1a9eae.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
1cef1a9eae
Manifest
skills/define-problem-statement/SKILL.md
Scanned
6 files, 405 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill helps AI agents articulate a clear and concise problem statement for a product or feature. It guides the agent to define the user, their need, the impact of not addressing that need, and ultimately frames the problem in a way that informs solution design. The output is intended to be actionable and focused on the core issue being solved.

When to use it

  • New Product Ideas: Before starting development, use this skill to ensure everyone understands what problem you're solving.
  • Feature Prioritization: When deciding which features to build next, a well-defined problem statement clarifies their value and impact.
  • Alignment Across Teams: Facilitate shared understanding among engineering, design, and product teams by establishing a common problem definition.
  • Reframing Existing Issues: If a project is struggling or producing unexpected results, this skill can help re-examine the underlying problem.

Key capabilities

  • Defines the user experiencing the problem.
  • Articulates the specific need of the user.
  • Identifies the negative consequences of not addressing the problem.
  • Creates a concise and actionable problem statement.

Example prompts

  • "I want to define the problem for a mobile app that helps users track their daily water intake."
  • "Generate a problem statement for a feature allowing customers to easily return online purchases."
  • "Help me frame the problem we're trying to solve with our new CRM system."

Tips & gotchas

The quality of the output depends heavily on the initial information provided. Be as specific as possible about the user and their situation when providing context to the AI agent.