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Requirements Analysis

Checked
jwynia·vlatest·other

Analyzes user requests to extract key needs, constraints, and desired outcomes for clear project scoping.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jwynia/agent-skills --skill requirements-analysis --full-depth -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jwynia

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 jwynia/agent-skills at e02ec7e226.

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

Commit
e02ec7e226
Manifest
skills/tech/development/architecture/requirements-analysis/SKILL.md
Scanned
4 files, 539 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

The requirements-analysis skill helps AI agents identify, organize, and clarify user needs for a project or task. It enables structured analysis of functional and non-functional requirements, ensuring clarity and completeness in defining objectives.

When to use it

  • When starting a new project and needing to gather initial user requirements
  • When refining ambiguous or incomplete specifications provided by stakeholders
  • When preparing documentation for development teams or clients

Key capabilities

  • Extracting functional and non-functional requirements from user input
  • Organizing requirements into structured formats like user stories or use cases
  • Identifying gaps, ambiguities, or inconsistencies in the provided information

Example prompts

  • "Analyze this project description and list all key requirements."
  • "Break down these user needs into clear functional and non-functional categories."
  • "Identify any missing details in this specification and suggest clarifications."

Tips & gotchas

  • Provide as much context as possible for accurate analysis.
  • This skill works best with textual input; complex diagrams or workflows may require additional tools.