AN

analyze-requirements

Converts feature ideas into formal User Stories and Product Backlog Items by analyzing the codebase for feasibility.

Install

mkdir -p .claude/skills/analyze-requirements && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/15361" && unzip -o skill.zip -d .claude/skills/analyze-requirements && rm skill.zip

Installs to .claude/skills/analyze-requirements

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Requirements analysis and PBI writing workflow. Transforms user feature requests into structured User Stories with acceptance criteria, PBI breakdowns with story point estimates, impact assessments, and dependency maps. Analyzes actual codebase for feasibility and reuse opportunities. Outputs polished markdown requirements, using specs/<feature-id>-<slug>/spec.md as the canonical home for non-trivial work and docs/requirements/ only for backlog-intake artifacts. Use when asked to write a story, create PBIs, analyze requirements, or plan a feature.
553 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Capture user intent for feature requests
  • Analyze codebase for reuse opportunities and dependencies
  • Decompose stories into implementable PBIs
  • Assess impact of new features
  • Choose canonical output location for requirements

How it works

This skill transforms user feature requests into structured requirements by capturing intent, analyzing the codebase for feasibility, writing user stories, decomposing them into PBIs, and assessing impact.

Inputs & outputs

You give it
a user feature request or idea
You get back
structured user stories, PBIs, and an impact assessment in markdown

When to use analyze-requirements

  • Formalizing feature requests
  • Planning sprints
  • Refining backlog items

About this skill

Analyze Requirements — From Idea to Structured PBIs

Transform vague feature ideas into structured, implementable requirements by combining user intent with actual codebase analysis.

When to Use

  • User describes a feature idea and wants it formalized as a Story/PBI
  • User asks "I want to add X" or "we need Y functionality"
  • Backlog grooming — refining rough items into implementable PBIs
  • Requirements clarification before sprint planning
  • Keywords: "write story", "create PBI", "analyze requirement", "plan feature", "define scope"

Workflow

Step 1: Capture Intent

From the user's input, extract:

  • What they want (capability)
  • Who it's for (persona)
  • Why it matters (business value)
  • Where it fits (module, screen, API)
  • Ask clarifying questions if any of these are unclear

Step 2: Codebase Analysis

Always analyze the codebase before writing requirements.

  1. Search for related entities, services, endpoints
  2. Identify existing components that can be reused
  3. Find existing patterns to follow
  4. Map affected modules and dependencies
  5. Check for conflicting business rules

Output a reuse assessment table:

ComponentExists?Action Needed
[Entity]✅ YesExtend with new fields
[Service]✅ YesAdd new method
[Endpoint]❌ NoCreate new
[Tests]✅ PartialAdd new scenarios

Step 3: Write User Story

Use standard format:

  • As a [persona] I want [capability] So that [benefit]
  • Acceptance Criteria in Given/When/Then format
  • Mark priority: Must Have / Should Have / Could Have
  • Define what's Out of Scope

Step 4: PBI Decomposition

Break the story into implementable PBIs:

  • Each PBI ≤ 8 story points (split larger ones)
  • Each PBI has: description, acceptance criteria, technical notes, DoD
  • Map dependencies between PBIs
  • Assign story points based on actual codebase complexity

Step 5: Impact Assessment

Produce:

  • Affected areas table (🔴🟡🟢 impact levels)
  • PBI dependency graph
  • Risk assessment with mitigations
  • Estimation summary

Step 6: Choose the Canonical Output

Use one canonical home for the result:

  • Non-trivial, multi-module, or requirement-heavy work: save or promote the output into specs/<feature-id>-<slug>/spec.md so downstream review, planning, and implementation use the same artifact.
  • Backlog intake or pre-spec triage only: save to docs/requirements/[feature-name]-requirements.md when the repo explicitly wants a lightweight intake artifact.

If both files exist, state plainly that specs/<feature-id>-<slug>/spec.md is canonical and docs/requirements/ is only a summary or intake note.

Validation

  • Every user story has acceptance criteria
  • Every PBI has Definition of Done
  • Story points are realistic (based on codebase analysis)
  • Dependencies are clearly mapped
  • Out-of-scope items are explicitly listed
  • Technical notes reference actual file paths
  • Canonical artifact home is identified explicitly
  • Output is saved as markdown file

When not to use it

  • When the feature idea is already fully formalized
  • When the task is only about typo fixes or formatting edits
  • When the codebase analysis is not required

Limitations

  • Each PBI must be ≤ 8 story points
  • Every user story must have acceptance criteria
  • Every PBI must have a Definition of Done

How it compares

This skill integrates codebase analysis directly into the requirements writing process, ensuring feasibility and identifying reuse opportunities, unlike purely abstract requirements gathering.

Compared to similar skills

analyze-requirements side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
analyze-requirements (this skill)03moReviewIntermediate
pmbok-project-management389moNo flagsIntermediate
project-planner3210moReviewIntermediate
spec-kit-workflow118moNo flagsIntermediate

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Example prompts that trigger this skill in your AI assistant.

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