requirements-analysis — community requirements-analysis, pigeon-pod, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
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About this Skill

Ideal for Development Agents requiring streamlined project analysis and goal alignment, such as Cursor or AutoGPT. Analyze product/feature requirements for the PigeonPod project with software engineering rigor. Use when users ask to evaluate a requirements value, feasibility, architecture fit, implementation impac

aizhimou aizhimou
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Updated: 3/12/2026

Agent Capability Analysis

The requirements-analysis skill by aizhimou is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Ideal for Development Agents requiring streamlined project analysis and goal alignment, such as Cursor or AutoGPT.

Core Value

Empowers agents to analyze requirements against project goals and architecture using MCP/Context7, defining expected user and business value while identifying requirement types like feature, enhancement, or bugfix.

Capabilities Granted for requirements-analysis

Analyzing requirements against PigeonPod goals
Identifying and categorizing requirement types
Defining expected user and business value for project enhancements

! Prerequisites & Limits

  • Requires access to project documentation and code
  • Dependent on MCP/Context7 for dependency and API constraint confirmation
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SKILL.md
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Requirements Analysis

Analyze requirements against PigeonPod goals, current architecture, and implementation reality.

Follow This Workflow

  1. Restate the requirement in one short paragraph.
  2. Identify requirement type: feature, enhancement, bugfix, non-functional, integration, or migration.
  3. Define expected user value and business value.
  4. Read relevant project docs and code before giving conclusions.
  5. Use MCP/Context7 to confirm dependency or API constraints when external libraries/services are involved.
  6. Evaluate architecture fit, implementation complexity, data impact, and operational impact.
  7. Propose an implementation strategy with phased scope (MVP, next, later).
  8. Output a decision with explicit rationale and open questions.

Read Local Context First

Prioritize these files for PigeonPod:

  • README.md
  • dev-docs/architecture/architecture-design-en.md
  • backend/src/main/resources/application.yml
  • backend/src/main/resources/db/migration/*.sql
  • Relevant backend packages under backend/src/main/java/top/asimov/pigeon/
  • Relevant frontend routes/components under frontend/src/pages/ and frontend/src/components/

Use fast discovery commands when needed:

bash
1rg -n "keyword|concept|module" backend/src/main/java frontend/src dev-docs README.md 2rg --files dev-docs/

Use Context7 and MCP Deliberately

Use Context7/MCP when the requirement depends on framework/library/service behavior, version constraints, configuration, or integration details.

Typical triggers:

  • Spring Boot/MyBatis-Plus/Sa-Token behavior or config decisions
  • React/Mantine/React Router/i18next/Axios constraints
  • YouTube Data API v3 limits/quotas/contract details
  • RSS/Podcasting namespace compatibility details
  • yt-dlp options/behavior and compatibility implications

Rules:

  • Resolve library ID first, then query focused questions.
  • Prefer primary/official docs and version-aware guidance.
  • Distinguish facts from inference.
  • If docs conflict with local implementation, prioritize local code reality and call out the gap.

Evaluate With These Dimensions

Assess each dimension explicitly:

  1. Value Alignment: Match with PigeonPod core goals (YouTube-to-podcast conversion, auto-sync/download, feed usability, operations simplicity).
  2. Feasibility: Confirm technical possibility with current stack and constraints.
  3. Architecture Fit: Check compatibility with backend service boundaries, scheduler/event flow, DB schema, and frontend route/state model.
  4. Data and Migration Impact: Identify new fields/tables, migration requirements, backfill, and backward compatibility.
  5. API and Contract Impact: Identify REST/RSS contract changes and consumer compatibility risks.
  6. Security and Compliance: Review auth, permissions, secrets/API keys, abuse vectors.
  7. Performance and Cost: Estimate queue pressure, I/O/download load, external API quota consumption, and storage growth.
  8. Testability and Operability: Define unit/integration/e2e coverage and monitoring/logging needs.

Produce This Output Format

Use this structure in final analysis:

markdown
1## Requirement Summary 2- User request: 3- Requirement type: 4- Assumptions: 5 6## Value Assessment 7- User value: 8- Product/business value: 9- Priority suggestion: High/Medium/Low 10 11## Feasibility and Architecture Fit 12- Current touchpoints: 13- Proposed changes: 14- Architecture fit verdict: Good/Partial/Poor 15 16## Impact Analysis 17- Backend impact: 18- Frontend impact: 19- Database/migration impact: 20- External dependency impact: 21- Security/performance/ops impact: 22 23## Delivery Plan 24- MVP scope: 25- Non-MVP scope: 26- Estimated complexity: S/M/L/XL 27- Key risks and mitigations: 28 29## Decision 30- Recommendation: Proceed / Proceed with constraints / Defer / Reject 31- Reasoning: 32- Open questions:

Decision Heuristics

  • Recommend Proceed when value is clear, fit is good, and risk is manageable.
  • Recommend Proceed with constraints when value is high but scope/risk needs staged delivery.
  • Recommend Defer when value exists but prerequisites are missing.
  • Recommend Reject when requirement conflicts with core goals or creates disproportional cost/risk.

Quality Bar

Before finalizing, verify all checks:

  • Base conclusions on repository evidence, not assumptions only.
  • Confirm external-library/API claims through Context7/MCP when relevant.
  • Separate facts, assumptions, and unknowns.
  • Include at least one feasible implementation path.
  • Include explicit tradeoffs and rollback/fallback considerations.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is requirements-analysis?

Ideal for Development Agents requiring streamlined project analysis and goal alignment, such as Cursor or AutoGPT. Analyze product/feature requirements for the PigeonPod project with software engineering rigor. Use when users ask to evaluate a requirements value, feasibility, architecture fit, implementation impac

How do I install requirements-analysis?

Run the command: npx killer-skills add aizhimou/pigeon-pod/requirements-analysis. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for requirements-analysis?

Key use cases include: Analyzing requirements against PigeonPod goals, Identifying and categorizing requirement types, Defining expected user and business value for project enhancements.

Which IDEs are compatible with requirements-analysis?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for requirements-analysis?

Requires access to project documentation and code. Dependent on MCP/Context7 for dependency and API constraint confirmation.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add aizhimou/pigeon-pod/requirements-analysis. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use requirements-analysis immediately in the current project.

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