Elicit comprehensive software requirements through AI-powered workflows: conduct or simulate stakeholder interviews, extract from documents/transcripts, perform domain research, detect gaps, prioritize via MoSCoW/Kano/WSJF, map user stories/journeys, analyze JTBD/business rules, run surveys/workshops, and export to YAML/Gherkin/EARS formats.
npx claudepluginhub melodic-software/claude-code-plugins --plugin requirements-elicitationPROACTIVELY use. Extract requirements from documents including PDFs, specifications, meeting transcripts, and web content. Uses pattern matching and NLP to identify requirement candidates.
PROACTIVELY use. Analyze requirements for completeness using domain checklists, NFR categories, and INVEST criteria. Identifies missing requirements and recommends elicitation techniques to fill gaps.
PROACTIVELY use when creating customer journey maps from elicited requirements. Analyzes synthesis data, interview transcripts, and stakeholder perspectives to generate comprehensive journey visualizations with emotional curves, pain points, and opportunities. Outputs Mermaid diagrams.
PROACTIVELY use when assessing requirement quality. Evaluates requirements against 6Cs criteria (Clear, Concise, Complete, Consistent, Correct, Confirmable) and INVEST principles. Returns quality scores with improvement recommendations.
PROACTIVELY use. AI-led requirements elicitation interviewer using LLMREI research-backed patterns. Conducts structured stakeholder interviews with context-adaptive questioning.
PROACTIVELY use when consolidating requirements from multiple sources. Synthesizes requirements from interviews, documents, simulations, and research into unified requirement sets. Resolves conflicts, identifies patterns, and produces pre-canonical output.
PROACTIVELY use. Unified simulated stakeholder for requirements elicitation. Supports persona types: business, compliance, end-user, operations, technical. Pass persona type as argument.
Analyze meeting transcripts to extract requirements, decisions, and action items. Specialized extraction for conversational content.
Facilitate AI-assisted brainstorming sessions for requirements discovery. Uses divergent-convergent thinking patterns to generate ideas, then filters and refines them. Supports multiple brainstorming techniques.
Business rules elicitation and analysis techniques. Covers rule types (constraints, derivations, inferences), decision tables, rule templates, and policy documentation. Use when identifying business policies, constraints, calculations, and decision logic during requirements elicitation.
Main orchestrator for full requirements elicitation workflow. Coordinates interviews, document extraction, simulation, research, and synthesis.
Extract requirements from existing documents including PDFs, Word docs, meeting transcripts, specifications, and web content. Identifies requirement candidates, categorizes them, and outputs in pre-canonical format.
MCP-powered domain research for requirements elicitation. Uses perplexity, context7, firecrawl, and other MCP servers to research domain knowledge, best practices, and industry requirements.
Hub skill for requirements elicitation. Provides technique selection, orchestration guidance, LLMREI patterns, and autonomy level configuration. Use when gathering requirements from stakeholders, conducting elicitation sessions, or preparing requirements for specification.
Export elicited requirements to specification formats. Supports canonical, EARS, Gherkin, and other formats.
Extract requirements from documents (PDF, Markdown, Word, URLs). Identifies requirement candidates and outputs in pre-canonical format.
Analyze requirements for completeness, missing areas, and gaps. Uses domain checklists, NFR categories, and INVEST criteria to identify what's missing from elicited requirements.
Analyze current requirements for completeness and identify missing areas. Uses domain checklists, NFR categories, and INVEST criteria.
AI-led stakeholder interviews using LLMREI research-backed patterns. Conducts structured interviews to elicit requirements through context-adaptive questioning, active listening, and systematic requirement extraction.
Conduct an AI-led requirements elicitation interview with a stakeholder. Uses LLMREI research-backed patterns for effective requirement capture.
Create a customer journey map from elicited requirements. Visualizes end-to-end user experience with stages, touchpoints, emotions, and pain points. Outputs Mermaid diagrams.
Jobs to Be Done (JTBD) framework by Clayton Christensen. Analyzes requirements through the lens of what 'job' customers hire products to do. Covers functional, emotional, and social jobs. Use when understanding underlying customer motivations or reframing features as outcomes.
Requirements prioritization techniques including MoSCoW, Kano Model, WSJF (SAFe), and Wiegers' Value/Cost/Risk matrix. Provides scoring frameworks, trade-off analysis, and priority visualization. Use when ranking requirements by business value, customer impact, or implementation efficiency.
Apply prioritization methods to elicited requirements. Supports MoSCoW, Kano Model, WSJF, and Wiegers' scoring. Outputs ranked requirements with justification.
Research domain knowledge using MCP servers (perplexity, context7, firecrawl). Gathers best practices, regulatory requirements, and competitive insights.
Run multi-stakeholder simulation to generate requirements from diverse perspectives. Simulates End User, Technical, Business, Compliance, and Operations stakeholders.
Multi-persona stakeholder simulation for solo requirements work. Generates diverse perspectives from simulated End User, Technical, Business, Compliance, and Operations stakeholders when real stakeholders are unavailable.
Create a user story map from elicited requirements. Visualizes user journey with backbone activities, walking skeleton, and release slices. Outputs Mermaid diagrams.
Generate stakeholder surveys for requirements gathering and analyze responses. Creates questionnaires based on domain context, collects structured feedback, and produces statistical analysis with requirement candidates.
Use Case 2.0 methodology by Ivar Jacobson. Covers use case slices, lightweight documentation, user story derivation, and value-driven prioritization. Modern approach to use case modeling for agile teams.
Jeff Patton's User Story Mapping technique for Agile discovery. Visualizes user journey as a map, identifies backbone activities, walking skeleton, and release slices. Use when organizing requirements into deliverable increments or defining MVP scope.
Facilitate structured requirements workshops (JAD-style). Guides through agenda, captures decisions, resolves conflicts, and produces consolidated requirements. Supports multiple workshop formats.
Uses power tools
Uses Bash, Write, or Edit tools
Share bugs, ideas, or general feedback.
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Sign in to claimProblem-Based Software Requirements Specification methodology for AI-assisted requirements engineering. Provides structured approach to capture Customer Problems (WHY), Customer Needs (WHAT), and Functional Requirements (HOW) with full traceability.
Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear or features are complex.
Specification-driven development with EARS, Gherkin, and multi-provider support. Covers requirements authoring, BDD workflows, SpecKit integration, Kiro compatibility, ADRs, and user story management.
AI-augmented Innovation & Development Workflow: V-Model phases from business analysis to security audit
Spec-driven development methodology for Claude Code. Provides skills for requirements engineering (EARS format), design documentation, task breakdown, AI prompting strategies, quality assurance, and troubleshooting.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Plugins for Claude Code: documentation management, code quality, and ecosystem support.
fnm (Fast Node Manager) is the recommended Node.js version manager for this project. It:
Install fnm:
# Windows (PowerShell as Admin)
winget install Schniz.fnm
# macOS/Linux
curl -fsSL https://fnm.vercel.app/install | bash
Configure for Git Bash (add to ~/.bashrc):
eval "$(fnm env --use-on-cd --shell bash)"
Or source the setup script which includes fnm initialization:
source "/path/to/claude-code-plugins/setup/bashrc-claude.sh"
Install Node:
fnm install 24
fnm default 24
npm install
npm run lint:md # Check for errors
npm run lint:md:fix # Auto-fix errors
Markdown linting runs automatically on PRs via GitHub Actions. The same rules apply locally and in CI.
/plugin install claude-ecosystem@claude-code-plugins
/plugin install code-quality@claude-code-plugins
/plugin install google-ecosystem@claude-code-plugins
This repo expects Codex CLI configuration to live in user scope under ~/.codex.
See .codex/README.md for the canonical locations.
| Plugin | Purpose |
|---|---|
| atlassian | Atlassian MCP server: Jira, Confluence, Compass integration |
| browser-automation | Browser automation MCP servers: Chrome DevTools, Playwright |
| business-analysis | BABOK techniques: capability mapping, stakeholder analysis, value streams, journey mapping |
| ci-cd | CI/CD pipelines: GitHub Actions, deployment automation, release management |
| claude-code-observability | Event logging, metrics, session diagnostics |
| claude-ecosystem | Claude Code docs, meta-skills, hooks, observability, auditors |
| code-quality | Code review, markdown linting, debugging, CI/CD templates |
| compliance-planning | Regulatory compliance: GDPR, HIPAA, PCI-DSS, AI governance, ISO 27001 |
| content-management-system | Headless CMS architecture: content modeling, taxonomies, media, theming |
| cursor-ecosystem | Cursor IDE docs, CLI, agent, keyword-based search |
| documentation-standards | Technical docs: arc42, C4 model, ADRs, RFC process, docs-as-code |
| dotnet | .NET 10+ automation: build, clean, SDK/tool install, version upgrades, Aspire MCP |
| duende-ecosystem | Duende IdentityServer, BFF, IdentityModel docs |
| enterprise-architecture | TOGAF, Zachman, ADRs, cloud alignment |
| event-modeling | Event-driven design: Event Modeling, Event Storming, CQRS, sagas |
| figma | Figma MCP server: design context, code generation, design tokens |
| formal-specification | Formal methods: UML/SysML, TLA+, OpenAPI/AsyncAPI, state machines |
| git | Git config, GPG signing, hooks, GitHub issues, history exploration |
| google-ecosystem | Gemini CLI docs, Claude-to-Gemini integration, configuration management |
| melodic-software | Developer onboarding, environment setup, commit workflows |
| microsoft | Microsoft MCP servers: Microsoft Learn, Azure, NuGet, Azure DevOps |
| milan-jovanovic | Milan Jovanovic .NET patterns: Clean Architecture, DDD, CQRS, EF Core |
| openai-ecosystem | OpenAI Codex CLI docs |
| requirements-elicitation | Requirements gathering: LLMREI interviews, gap analysis, prioritization |
| research | Research workflows: MCP integration, multi-source synthesis, structured output |
| response-quality | Response quality standards, source citations |
| security | Security: OWASP, authentication, cryptography, DevSecOps, threat modeling, 12 skills |
| soft-skills | Career progression, interviews, communication, professional visibility |