moai-foundation-ears
Enterprise EARS (Evaluate, Analyze, Recommend, Synthesize) Framework with AI-powered requirements engineering, Context7 integration, and intelligent solution orchestration for systematic problem-solving
npx skills add majiayu000/claude-skill-registry --skill moai-foundation-ears-ajbcoding-claude-skill-eval --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What It Does
Enterprise EARS (Evaluate, Analyze, Recommend, Synthesize) Framework expert with AI-powered requirements engineering, Context7 integration, and intelligent solution orchestration for systematic problem-solving.
Revolutionary v4.0.0 capabilities:
- 🤖 AI-Powered EARS Analysis using Context7 MCP for latest problem-solving methodologies
- 📊 Intelligent Requirements Engineering with automated stakeholder analysis and validation
- 🚀 Advanced Solution Synthesis with AI-driven alternative evaluation and optimization
- 🔗 Enterprise Decision Framework with zero-configuration systematic thinking processes
- 📈 Predictive Solution Validation with success probability and risk assessment
How it works
- The skill defines an EARSFrameworkOptimizer that orchestrates four phases (Evaluate, Analyze, Recommend, Synthesize) by retrieving Context7 documents for requirements and problem-solving topics, then performing phase-specific engines.
- Phase E (Evaluation) analyzes stakeholders, constraints, and context to produce stakeholder_analysis, constraint_analysis, and context_assessment, plus evaluation_summary and critical_factors.
- Phase A (Analysis) breaks down problem context into components, roots causes, and dependencies, producing component_analysis, root_cause_analysis, and dependency_analysis plus problem_complexity and key_insights.
- Phase R (Recommendations) generates solution_alternatives, evaluates them against requirements and constraints, assesses risks, and outputs an implementation timeline and resource needs.
- Phase S (Synthesize) combines evaluation, analysis, and recommendations into a synthesis with an overall plan and validation view, plus risk and confidence measures.
- The implementation blocks show Python-style classes and methods to perform these steps using Context7 docs, root-cause techniques, and structured analyses.
When to use it
- Automatic triggers:
- Complex problem analysis and systematic solution design discussions
- Requirements engineering and stakeholder alignment processes
- Decision-making framework implementation and evaluation
- Project planning and solution architecture validation
- Manual invocation:
- Applying EARS framework to complex business problems
- Conducting systematic requirements analysis and validation
- Designing solution alternatives with comprehensive evaluation
- Implementing structured decision-making processes
What it can touch
- The skill references tools and modules such as Context7 integration components and internal engines (EvaluationEngine, AnalysisEngine, RecommendationEngine, SynthesisEngine) to access library docs, perform analyses, and generate solutions. It relies on methods like Context7Client.get_library_docs and various analysis/recommendation routines to process data and produce outputs.
Caveats
- The skill description shows an abstract implementation with placeholder components and async methods; real-world usage requires the Context7 services to be available and correctly authenticated.
- No explicit license or deployment constraints beyond the MIT license in metadata; ensure compatible deployment environment.
# Enterprise EARS Framework Expert v4.0.0 ## Skill Metadata | Field | Value | | ----- | ----- | | **Skill Name** | moai-foundation-ears | | **Version** | 4.0.0 (2025-11-13) | | **Tier** | Foundation Framework Expert | | **AI-Powered** | ✅ Context7 Integration, Intelligent Analysis | | **Auto-load** | On demand when systematic analysis keywords detected | --- ## What It Does Enterprise EARS (Evaluate, Analyze, Recommend, Synthesize) Framework expert with AI-powered requirements engineering, Context7 integration, and intelligent solution orchestration for systematic problem-solving. **Revolutionary v4.0.0 capabilities**: - 🤖 **AI-Powered EARS Analysis** using Context7 MCP for latest problem-solving methodologies - 📊 **Intelligent Requirements Engineering** with automated stakeholder analysis and validation - 🚀 **Advanced Solution Synthesis** with AI-driven alternative evaluation and optimization - 🔗 **Enterprise Decision Framework** with zero-configuration systematic thinking processes - 📈 **Predictive Solution Validation** with success probability and risk assessment --- ## When to Use **Automatic triggers**: - Complex problem analysis and systematic solution design discussions
- Skill Metadata
- What It Does
- When to Use
- EARS Framework Overview
- Four-Phase Systematic Analysis
- Core Principles
- Application Areas
- Integration Benefits
- EARS Architecture Intelligence
- Phase E: Evaluation Implementation
- Phase A: Analysis Implementation
- Phase R: Recommendations Implementation
- Phase S: Synthesis Implementation
- API Reference
What does the moai-foundation-ears skill do?
Enterprise EARS (Evaluate, Analyze, Recommend, Synthesize) Framework with AI-powered requirements engineering, Context7 integration, and intelligent solution orchestration for systematic problem-solving
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill moai-foundation-ears-ajbcoding-claude-skill-eval --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
