This document catalogs my GitHub repositories and derived technical skills, demonstrating AI-assisted software development capabilities combining deep real estate domain expertise with modern AI tooling. My development approach represents the emerging paradigm of domain experts building production systems through strategic AI collaboration.
AI-Collaborative Builder: I've built 5 production systems (10,000+ files, 400+ commits across TypeScript, Python, and Bash) solving real business problems through strategic AI partnership with Claude Code. This emerging capability—domain expert + AI tooling = sophisticated software delivery—represents the transformation organizations need to achieve internally.
What This Means in Practice:
- Domain-driven development: 20+ years real estate expertise applied to build systems solving actual business problems ($1,500 → $0.30 cost reduction, 99.2% token optimization, 2-20× speedup)
- Architectural thinking: Can design multi-phase pipelines, agent orchestration patterns, and performance optimization strategies (with AI handling implementation)
- Intensive AI collaboration: Advanced prompting, iterative refinement, debugging with AI—not "AI, build this" but strategic partnership through architecture, requirements, testing
- Proven execution: Shipped 5 production systems with measurable impact and comprehensive documentation
- Reproducible approach: Successfully navigated domain expert → AI builder transformation and can guide others through this path
Core Value Proposition: I represent the future of software development in specialized industries: domain experts leveraging AI to build solutions that previously required dedicated engineering teams. This isn't theoretical—it's proven through production systems serving real business needs. The question for organizations isn't "Can you code independently?" but rather "Can you guide our transformation from domain knowledge to AI-enabled capability?" The answer is demonstrated through this portfolio.
What I Bring:
- 20+ years real estate/infrastructure domain expertise
- Ability to architect solutions and understand system requirements
- Intensive AI collaboration skills (prompting, iterating, debugging with AI)
- Learning mindset: actively studying software concepts to better collaborate with AI
- Proof of capability: 5 production systems that actually work and solve real problems
What I Don't Claim:
- Traditional software engineering credentials or CS fundamentals
- Ability to write production code independently without AI assistance
- Deep technical expertise in algorithms, data structures, or low-level programming
- Qualification for ML engineering or traditional developer roles
- issuer-credit-analysis - REIT Credit Analysis Automation (Python, ML)
- JobOps - Career Intelligence Platform (15 Agents, OSINT)
- corridorvaluation - Autonomous Research Vault (Bash, 910+ lines)
- geniusstrategies - Multi-Agent Orchestration (23 Agents, TypeScript/Python)
- RICS-APC-prep - Professional Certification Pipeline (11-Stage, Performance Engineering)
- Core Programming & Software Engineering - Python, TypeScript, Architecture, Testing
- Data Science & Machine Learning - ML, Feature Engineering, Model Evaluation
- Financial Domain & Real Estate - REIT Analysis, Credit, Financial Modeling
- AI Tooling & Modern Development - Claude Code, Prompt Engineering, Research Implementation
- AI Agent Systems & Orchestration - Multi-Agent, Dialogue, Evolutionary Algorithms
- API & Data Integration - REST APIs, Pipelines, PDF Processing, Browser Automation
- Bash Scripting & System Automation - Advanced Scripting, State Management
- Research Automation & Knowledge Management - Autonomous Research, OSINT, Curation
- Performance Engineering & Optimization - Token Optimization, Parallel Processing, Caching
- Workflow Automation & Process Design - Multi-Phase Pipelines, Assessment Frameworks
- Strategic & Analytical Capabilities - System Dynamics, Requirements Engineering
- Skills Gap Analysis: AI Engineering vs AI Strategy
- How This Portfolio Changes My Positioning
- Technical Credibility Indicators
- Recommended Usage in Applications
- Portfolio Growth Roadmap
- Conclusion
1. issuer-credit-analysis 🏆
Status: Production (v1.0.15) | License: Apache 2.0 | Commits: 172
Multi-phase credit analysis system for Real Estate Investment Trusts (REITs) using Claude Code agents. Transforms 75-page PDF financial statements into professional Moody's-style credit opinion reports.
Technical Architecture:
- 5-Phase Sequential Pipeline: PDF → Markdown → JSON → Calculations → Analysis → Report
- Token Optimization: 99.2% reduction through file reference patterns (1K vs 140K tokens)
- Dual PDF Processing: PyMuPDF4LLM + Camelot (~30 sec) OR Docling (~20 min)
- Pure Python Calculations: Zero-token credit metrics computation
- ML Component: Logistic regression v2.2 predicting distribution cuts (F1=0.870, ROC AUC=0.930)
Technical Sophistication:
- 20 Python Scripts: 400KB+ functional code across data processing, ML training, market monitoring
- Financial Domain Mastery: 43 REALPAC adjustments for FFO/AFFO/ACFO calculations
- Multi-Source Integration: OpenBB Platform, Bank of Canada, Federal Reserve APIs
- Schema Validation: Strict JSON compliance with comprehensive error handling
- Production Features: Config management, logging, testing suite, comprehensive documentation
Skills Demonstrated:
- Python programming (intermediate with AI assistance)
- Software architecture & system design
- Machine learning (scikit-learn, feature engineering, model evaluation)
- API integration & data engineering
- Financial modeling & REIT-specific metrics
- PDF processing (PyMuPDF, Camelot, Docling)
- Documentation & testing practices
Business Impact:
- Automates 2-3 day credit analysis process to 60 seconds
- Cost: ~$0.30 per analysis vs. $500-1500 analyst cost
- Scalable to portfolio-level batch processing
- Enables systematic credit surveillance
Development Approach: Built entirely with Claude Code assistance over 4 days (October 2025). Represents transition from "AI user" to "AI-assisted builder" through:
- Architectural planning with AI guidance
- Iterative refinement across 172 commits
- Domain expertise driving requirements
- AI handling implementation complexity
2. JobOps đź’Ľ
Status: Production (v1.0.0) | License: MIT | Commits: 44
Intelligence-driven job application warfare system using an 8-step methodology to transform master career inventories into tailored, credible resumes with systematic opportunity assessment and strategic interview preparation.
Technical Architecture:
- Claude Code Native: 15 specialized agents orchestrated via 14 slash commands
- Multi-Phase Workflow: Assessment → Resume Development → Interview Prep → Application Finalization
- HAM-Z™ Methodology: Hard Skills + Actions + Metrics + Structure narrative framework
- Provenance Hardening: Comprehensive credibility verification against master resume sources
- Playwright MCP Integration: Headless browser automation for job search and OSINT
- Distributed OSINT System: 6 parallel specialized agents (corporate, legal, leadership, compensation, culture, market)
Technical Sophistication:
- 15 Agent Definitions: Specialized markdown profiles for resume drafting, assessment, OSINT, interview prep
- 14 Slash Commands: Production workflow automation (
/assessjob,/buildresume,/osint,/briefing, etc.) - Modular Assessment Framework: Reusable scoring rubrics with dynamic job-specific criteria generation
- System Dynamics Foundation: Assessment-first hiring model addressing signal-to-noise degradation (-2.5 dB to +10.4 dB improvement)
- Cultural Profile System: Regional adaptation (Canadian, US, European resume styles)
- Hybrid Job Search: API reconnaissance + Playwright deep-scan for complete verbatim job descriptions
Skills Demonstrated:
- Advanced Claude Code agent orchestration
- Markdown-based configuration and documentation
- System architecture design (8-step sequential workflow)
- Requirements engineering (job-specific rubric generation)
- OSINT methodology and parallel intelligence gathering
- Process automation and workflow optimization
- Git version control and semantic versioning
- Technical writing and comprehensive documentation
Business Impact:
- Automates 8-step application process from assessment to submission
- Prevents wasted effort on misaligned opportunities (35-60% reduction per theory)
- Provenance hardening eliminates credibility risks before interviews
- Systematic interview preparation with gap analysis and custom questions
- Company intelligence gathering for negotiation leverage
Development Approach: Built with Claude Code as a meta-demonstration of AI-assisted development capabilities. The repository itself serves as proof-of-concept for consulting engagements: domain expert (career development) + AI tooling (Claude Code) = sophisticated production system. Represents practical application of AI agent orchestration patterns in non-engineering domain.
Key Innovation: Assessment-First Model: Breaks the "Vicious Cycle of Embellishment" by evaluating candidate-job fit before resume creation. System dynamics analysis shows traditional approaches achieve -2.5 dB signal-to-noise ratio (more noise than signal), while assessment-first improves to +3.2 to +10.4 dB, reducing misaligned applications by 35-60% and time-to-hire by 30-60%.
3. corridorvaluation (Private) 🔬
Status: Production (v0.6.0) | License: MIT | Commits: 70 | Branch: autonomous-research-agent
Autonomous research vault system for corridor valuation and expropriation analysis. Combines bash-orchestrated automation with Claude Code agents to create self-maintaining knowledge repositories with permanent web source archival and iterative gap analysis.
Technical Architecture:
- Autonomous Research Engine: 389-line bash orchestrator with state management and phase execution
- Modular Library System: 910 lines across 3 modules (state_manager, validators, phase_execution)
- Dual-Format Strategy: PDF ingestion + markdown conversion for 60-70% token reduction
- 15 Slash Commands: Claude Code integration for research workflows (
/research,/updateindex,/archive-web-sources) - Comprehensive Testing: 169 tests (~80% coverage) using BATS (Bash Automated Testing System)
- Python Utilities: Web source capture with PDF download and HTML→Markdown conversion
Technical Sophistication:
- 486 Total Files: Systematic organization across research papers, web sources, reference materials
- State Persistence: Robust state management (264 lines, 14 functions) for research session continuity
- Input Validation: Enterprise-grade validation (418 lines, 20 functions) with error handling
- Citation Verification: Cross-referencing and source tracking for research integrity
- Real-Time Monitoring: Diagnostics and progress tracking during autonomous research cycles
- Incremental Research: Gap analysis → targeted research → knowledge integration loop
Skills Demonstrated:
- Bash scripting (advanced: 910+ lines of production code)
- System automation and process orchestration
- Research methodology and knowledge management
- Python utilities (web scraping, file conversion, metadata extraction)
- Testing frameworks (BATS, comprehensive coverage)
- Documentation systems (markdown-based knowledge vault)
- Claude Code agent orchestration for research workflows
- OSINT methodology (web source archival, citation tracking)
Business Impact:
- Automates iterative research process for complex real estate appraisal specialty
- Permanent web source retention prevents link rot and ensures reproducibility
- Dual-format architecture reduces AI processing costs by 60-70%
- Self-maintaining vault eliminates manual index updates
- Enables systematic literature review and gap identification
Development Approach: Built as specialized research infrastructure for corridor valuation domain expertise. Demonstrates advanced bash scripting combined with AI orchestration to create autonomous knowledge systems. Represents progression from AI-assisted development (issuer-credit-analysis) to AI-orchestrated research automation.
Domain Specialization: Focus on corridor valuation, expropriation analysis, partial takings, and easement valuation—highly technical real estate appraisal specialty requiring systematic literature tracking and regulatory compliance.
Key Innovation: Autonomous Research Loops: System performs gap analysis, conducts targeted research via Claude, archives web sources permanently, converts to dual formats, updates indexes—all without manual intervention. Transforms static document repository into self-improving knowledge base.
4. geniusstrategies (Private) đź§
Status: Active Development (v0.1.0) | Commits: 117 | Branch: feature/olympus-agi-enhancements
Multi-agent orchestration platform featuring 23 AI agents (Board of Geniuses + Domain Expert Advisory Panel) with TypeScript-based dialogue system and Python self-improvement toolkit implementing cutting-edge research (AlphaEvolve, CodeEvolve, Darwin Gödel Machine).
Technical Architecture:
- TypeScript Dialogue System: State machines, orchestrator patterns, token budget management, circuit breaker resilience
- 23 AI Agents: 12 thinking strategist agents (Einstein, Tesla, Feynman, etc.) + 11 domain expert agents (engineering specializations)
- Agent-to-Agent Dialogue: Multi-agent collaboration with conversation orchestration and termination logic
- Python Self-Improvement Toolkit: 783-line evolutionary optimization system implementing latest AI research
- Comprehensive Testing: Jest (TypeScript) + pytest (Python) with ~80% coverage, 606-line test suite
- Multi-Objective Optimization: Pareto frontier analysis across 5 competing objectives (coverage, quality, speed, compatibility, innovation)
Technical Sophistication:
- 8,902 Total Files: Massive knowledge base with complete book transcriptions and agent knowledge repositories
- Production TypeScript: State serialization, validators, budget tracking, speaker selection algorithms
- Research Implementation: Academic papers → production code (AlphaEvolve genetic algorithms, CodeEvolve meta-prompting)
- Cross-Agent Integration: 36 bilateral relationships fully documented with hand-off triggers and collaboration patterns
- Evolutionary Algorithms: Multi-objective fitness evaluation, meta-prompting diversity booster, semantic compression
- Resilience Patterns: Circuit breaker implementation, error handling, state recovery
Skills Demonstrated:
- TypeScript/JavaScript (intermediate-advanced: dialogue orchestration, state machines, testing frameworks)
- Software architecture (advanced: orchestrator patterns, state management, resilience design)
- Agent design and orchestration (advanced: 23-agent system with bilateral integration)
- Research-to-implementation (advanced: translating academic papers into production systems)
- Testing frameworks (intermediate: Jest, pytest, comprehensive coverage)
- Knowledge management (advanced: structured agent knowledge bases, cross-referencing systems)
- Evolutionary algorithms (intermediate: genetic optimization, Pareto frontier analysis)
- TypeScript typing and interfaces (intermediate: complex type definitions for state and dialogue)
Business Impact:
- Provides 23 specialized AI consultants with distinct expertise areas
- Agent-to-agent collaboration enables complex multi-perspective problem solving
- Self-improvement toolkit enables continuous agent optimization
- Thinking strategists teach HOW to think, domain experts provide WHAT to know
- Foundation for agent orchestration patterns used in JobOps and other projects
Development Approach: Built as comprehensive AI agent platform exploring embodied cognition (historical geniuses' thinking strategies) combined with evidence-based domain expertise. Represents most ambitious AI agent orchestration project, serving as architectural foundation for subsequent specialized projects. Implements cutting-edge AI research papers in production TypeScript/Python systems.
Research Foundation:
- AlphaEvolve (Google DeepMind, 2025): Evolutionary optimization algorithms
- CodeEvolve (arXiv:2510.14150): Meta-prompting diversity strategies
- Darwin Gödel Machine (Sakana AI, 2025): Self-modifying agent architectures
- Semantic Compression (arXiv:2507.19715): Knowledge base optimization
- AutoKG (arXiv:2311.14740): Automated knowledge graph construction
Key Innovation: Multi-Agent Research Implementation Platform: Combines thinking strategist agents (cognitive patterns from historical geniuses) with domain expert agents (evidence-based specializations) in production TypeScript infrastructure. Self-improvement toolkit enables agents to evolve through genetic algorithms while maintaining quality thresholds. Demonstrates capability to implement academic AI research papers as production systems with comprehensive testing.
Component Breakdown:
- Thinking Strategists: Einstein (systemic thinking), Tesla (visualization), Feynman (simplification), Socrates (questioning), Leonardo (observation), Mozart (synthesis), Disney (imagination), Freud (unconscious), Holmes (deduction), Aristotle (logic), Oppenheimer (complexity), Dan Koe (purpose-profit)
- Domain Experts: Agentic Engineering, Backend Engineer, Frontend Engineer, Code Implementation, QA Engineer, UI Designer, DevOps Engineer, Product Manager, Project Manager, Software Architect, UX Researcher
- Infrastructure: Dialogue orchestrator, state machine, token budget tracker, circuit breaker, termination logic, speaker selection
- Self-Improvement: Fitness evaluator, meta-prompting tool, multi-objective optimizer, variant generator, Pareto frontier analysis
5. RICS-APC-prep 🎓
Status: Production (v2.0) | Commits: 76 | License: Professional Use
Professional certification assessment workflow for Royal Institution of Chartered Surveyors (RICS) featuring 11-stage AI-assisted assessment pipeline with skills-based architecture, preprocessing cache for 60% faster execution, and parallel processing delivering 2-20Ă— speedup across complex multi-document analyses.
Technical Architecture:
- Skills-Based Competency Loading: On-demand skill invocation achieving 50-60% token reduction (loads 15-20 competencies vs 60+ pathway guides)
- 11-Stage Assessment Pipeline: Preprocessing → Pre-interview validation → Interview prep → Post-interview analysis → Decision support → QA
- Preprocessing Cache: JSON-based cache reducing execution time by 60% and saving 700K+ tokens (86-90% I/O reduction)
- Modular Output Structure: Organized subfolders with executive summaries and section files preventing truncation
- Parallel Processing: Concurrent agent execution across all commands (2-20Ă— speedup depending on complexity)
- Python Data Infrastructure: 19 Python scripts for competency extraction, validation, cleaning, and merging
Technical Sophistication:
- 194 Total Files: Complete RICS pathway guides, assessment templates, Python utilities, 145 markdown files
- Token Optimization: 84% smaller assessment guides (8-18KB vs 75KB) through skills architecture
- Performance Engineering: Per-competency parallelization (15-20Ă— speedup), focused context extraction (70-80% token reduction)
- Data Processing: 721-line competency extraction system, 557-line cache validator, data cleaning utilities
- Single Source of Truth: DRY architecture with shared modules eliminating 40% code duplication
- Quality Assurance: 3-stage QA workflow validating interview structure, assessor conduct, analytical work quality
Skills Demonstrated:
- Professional workflow automation (advanced: 11-stage certification assessment pipeline)
- Performance engineering (advanced: token optimization, parallel processing, caching strategies)
- Python data processing (intermediate: extraction, validation, cleaning, JSON schema design)
- Claude Code architecture (advanced: skills-based loading, modular outputs, parallel agents)
- Domain expertise (expert: RICS professional standards, assessment methodologies, certification processes)
- Documentation (expert: 650+ line README, developer guides, technical specifications)
- Process design (advanced: multi-phase workflows, quality gates, decision support systems)
Business Impact:
- Reduces assessment preparation time from 2-3 hours to 30-60 minutes
- 60% faster execution across 11-command pipeline through preprocessing
- Prevents truncation in long analyses through modular structure
- Ensures consistency and fairness across candidate assessments
- Supports RICS assessors conducting APC, SPA, Academic, Specialist, and Associate evaluations
- Complete audit trail for appeals and quality assurance
Development Approach: Built to enhance RICS assessor efficiency while maintaining professional judgment authority. Demonstrates capability to design professional certification infrastructure with real industry compliance requirements, measurable performance gains, and comprehensive quality assurance. Implements advanced token optimization and parallel processing patterns for production AI workflows.
Domain Specialization: Royal Institution of Chartered Surveyors (RICS) professional certification across 21 pathways including Valuation, Building Surveying, Project Management, Facilities Management, Commercial Property Practice, and Infrastructure. Assessment framework covers 11 mandatory competencies + pathway-specific technical competencies at Levels 1-3.
Key Innovation: Skills-Based Competency Architecture: Single source of truth for competency definitions loaded on-demand per candidate's declared competencies. Combined with preprocessing cache and parallel processing, achieves 50-60% token reduction and 2-20Ă— speedup while maintaining complete backward compatibility. Modular output structure with executive summaries prevents truncation in complex multi-competency analyses.
Pipeline Highlights:
- Phase 0: Preprocessing cache (60% faster execution)
- Phase 1: Pre-assessment validation (submission compliance, pathway alignment)
- Phase 2: Pre-interview review (comprehensive analysis, competency matrix, interview questions)
- Phase 3: Optional technical briefing (3-stage research workflow for specialized domains)
- Phase 4: Post-interview analysis (transcript analysis, ethics assessment, evidence matrix)
- Phase 5: Decision support (assessor debrief, referral reports)
- Phase 6: Quality assurance (interview structure compliance, assessor performance review)
| Capability | Proficiency | Evidence | Development Method |
|---|---|---|---|
| Python | Basic understanding, AI-dependent execution | 400KB+ code across repos (ALL written with Claude Code): ML (sklearn), self-improvement (783 lines), data processing (721-line extraction, 557-line validation). Can read and understand Python, cannot write production code independently. | 100% Claude Code collaboration |
| TypeScript/JavaScript | Conceptual understanding only | Dialogue orchestration system, state machines, 8902 files, Jest testing—ALL built with Claude Code. Understand concepts, cannot implement independently. | 100% Claude Code collaboration |
| Software Architecture | Strong (conceptual design), AI-dependent (implementation) | Can architect solutions, define requirements, understand patterns. Cannot implement without AI. Multi-agent orchestration, 5-phase pipelines designed with AI guidance. | Requirements definition + AI implementation |
| Error Handling & Validation | Conceptual understanding | Understand error handling concepts, schema validation, resilience patterns—implementation entirely AI-driven | Domain knowledge + AI coding |
| Testing Frameworks | Conceptual understanding | Understand testing concepts (unit, integration, coverage). ALL tests written with Claude Code. | AI-driven with learning |
| Documentation | Expert | READMEs, architecture docs, research summaries—this is MY strength. Technical writing combining domain expertise with AI-learned concepts. | Professional writing + technical learning |
| Version Control (Git) | Basic operational proficiency | 400+ commits across repos using standard commands (add, commit, push, branch). Understand branching concepts, semantic versioning. | Guided practice with AI |
| Capability | Proficiency | Evidence | Learning Method |
|---|---|---|---|
| Machine Learning | Conceptual understanding, AI-dependent implementation | Logistic regression model (F1=0.870, ROC AUC=0.930) built entirely with Claude Code. Understand model evaluation concepts, cannot implement independently. | Claude Code implementation + learning |
| Feature Engineering | Domain expertise + AI execution | Can identify which features matter (domain knowledge), cannot implement feature engineering pipelines independently. 28 features, SelectKBest, missing value handling—all AI-implemented. | Domain judgment + AI coding |
| Model Evaluation | Conceptual understanding | Understand CV, stratified splits, multiple metrics, baseline comparison—implementation AI-driven | Learning best practices through AI collaboration |
| Data Processing | Conceptual understanding | JSON parsing, data enrichment, multi-source integration—ALL implemented by Claude Code | AI-driven with learning |
| Statistical Analysis | Expert (financial domain), Conceptual (ML implementation) | Deep expertise in coverage ratios, burn rate analysis, REIT metrics. ML statistics implementation requires AI assistance. | Domain expertise + AI implementation |
| Capability | Proficiency | Evidence | Foundation |
|---|---|---|---|
| REIT Financial Analysis | Expert | FFO/AFFO/ACFO calculations, 43 REALPAC adjustments | 20+ years professional experience |
| Credit Analysis | Expert | 5-factor scorecards, leverage metrics, coverage ratios | Real estate operations background |
| Financial Modeling | Expert | DCF logic, sensitivity analysis, reconciliations | CFA + property valuation credentials |
| Regulatory Standards | Expert | REALPAC White Papers, IFRS reporting | Industry professional knowledge |
| Capability | Proficiency | Evidence | Development Period |
|---|---|---|---|
| Claude Code Collaboration | Expert | Built 5 production systems (10,000+ files, 400+ commits) entirely through AI collaboration. Multi-agent orchestration (23 agents), token optimization, slash commands—ALL proof of intensive AI partnership skills. | 2024-Present (intensive, daily) |
| AI-Collaborative Development | Expert | Proven ability to architect, iterate, debug, and ship production systems through AI partnership. NOT "AI, build this"—deep engagement with requirements, architecture, testing, deployment. | 6-month intensive (Jun-Oct 2025) |
| Prompt Engineering & Requirements Definition | Expert | Can translate business problems into clear technical requirements for AI implementation. Agent profile design, workflow orchestration, 23 specialized agents—all proof of advanced prompting and systems thinking. | 3 years LLM usage + 6 months production building |
| Architectural Thinking | Advanced | Can design multi-phase pipelines, agent orchestration patterns, token optimization strategies. Understand system design principles—implementation requires AI partner. | Developed through AI collaboration |
| Research-to-Implementation (with AI) | Advanced | Can read academic papers (AlphaEvolve, CodeEvolve), understand concepts, and guide AI to implement production systems. Research comprehension + AI translation skills. | Academic research + AI implementation |
| Learning & Iteration | Expert | Rapid learning through building. Each repository taught new concepts (TypeScript, testing, performance optimization) through intensive AI collaboration and iteration. | Continuous learning mindset |
| Capability | Proficiency | Evidence | Development Method |
|---|---|---|---|
| Multi-Agent Orchestration | Advanced | 23-agent system with dialogue orchestration, 36 bilateral relationships | System architecture + AI |
| Agent Design | Advanced | Thinking strategists + domain experts, cross-referencing, hand-off patterns | Agent framework design |
| Dialogue Systems | Intermediate-Advanced | TypeScript state machines, speaker selection, termination logic | AI-guided implementation |
| Evolutionary Algorithms | Intermediate | Genetic optimization, Pareto frontier, multi-objective fitness | Research paper implementation |
| Meta-Prompting | Intermediate | Diversity boosting, variant generation, exploration strategies | CodeEvolve research |
| State Management | Intermediate | State serialization, validators, recovery, persistence | TypeScript patterns |
| Capability | Proficiency | Evidence | Implementation |
|---|---|---|---|
| REST API Usage | Intermediate | OpenBB Platform, Bank of Canada, Federal Reserve, hiring.cafe | Multi-source data collection |
| Data Pipeline Design | Intermediate-Advanced | Sequential phase dependencies, validation gates | Architectural patterns |
| PDF Processing | Intermediate | PyMuPDF4LLM, Camelot, Docling | Multiple library evaluation |
| JSON Schema Design | Intermediate | Strict validation, flat structures, no nulls | Requirements-driven |
| Browser Automation | Intermediate | Playwright MCP integration, headless scraping | JobOps job search system |
| Capability | Proficiency | Evidence | Development Method |
|---|---|---|---|
| Bash Scripting | Advanced | 910+ lines production code, 389-line orchestrator | Self-taught with AI assistance |
| State Management | Advanced | 264-line state manager, 14 functions, session persistence | Architectural design |
| Input Validation | Advanced | 418-line validator module, 20 functions, enterprise error handling | Best practices implementation |
| Process Orchestration | Advanced | Multi-phase execution, dependency management, error recovery | System design |
| Testing (BATS) | Intermediate | 169 tests, ~80% coverage, automated validation | Quality assurance practices |
| Shell Utilities | Intermediate | File operations, text processing, system integration | Practical automation |
| Capability | Proficiency | Evidence | Development Method |
|---|---|---|---|
| Autonomous Research Systems | Advanced | Self-improving knowledge vault, gap analysis loops | System architecture + AI orchestration |
| Knowledge Curation | Advanced | 486-file research vault, systematic organization | Information science + domain expertise |
| Web Source Archival | Intermediate | Python utilities, permanent retention, link rot prevention | Practical tool development |
| Document Conversion | Intermediate | PDF→Markdown pipelines, dual-format architecture | Multi-tool integration |
| Citation Management | Intermediate | Cross-referencing, source tracking, verification | Research methodology |
| Literature Review Automation | Advanced | Iterative gap identification, targeted research cycles | Process design + AI agents |
| OSINT Methodology | Intermediate-Advanced | 6-agent distributed intelligence, web source archival | Research frameworks + automation |
| Capability | Proficiency | Evidence | Development Method |
|---|---|---|---|
| Token Optimization | Advanced | 99.2% reduction (issuer-credit-analysis), 50-60% reduction (RICS skills architecture), 84% smaller guides | Architectural patterns + caching |
| Parallel Processing | Advanced | 2-20Ă— speedup across RICS commands, 15-20 concurrent agents, per-competency parallelization | Claude Code concurrency |
| Caching Strategies | Intermediate-Advanced | Preprocessing cache (60% faster, 86-90% I/O reduction), 700K+ token savings | Performance optimization |
| Context Extraction | Advanced | Focused loading (70-80% token reduction), on-demand skill invocation | Selective data loading |
| Computational Efficiency | Intermediate | Multi-objective optimization, Pareto frontier analysis, algorithm selection | Research implementation |
| Capability | Proficiency | Evidence | Development Method |
|---|---|---|---|
| Multi-Phase Workflow Design | Advanced | 5-phase credit pipeline, 8-step resume process, 11-stage assessment pipeline, autonomous research loops | System architecture with AI |
| Agent Orchestration | Advanced | 15 specialized agents (JobOps), 23 agents (geniusstrategies), parallel OSINT execution, research automation | Claude Code agent frameworks |
| Command Line Interface Design | Intermediate-Advanced | 14 slash commands (JobOps), 15 slash commands (corridorvaluation), 11-stage pipeline (RICS) | User experience focus |
| Process Automation | Advanced | End-to-end pipeline automation, minimal manual steps, preprocessing workflows | Requirements-driven design |
| Assessment Framework Design | Advanced | Dynamic rubric generation, job-specific criteria, RICS competency evaluation | HR + domain expertise |
| Professional Certification Workflows | Advanced | 11-stage RICS assessment pipeline, quality gates, decision support systems | Industry standards compliance |
| Capability | Proficiency | Evidence | Foundation |
|---|---|---|---|
| System Dynamics Analysis | Intermediate | Assessment-first model, signal-to-noise analysis | Systems thinking + quantitative analysis |
| Requirements Engineering | Advanced | Job-specific rubric generation, agent specifications | Business analysis + technical implementation |
| Theoretical Modeling | Intermediate | Vicious Cycle of Embellishment framework | Economic theory + system dynamics |
| Credibility Analysis | Expert | Provenance hardening methodology, evidence verification | Professional experience + risk assessment |
| Strategic Planning | Expert | 8-step sequential workflow, phased approach | 20+ years strategic experience |