Skill Profile
Track what you know, what you're learning, and where to grow next.
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Current Projects
- AI Intelligence Hub (agentic daily briefing system)
- CIM Pipeline (automated business presentation generation)
- Personal site publishing automation
Last updated: 2026-03-10
Proficient
Prompt Engineering
Daily practice with Claude
Agentic Workflows
Building the Intelligence Hub
Static Site Generation
Astro + Vercel pipeline
GitHub API Automation
Content publishing pipeline
Node.js Scripting
Agent development
Currently Learning
MCP (Model Context Protocol)
Understand concept, haven't built servers yet
Claude API
Using in agent, learning patterns
Multi-Agent Systems
Phase 2D goal
Skill Gaps
RAG (Retrieval Augmented Generation)
FrequentHaven't explored yet
Fine-tuning
No experience
Vector Databases
No experience
LangChain / LlamaIndex
Aware but haven't used
Python ML Stack
Focused on Node.js so far
Model Evaluation & Benchmarks
FrequentConsumer-level understanding
AI Safety & Alignment
TrendingGeneral awareness only
Computer Vision
Haven't explored
Agent Suggestions
Profile is healthy but skewed — Multi-Agent Systems dominates the learning queue at 37 items and deserves elevated priority, Claude API has graduated to proficient through shipped projects, and agent observability is emerging as an untracked gap worth naming.
Generated 2026-06-07
Multi-Agent Systems
learning → learning (high priority)
37 queue items in 30 days — by far the most active skill area. This volume indicates the field is moving fast and the practitioner's Phase 2D goals make this critical. Should be treated as the top learning priority.
- 37 queue items tagged Multi-Agent Systems in 30 days
- Items span orchestration patterns, memory architecture, skill libraries, failure modes, coordination protocols, and monitoring
- Phase 2D of Intelligence Hub is explicitly a multi-agent goal
Model Evaluation & Benchmarks
gaps → gaps (elevated priority)
10 queue items in 30 days hits the threshold for priority upgrade. The practitioner is consistently encountering evaluation frameworks, leaderboards, and failure mode analysis relevant to their own agent systems.
- 10 queue items tagged Model Evaluation & Benchmarks in 30 days
- Items cover evaluation frameworks, leaderboards, failure modes, and benchmarking vocabulary
- Directly relevant to evaluating Intelligence Hub and CIM Pipeline output quality
Agent Observability & Monitoring
A distinct pattern of agent monitoring, instrumentation, and session analysis content has emerged across multiple skill areas. This is a coherent capability gap not currently tracked in the profile.
- Queue item 2026-05-13: 'adding analytics instrumentation to your Intelligence Hub agents'
- Queue item 2026-06-02: 'extract 3 monitoring patterns you can apply to the Intelligence Hub pipeline'
- Queue item 2026-06-01: 'monitoring architecture section'
- Queue item 2026-06-07: 'session analysis' and 'observability'
- Queue item 2026-05-16: 'Torrix against your Intelligence Hub agent to observe API call patterns, latency, and token usage'
Claude API
learning → proficient
The practitioner has shipped two working systems (Intelligence Hub and CIM Pipeline) that actively use the Claude API in production agentic pipelines. The queue items show they're now working on advanced concerns (rate limiting, cost planning, provider-agnostic patterns, dynamic workflows) rather than learning fundamentals.
- AI Intelligence Hub is a running agentic system using Claude API (24 logged agent runs in 30 days)
- CIM Pipeline is an active project using Claude API for generation
- Queue items focus on advanced patterns: rate limits, cost planning, provider abstraction, parallel generation — not introductory learning
Interests
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