Skill Profile
Track what you know, what you're learning, and where to grow next.
E
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
E's profile is healthy but lagging behind actual activity — Multi-Agent Systems has become the dominant learning focus with 40 items, Claude API usage has matured to proficiency through shipped projects, and RAG and Model Evaluation have crossed from 'unexplored gaps' into active learning territory.
Generated 2026-06-05
Multi-Agent Systems
learning → learning (high priority)
Multi-Agent Systems dominates the learning queue with 40 items in 30 days — by far the most active area. This signals the field is moving fast and E is deeply engaged in tracking it. Priority should be elevated to reflect that this is the primary learning frontier, not just a Phase 2D goal.
- 40 queue items tagged Multi-Agent Systems in 30 days (highest of any category)
- Items span orchestration, memory architecture, skill libraries, coordination protocols, monitoring, and failure modes
- Nearly every daily run surfaces multi-agent content as high-relevance
RAG (Retrieval Augmented Generation)
gaps → learning
8 queue items in 30 days covering RAG fundamentals, GraphRAG, embedding models, and retrieval layers — E is actively building conceptual understanding. Multiple items explicitly connect RAG to the Intelligence Hub's briefing pipeline. This is no longer an unexplored gap.
- 8 RAG-tagged queue items across the 30-day window
- Items cover: conceptual intro, GraphRAG, embedding models, retrieval in agent loops, vector/query features
- Queue actions explicitly map RAG to Intelligence Hub use cases (e.g., 'where the Intelligence Hub could benefit from a retrieval layer')
Model Evaluation & Benchmarks
gaps → learning
9 queue items in 30 days covering evaluation frameworks, leaderboards, benchmarking vocabulary, and model comparison — E is building systematic understanding beyond 'consumer-level.' Items are action-oriented, asking E to apply evaluation thinking to their own agents.
- 9 queue items tagged Model Evaluation & Benchmarks
- Items include: building evaluation frameworks from use cases, agentic framework leaderboards, model selection tradeoffs, evaluating own Intelligence Hub outputs
- Consistent appearance across the full 30-day window (May 6 through June 5)
Claude API
learning → proficient
E is actively shipping multiple projects (Intelligence Hub, CIM Pipeline) that use the Claude API in production. Queue items show E is now working on advanced concerns — cost/rate-limit planning, provider-agnostic patterns, parallel generation, Dynamic Workflows — which are proficiency-level concerns, not learning-level.
- Intelligence Hub and CIM Pipeline are both active projects using Claude API
- Queue items focus on advanced API usage: metered usage cost planning, rate-limit enforcement, provider-agnostic architecture audits, Dynamic Workflows evaluation
- 5 Claude API-specific queue items plus several cross-tagged items showing integrated usage
AI Safety & Alignment
gaps → gaps (elevated priority)
4 dedicated items plus safety-adjacent content appearing in Multi-Agent Systems items (sandboxing, approval patterns, permission scoping, memory integrity). As E moves toward Phase 2D autonomous agents, safety architecture is becoming practically relevant, not just theoretical.
- 4 AI Safety & Alignment queue items in 30 days
- Additional safety-relevant content in agentic workflow items: sandboxing patterns, permission audits, agent approval flows
- Items are becoming practical ('map three layers against your Intelligence Hub', 'audit what real-world actions each agent can take')
Agent Observability & Monitoring
A distinct pattern of agent monitoring, analytics instrumentation, and observability content has emerged across multiple queue items. This is a discrete skill area not currently tracked in the profile, separate from both Multi-Agent Systems and Agentic Workflows.
- Queue item: 'evaluate whether adding analytics instrumentation to your Intelligence Hub agents would surface useful patterns' (May 13)
- Queue item: 'extract 3 monitoring patterns you can apply to the Intelligence Hub pipeline' (June 2)
- Queue item: 'skim the monitoring architecture section — note patterns applicable to your Intelligence Hub agent pipeline' (June 1)
- Queue item: 'Try spinning up Torrix against your Intelligence Hub agent to observe Claude API call patterns, latency, and token usage' (May 16)
Interests
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