cd developer-growth-plugin
npm install
npx tsc

Load the built plugin in LM Studio.

Configuration

Skill Categories

The plugin tracks 18 skill areas across four tiers:

LLM-Era Core

• prompt_engineering — Prompt design, chain-of-thought, few-shot, system prompts
• rag_and_retrieval — RAG pipelines, chunking, hybrid search, re-ranking
• agents_and_orchestration — Tool use, multi-agent systems, LangGraph, CrewAI
• llm_evaluation — Evals, benchmarks, LLM-as-judge, regression testing
• ai_assisted_coding — Using AI tools as a force multiplier in your workflow
• llmops_production — Deployment, observability, cost management, latency
• system_design_ai — Designing reliable, scalable AI-powered systems
• fine_tuning_and_peft — LoRA, QLoRA, instruction tuning, DPO
• open_source_llms — Local models, Ollama, LM Studio, GGUF, quantisation
• ai_security_and_safety — Prompt injection, jailbreaks, guardrails, red-teaming
• vector_databases — Pinecone, pgvector, Qdrant, FAISS, embedding strategies
• multimodal_ai — Vision, audio, video models and pipelines

Timeless Foundations (AI amplifies these, never replaces them)

• cs_fundamentals — Algorithms, data structures, complexity, concurrency
• software_architecture — Design patterns, modularity, system decomposition
• data_engineering — Pipelines, ETL, data quality, streaming
• devops_and_infra — CI/CD, containers, cloud, infrastructure as code

Adjacent High-Value

• product_and_communication — Problem framing, stakeholder communication, shipping
• other — Anything else worth tracking

Tools Reference

Skills

rate_skill — Rate your proficiency in a skill (1–10)

rate_skill(category, rating, notes?)

get_skill_map — View your full skill map with ASCII progress bars

get_skill_map()

Returns all rated skills sorted by category with visual progress bars (█░).

assess_skill_gaps — Get role-specific gap analysis

assess_skill_gaps()

Compares your ratings against expected levels for your target role. Returns critical gaps, growth areas, and prioritised next steps.

Learning Tracker

log_session — Record a learning session

log_session(topic, durationMinutes, category, format?, notes?, resourceUrl?)

• format: reading, video, practice, project, course, podcast, other
• Automatically calculates current streak and weekly goal progress

get_stats — View learning statistics

get_stats()

Returns total hours, sessions per category, streak, weekly goal %, most studied topics, and recent momentum.

list_sessions — Browse past learning sessions

list_sessions(limit?, category?, search?)

Goals

set_goal — Set a learning goal

set_goal(title, description, category, targetDate?, milestones?)

• status progresses through: active → completed / paused / abandoned

update_goal — Update goal status or notes

update_goal(goalId, status?, notes?, completedAt?)

list_goals — List all goals

list_goals(status?)

Filter by: active, completed, paused, abandoned.

Resources

add_resource — Save a learning resource

add_resource(title, url, category, type?, description?, tags?)

• type: article, paper, repo, course, video, podcast, book, tool, other
• status: want_to_read, in_progress, completed, archived

list_resources — Browse your resource library

list_resources(category?, type?, status?, search?)

update_resource — Update resource status or rating

update_resource(resourceId, status?, rating?, notes?)

search_resources — Search curated AI learning sources on the web

search_resources(query, category?)

Searches authoritative sources: eugeneyan.com, simonwillison.net, lilianweng.github.io, huyenchip.com, Arxiv, Hugging Face, and more.

Planning

generate_learning_path — Generate a phased learning roadmap

generate_learning_path(targetSkill, currentLevel?, timeframeWeeks?)

Returns a 4-phase plan: Foundation → Core Skills → Build Projects → Advanced Topics, with curated resources and project ideas at each phase.

generate_study_plan — Generate a weekly study schedule

generate_study_plan(focusAreas, availableHoursPerWeek, durationWeeks?)

Allocates time across your focus areas with daily session recommendations.

weekly_review — Run your weekly growth review

weekly_review()

Summarises the past 7 days: sessions logged, time invested vs goal, progress on active goals, and suggested focus for next week.

Content & Research

explain_concept — Get a concept explained at your level

explain_concept(concept, depth?)

• depth: beginner, intermediate, advanced

generate_project_idea — Get a hands-on project idea to build a skill

generate_project_idea(skill, scope?)

• scope: weekend, week, month
• Returns project brief, tech stack, learning outcomes, and stretch goals

search_ai_trends — Search for current AI/ML developments

search_ai_trends(topic?)

Fetches recent news, papers, and community discussions on a topic.

compare_ai_tools — Compare tools, frameworks, or approaches

compare_ai_tools(toolA, toolB, useCase?)

Returns strengths, weaknesses, when to use each, and community consensus.

Export

export_growth_report — Export a full growth report

export_growth_report(format?)

• format: summary (default) or detailed
• Includes: skill map with ASCII bars, learning stats, active goals, recent sessions, top resources
• Saves to your data directory as growth-report-YYYY-MM-DD.txt

Example Conversations

Starting out:

"I'm a backend engineer with 4 years of experience trying to move into AI engineering. Where should I start?"

The plugin will call assess_skill_gaps and generate_learning_path to give you a concrete, role-specific roadmap.

Daily tracking:

"I just spent 90 minutes going through the LangGraph docs and built a basic agent loop."

→ log_session(topic="LangGraph agents", durationMinutes=90, category="agents_and_orchestration", format="practice")

Weekly planning:

"Run my weekly review and suggest what to focus on this week."

→ weekly_review() + generate_study_plan()

Skill assessment:

"Rate my RAG skills at 4/10 — I understand the concept but haven't built a production system."

→ rate_skill(category="rag_and_retrieval", rating=4, notes="...")

Project ideas:

"Give me a weekend project to improve my LLM evaluation skills."

→ generate_project_idea(skill="llm_evaluation", scope="weekend")

Staying current:

"What's happening with reasoning models and inference-time compute right now?"

→ search_ai_trends(topic="reasoning models inference-time compute")

Skill Level Guide

Data Storage

All data is stored locally as JSON at ~/developer-growth/ (or your configured dataPath):

~/developer-growth/
  growth.json          # skills, sessions, resources, goals
  growth-report-*.txt  # exported reports

No data leaves your machine. No API keys required.

What the LLM Era Means for Developers

AI is replacing: Boilerplate code, simple CRUD, basic scripting, routine data transformation

AI is amplifying: System design, architecture decisions, domain expertise, evaluation and judgment

Timeless skills (more valuable than ever): Problem decomposition, deep CS fundamentals, communication, debugging mental models

The developers who thrive won't be the ones who know the most syntax — they'll be the ones who can decompose hard problems, evaluate AI output critically, design reliable systems, and ship products that matter. This plugin helps you build exactly those skills.