Skillget

knowledge-space

mcp
Security Audit
Warn
Health Warn
  • No license — Repository has no license file
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 5 GitHub stars
Code Warn
  • network request — Outbound network request in docs/javascripts/graph.js
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This project is a curated, static technical knowledge base containing over 700 articles across 27 software engineering domains. It is designed to provide dense, structured references optimized for RAG retrieval and context injection via MCP tools.

Security Assessment
The overall risk is Low. The repository does not request dangerous system permissions, execute shell commands, or access sensitive local data. There are no hardcoded secrets. The only flagged security finding is an outbound network request located in `docs/javascripts/graph.js`. This is standard for a static site generator or documentation page (likely for fetching a script or rendering a graph) and does not pose a threat to the host machine.

Quality Assessment
The project is actively maintained, with the most recent code push occurring today. However, it suffers from low community visibility, currently sitting at only 5 GitHub stars. More importantly, the repository lacks a designated open-source license file. This means that while you can personally read and use the content, formal legal rights to redistribute, modify, or commercially integrate the material into your own AI projects are technically undefined.

Verdict
Safe to use as a reference, but use with caution regarding redistribution due to the missing software license.
SUMMARY

Curated technical knowledge base. 683+ articles across 27 domains - dense references for AI agents and engineers. Site: happyin.space

README.md

logo Happyin Knowledge Space

Curated technical knowledge base across 27 domains. Agent-first design - dense, structured references optimized for RAG retrieval, MCP tools, and context injection.

731+ articles | 27 domains | 2100+ cross-references

Live site

What's inside

Domain Articles Coverage
data-science/ 85 ML, statistics, neural networks, CV, NLP, math foundations
python/ 43 Core language, FastAPI, Django, async, testing, packaging
web-frontend/ 40 React, TypeScript, CSS, Figma, bundlers, accessibility
devops/ 39 Docker, Kubernetes, Terraform, CI/CD, monitoring, SRE
architecture/ 39 Microservices, DDD, system design, API patterns, CQRS
data-engineering/ 38 ETL/ELT, Spark, Airflow, data warehouses, streaming, CDC
kafka/ 33 Broker internals, consumers, producers, Streams, KSQL, Connect
sql-databases/ 27 PostgreSQL, MySQL, query optimization, migrations, indexing
linux-cli/ 25 Shell scripting, filesystem, systemd, permissions, networking
llm-agents/ 24 RAG, fine-tuning, agent frameworks, prompt engineering, embeddings
java-spring/ 21 Spring Boot, JPA, microservices, Kotlin, Android
bi-analytics/ 21 Tableau, Power BI, SQL analytics, dashboards, product analytics
algorithms/ 19 Sorting, graphs, DP, data structures, complexity analysis
security/ 18 Web security, penetration testing, Active Directory, anti-fraud
seo-marketing/ 16 Technical SEO, keyword research, link building, AI-driven SEO
testing-qa/ 15 Selenium, Playwright, API testing, CI integration, test design
rust/ 14 Ownership, lifetimes, async, error handling, unsafe
php/ 12 Laravel, MVC, ORM, testing, PHP 8 features
nodejs/ 10 Event loop, streams, clusters, performance, design patterns
ios-mobile/ 10 SwiftUI, Swift, Android/Kotlin fundamentals
image-generation/ 27 Diffusion models, flow matching, LoRA training, inpainting
misc/ 9 JavaScript, Go, competitive programming, interview prep

For AI agents

Quick access via sandbox

Upload the repo into a ConTree sandbox (or any other isolated environment you prefer) and query it via MCP tools - search, read, and analyze articles:

# Upload to ConTree sandbox
contree upload --path ./docs

# Search across all domains
contree search "kafka consumer rebalancing"

# Read specific article
contree read docs/kafka/consumer-groups.md

Direct file access

Clone and point your agent at it:

git clone https://github.com/AnastasiyaW/knowledge-space.git

Each article is a standalone .md file - easy to index, retrieve, and inject into LLM context. Articles cross-reference each other with [[wiki-links]] forming a navigable knowledge graph.

Article format

Every article follows a consistent structure optimized for machine consumption:

# Consumer Groups

## Key Facts
- Bullets with [[wiki links]]

## Patterns
[Code. Configs. Commands. Runnable.]

## Gotchas
[symptom -> cause -> fix]

## See Also
[Cross-references + official docs]

Freshness policy

Not all knowledge ages equally. Each domain has an update cycle:

Cycle Domains
Stable (fundamentals) Algorithms, Architecture, Linux CLI
Yearly SQL, Kafka, Rust, Java/Spring, PHP, Node.js, Testing, BI, Data Engineering
Every 6 months Web Frontend, DevOps, LLM/RAG, iOS, Security, SEO
Monthly Image Generation, Agent Frameworks

Articles include version context where relevant (e.g., "PostgreSQL 17", "React 19").

Contributing

We accept contributions from both AI agents and humans. See CONTRIBUTING.md for the full guide.

Quick version:

  1. Fork the repo
  2. Create/update an article in docs/{domain}/
  3. Follow the article format (dense reference, not tutorial)
  4. Submit a PR

For agents submitting findings

If you're an agent that discovered outdated or missing information:

  1. Branch: update/{domain}/{topic-slug}
  2. Format: follow the article structure above - compress, no filler
  3. PR: include what changed, why, and source links
  4. Forbidden: course names, instructor names, tutorial prose, marketing language

Automated validation checks run on every PR.

License

MIT

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