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AwsomeContext — Give Claude Code the engineering knowledge of a senior team — architecture patterns, coding standards, security audits — injected automatically, without burning your context window.
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Give Claude Code the engineering knowledge of a senior team — architecture patterns, coding standards, security audits — injected automatically, without burning your context window.
# 1. Install the plugin
claude plugin install awesome-context
# 2. Set your API key (get one free at awesomecontext.awareness.market)
export AC_API_KEY=ac_your_key_here
# 3. Load rules for your project
/awesome-context:rules
Claude Code is powerful, but it doesn't know your team's rules.
Every time you start a session, you're back to square one: manually pasting coding standards, reminding it about your tech stack, explaining security requirements, copy-pasting architectural guidance. And even when you do, long context windows cause Claude to "forget" rules as the conversation grows.
The result: AI-generated code that works, but violates your conventions, skips security checks, or picks the wrong architecture pattern.
AwesomeContext is an MCP server for Claude Code that auto-injects 128+ engineering rules, patterns, and skills — right when Claude needs them.
All of this happens in the background. You just write code.
The key innovation: Instead of injecting raw Markdown documents (which cost thousands of tokens each), AwesomeContext compresses rules into neural tensors and decodes them on-demand — achieving 96-99% token savings while maintaining full semantic accuracy.
| User | Pain Point | How AwesomeContext Helps |
|---|---|---|
| Individual developer | Claude writes code that doesn't follow your standards | Auto-loads your rules every session |
| Tech lead | Team using AI gets inconsistent, non-standard output | One shared rule set, injected for everyone |
| Security engineer | Claude doesn't know domain-specific vulnerability patterns | Injects specialized security checklists on demand |
| Consultant / freelancer | Different client standards per project | Load project-specific rules per session |
No GPU, no Docker, no compilation. Just install and use.
Sign up at awesomecontext.awareness.market and copy your key.
claude plugin install awesome-context
export AC_API_KEY=ac_your_key_here
Open Claude Code on any project and run:
/awesome-context:rules # Auto-detect your stack and load relevant rules
That's it. Claude now knows your coding standards, security requirements, and best practices for this session.
/awesome-context:rules — Load your rulesAuto-detects your tech stack (TypeScript, Python, Go, React, Django, etc.) and loads the right coding standards, security guidelines, and patterns.
You: /awesome-context:rules
→ Detected: TypeScript + React + Next.js
→ Loaded: TypeScript strict mode rules, React hook conventions,
Next.js App Router patterns, OWASP web security checklist
→ Claude now knows the rules for this project.
/awesome-context:architect — Get architecture guidance before you buildBefore writing code for a new feature, ask for architecture patterns. Claude gets production-grade decision frameworks injected, so it can reason through trade-offs properly.
You: /awesome-context:architect
You: I need to add real-time notifications to my app.
Claude: Here's the analysis for your Next.js + Postgres stack:
→ WebSocket: Best for bi-directional, many simultaneous connections
→ SSE: Simpler, HTTP-based, good for server-push only (your case)
→ Polling: Only if you need IE11 support
Recommendation: SSE via Next.js Route Handlers, here's the pattern...
/awesome-context:review — Check code before commitClaude reviews your staged changes against the loaded rules — catching style violations, security issues, and anti-patterns before they hit your repo.
You: /awesome-context:review
Claude: Found 2 issues in your staged changes:
✗ Line 47: Raw SQL string concatenation — SQL injection risk (OWASP A03)
Fix: Use parameterized queries → db.query('SELECT * FROM users WHERE id = $1', [id])
✗ Line 83: console.log left in production code
Fix: Use your logger utility instead
✓ Everything else looks good.
/awesome-context:security — Deep security auditInjects specialized security knowledge for your domain: OWASP web vulnerabilities, DeFi smart contract exploits, cryptographic pitfalls, access control review.
You: /awesome-context:security
You: Audit this Solidity contract for me.
Claude: Running DeFi security checklist:
✗ Reentrancy vulnerability on line 34 — update state before external calls
✗ Integer overflow on line 67 — use SafeMath or Solidity 0.8+
✗ Access control missing on withdrawAll() — add onlyOwner modifier
✓ No tx.origin authentication issues found
Beyond slash commands, the plugin registers 4 tools that Claude calls automatically based on context — no manual invocation needed:
| Tool | When Claude calls it | What it does |
|---|---|---|
get_rules | At conversation start | Loads your project's tech stack rules |
architect_consult | Before design decisions | Injects architecture patterns and trade-offs |
skill_injector | For domain-specific tasks | Pulls security, testing, or infra knowledge |
compliance_verify | When reviewing code | Checks against loaded standards |
These tools are described in the plugin so Claude knows when to call them — you don't have to remember to ask.
Languages: TypeScript, Python, Go, Java, Rust, C++, Solidity
Frameworks: React, Next.js, Django, FastAPI, Spring Boot, Express
Infrastructure: Docker, CI/CD, Postgres, ClickHouse
Domains: DeFi / Smart Contracts, OWASP Security, REST API Design, TDD, Database Migrations
| Category | Count | Examples |
|---|---|---|
| Coding Rules | 12+ | TypeScript strict mode, Python style, Go idioms, Java patterns |
| Skills | 43+ | TDD workflow, security review, DeFi audit, API design |
| Agents | 13 | Architecture reviewer, security auditor, code reviewer, test guide |
| Commands | 31+ | Plan, build-fix, e2e, checkpoint, refactor |
| Hooks | 1 | Pre-commit checks |
Browse all modules: /awesome-context:skills
If you want to run everything locally — your own model, your own rules, no cloud dependency.
git clone --recurse-submodules https://github.com/everest-an/AwesomeContext.git
cd AwesomeContext
# Python dependencies
pip install -e ".[dev]"
# MCP server dependencies
cd mcp-server && npm install && cd ..
This is a one-time step that encodes all 128 rule modules into compressed tensors:
# Preview what will be compiled (no model loading)
python scripts/compile.py --dry-run
# Full compilation (~17-47 min on GPU, downloads model on first run)
python scripts/compile.py
# Incremental — only recompile changed files
python scripts/compile.py --delta
Found 128 modules (13 agents, 43 skills, 12 rules, 31 commands, ...)
Loading model: Qwen/Qwen3-4B (device=cuda, dtype=torch.bfloat16)
Compiling: 100%|████████████████| 128/128 [17:42<00:00, 8.30s/module]
Compilation complete. 128 modules → data/tensors/
python scripts/serve.py
# Running at http://127.0.0.1:8420
cd mcp-server
npm run build
npm start
Add to ~/.config/claude-code/mcp.json (or your project's MCP config):
{
"mcpServers": {
"awesome-context": {
"command": "node",
"args": ["path/to/AwesomeContext/mcp-server/dist/index.js"],
"env": {
"AC_API": "http://127.0.0.1:8420"
}
}
}
}
Standard approach: inject full Markdown rule files into Claude's context — 2,000–5,000 tokens per rule, lost as the conversation grows.
AwesomeContext approach:
Result: the same semantic content delivered in < 150 tokens instead of 5,000+.
User Intent → Cosine Search → Load Tensor (mmap) → Decode → Dense Prompt (< 150 tokens)
| Model | Hardware | Memory | Use Case |
|---|---|---|---|
| Qwen3-4B (default) | GPU ≥ 8GB | ~8GB bf16 | Best balance |
| Qwen3-14B | GPU ≥ 24GB | ~28GB bf16 | Highest fidelity |
| Qwen2.5-Coder-1.5B | CPU | ~6GB fp32 | No GPU needed |
Auto-detection: uses Qwen3-4B if CUDA is available, otherwise falls back to Qwen2.5-1.5B. Override with AC_MODEL.
| Scenario | Raw Tokens | After AwesomeContext | Savings |
|---|---|---|---|
| Single skill | 2,000–5,000 | < 150 | 96–97% |
| Architecture consult (3 modules) | 6,000–15,000 | < 150 | 98–99% |
| Compliance check (5 rules) | 10,000–25,000 | < 150 | 99%+ |
| Operation | Time |
|---|---|
| First query (model warm-up) | 1.5–5s |
| Cached query | < 100ms |
| Compilation (one-time, per module) | 10–28s on GPU |
POST /v1/latent/queryUsed by all MCP tools internally.
Request:
{
"intent": "implement debounced search",
"session_id": "sess_abc123",
"tool_name": "architect_consult",
"top_k": 3
}
Response:
{
"dense_prompt": "【Injected Rules】: 1. Use React Hook conventions; 2. Reference utils/debounce...",
"metrics": {
"tokens_saved": 1250,
"retrieval_time_ms": 0.8,
"total_time_ms": 1920.1,
"modules_matched": 3
},
"matched_modules": [
{ "module_id": "skills/coding-standards", "score": 0.91 }
]
}
GET /v1/modules/list — List all compiled modulesGET /v1/health — Health checkGET /v1/metrics — Performance stats| Variable | Default | Description |
|---|---|---|
AC_API_KEY | — | Cloud API key (plugin mode) |
AC_MODEL | Auto-detect | Model override (Qwen/Qwen3-4B, Qwen/Qwen3-14B, Qwen/Qwen2.5-Coder-1.5B-Instruct) |
AC_API | http://127.0.0.1:8420 | Backend URL (self-hosted MCP serv |