skills-but-for-dev
Queries GitHub repositories to discover and rank technology candidates using repository search, star velocity and acceleration metrics, health signals, and license information.
Searches Hugging Face models and estimates VRAM requirements across safetensors, sharded weights, Diffusers pipelines, and GGUF quantization levels.
Fetches PyPI monthly download statistics via pypistats.org to verify real-world production adoption of Python packages.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@skills-but-for-devFind a maintained MIT-licensed Python speech-to-text library that fits in 8GB VRAM"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
skills-but-for-dev
β‘ The Problem: LLM Knowledge Cutoff & Dead Repositories
When you ask Cursor, Claude Code, Antigravity, or Copilot:
"Build an AI meeting assistant that records audio, separates speakers, and generates summaries"
The model immediately starts writing code using libraries from its static pre-training data. Meanwhile:
π High-star repos might be dead: A project with 35,000 stars might have had zero commits in 18 months.
π Breakout libraries remain invisible: The state-of-the-art voice model or fast audio engine released last month doesn't exist in the LLM's weights.
βοΈ Hidden licensing traps: Generating code that imports
GPL-3.0orCC-BY-NC-4.0libraries can create major intellectual property and commercial liabilities.π₯ VRAM crashes: AI agents frequently recommend 14B parameter models on a local machine with only 8 GB of VRAM.
skills-but-for-dev fixes this. It acts as an autonomous pre-flight curator for your agent, querying live GitHub velocity, PyPI monthly adoption, and Hugging Face VRAM requirements before writing code.
Related MCP server: repo-scout
π₯ Before & After
Task: "Build a local meeting notes agent" | β Vanilla AI Agent (Without | β
AI Agent + |
Transcription (ASR) | Suggests |
|
Speaker Diarization | Tries to hand-code clustering or hallucinates non-existent APIs |
|
Licensing | Generates code importing | 100% Permissive Verification (Flags MIT / Apache-2.0, filters restrictive licenses) |
Local Hardware Check | Recommends large Whisper model, crashing local GPU | Automatic VRAM Baseline (Estimates 0.54 GB FP16 ceiling for |
π οΈ Key Capabilities
Dual-Bucket Candidate Retrieval: Discovers proven industry leaders (
stars:>500) alongside fast-rising breakout repositories (stars:75..3500, pushed $< 30$ days) without cannibalization.Real-World PyPI Production Adoption: Fetches real monthly download statistics via
pypistats.orgto protect against star-inflation and GitHub bots.Real-Time Star Velocity & Acceleration: Calculates 4-week moving average ($v_4$) and acceleration ratio ($v_4 / v_{12}$) in a single request.
Dormant Spike Anomaly Detection: Flags suspicious repositories that were inactive for 6+ months and suddenly spiked in stars over a single week.
Hardware VRAM Estimator: Accurate minimum VRAM baseline calculation across
.safetensors, sharded model weights, Diffusers pipelines, and distinct GGUF quantization levels (Q4_K_M,Q8_0).Zero-Config GitHub Token Auto-Discovery: Seamlessly discovers existing GitHub CLI logins (
gh auth token) or falls back to an adaptive, quota-conserving unauthenticated mode.Anti-Prompt-Injection Sanitizer: Strips malicious hidden markdown instructions, zero-width tags, and bloated YAML headers before context reaches your LLM.
Sub-5ms SQLite WAL Cache: Deterministic, persistent local cache with automatic TTL expiration.
π Architecture & Data Flow
flowchart TD
User["Developer Prompt\n'Build a RAG system for PDF invoices'"] --> Agent["AI Coding Agent\n(Cursor / Antigravity / Claude Code)"]
Skill["SKILL.md"] -.->|"Decomposition Instructions"| Agent
Agent -->|"1. search_github_repos('pdf parser')\n2. search_hf_models('embeddings')"| Server["skills-but-for-dev MCP Server"]
subgraph Server_Internals["skills-but-for-dev Engine"]
Server --> Norm["Query Normalizer\n(Noise stripping & Language filter)"]
Norm --> GH["GitHub REST Client\n(Dual-Bucket search + Star Velocity)"]
Norm --> HF["Hugging Face Client\n(VRAM & Pipeline Estimator)"]
Norm --> PyPI["PyPI Stats Client\n(30-Day Download Verification)"]
GH --> Scoring["Conservative Scoring Engine\n(80% Base + 10% Health + 10% PyPI)"]
HF --> Scoring
PyPI --> Scoring
Scoring --> Cache[("SQLite Cache (WAL Mode)\n~4.7ms warm latency")]
end
Server -->|"Curated Architecture Recipe"| Agent
Agent -->|"Step-by-step Tech Stack with Verified Licences"| Userπ Quickstart
1. Run Instantly with uvx (Zero Installation)
No cloning or virtual environment setup required. Works with any MCP-compatible client:
# Verify it runs and check diagnostics
uvx skills-but-for-dev ping2. Configure in Your IDE
πΉ Cursor (.cursor/mcp.json)
{
"mcpServers": {
"skills-but-for-dev": {
"command": "uvx",
"args": ["skills-but-for-dev"],
"env": {
"GITHUB_TOKEN": "optional_your_pat_here"
}
}
}
}πΉ Antigravity / Gemini (mcp_config.json)
{
"mcpServers": {
"skills-but-for-dev": {
"command": "uvx",
"args": ["skills-but-for-dev"]
}
}
}πΉ Claude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"skills-but-for-dev": {
"command": "uvx",
"args": ["skills-but-for-dev"]
}
}
}Tip: If you already use the GitHub CLI (
gh),skills-but-for-devautomatically detects your authentication token viagh auth token. You don't even need to configureGITHUB_TOKENmanually!
π‘ Example Agent Stack Recipe
When your agent uses skills-but-for-dev, it generates structured architecture recipes before writing code:
Layer | Recommended Component | Star Velocity | Real-World PyPI Adoption | License | Hardware / Memory |
ASR (Speech-to-Text) | $v_4 = 18.2$/day (accel: 1.15) | 9,614,222 downloads/mo | MIT | ~0.54 GB VRAM (small FP16) | |
Speaker Diarization | $v_4 = 8.4$/day (accel: 1.20) | 272,534 downloads/mo | MIT | ~1.2 GB VRAM | |
Embeddings | $v_4 = 14.1$/day (accel: 1.05) | 22,332,616 downloads/mo | Apache-2.0 | ~0.62 GB VRAM |
β‘ Performance & Evaluation Benchmarks
Benchmarked across realistic multi-layer agent scenarios using strict Ground Truth matching:
Metric | Result | Benchmark Details |
Warm Cache Latency | 4.73 ms | Sub-5 millisecond response time via SQLite WAL mode |
Cold API Latency | 1,728 ms | Optimized single-flight queries with adaptive quotas |
Cache Speedup | 365.7x | Massive latency reduction for recurring architecture queries |
Commercial License Safety | 95.2% | Automatic filtering of GPL / CC-BY-NC restrictive licenses |
PyPI Download Signal Rate | 52.4% | Over half of returned candidates enriched with production downloads |
Unit Test Coverage | 40 / 40 Passing | Comprehensive suites for normalizer, PyPI client, cache, and query relaxation |
You can run the evaluation suite locally at any time:
python evals/run_evals.pyπ§° CLI Tools & Diagnostics
Inspect active tokens, cache paths, and server health directly from your terminal:
# Print active version
skills-but-for-dev --version
# Ping server and verify token discovery source (ENV vs GitHub CLI)
skills-but-for-dev pingπ Architecture Deep Dive
For mathematical formulas (star velocity moving averages, exponential recency decay, dormant spike detection ratios), see ARCHITECTURE.md.
π License
Distributed under the Apache License 2.0.
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