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skills-but-for-dev


⚑ 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:

  • πŸ’€ High-star repos might be dead: A project with 35,000 stars might have had zero commits in 18 months.

  • πŸš€ 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.

  • βš–οΈ Hidden licensing traps: Generating code that imports GPL-3.0 or CC-BY-NC-4.0 libraries can create major intellectual property and commercial liabilities.

  • πŸ’₯ 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: Implementation Intelligence MCP

πŸ₯Š Before & After

Task: "Build a local meeting notes agent"

❌ Vanilla AI Agent (Without skills-but-for-dev)

βœ… AI Agent + skills-but-for-dev

Transcription (ASR)

Suggests SpeechRecognition (deprecated Python 2 era wrapper)

Systran/faster-whisper (9.6M/mo PyPI downloads, 4x faster CTranslate2)

Speaker Diarization

Tries to hand-code clustering or hallucinates non-existent APIs

modelscope/FunASR / pyannote.audio (Verified active acceleration: $v_4 / v_{12} = 1.2$)

Licensing

Generates code importing GPL / non-commercial packages

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 faster-whisper-small)


πŸ› 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.org to 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.


πŸ“‹ 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"| User

πŸš€ 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 ping

2. Configure in Your IDE

πŸ”Ή Cursor (.cursor/mcp.json)

{
  "mcpServers": {
    "skills-but-for-dev": {
      "command": "uvx",
      "args": ["skills-but-for-dev"],
      "env": {
        "GITHUB_TOKEN": "optional_your_pat_here"
      }
    }
  }
}

πŸ”Ή Antigravity / Gemini (mcp_config.json)

{
  "mcpServers": {
    "skills-but-for-dev": {
      "command": "uvx",
      "args": ["skills-but-for-dev"]
    }
  }
}

πŸ”Ή 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-dev automatically detects your authentication token via gh auth token. You don't even need to configure GITHUB_TOKEN manually!


πŸ’‘ 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)

SYSTRAN/faster-whisper

$v_4 = 18.2$/day (accel: 1.15)

9,614,222 downloads/mo

MIT

~0.54 GB VRAM (small FP16)

Speaker Diarization

modelscope/FunASR

$v_4 = 8.4$/day (accel: 1.20)

272,534 downloads/mo

MIT

~1.2 GB VRAM

Embeddings

sentence-transformers

$v_4 = 14.1$/day (accel: 1.05)

22,332,616 downloads/mo

Apache-2.0

~0.62 GB VRAM


⚑ 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.py

🧰 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 ping

πŸ“– Architecture Deep Dive

For mathematical formulas (star velocity moving averages, exponential recency decay, dormant spike detection ratios), see ARCHITECTURE.md.


πŸ“„ License

Distributed under the Apache License 2.0.

Available Tools

7 tools
compare_candidatesCompare CandidatesB

Compare a list of repository or model identifiers side-by-side in a structured comparison table.

Inputs: ids (required). Returns: object

get_readmeGet ReadmeA

Fetch and sanitize documentation for a GitHub repo or Hugging Face model/Space.

IMPORTANT SAFETY: Output is sanitized against prompt injection and wrapped in <untrusted_content> tags. Giant benchmark tables and YAML frontmatter blocks are omitted to prioritize Usage and Quickstart sections.

Inputs: id (required), source (required), max_chars. Returns: result

get_repo_healthGet Repo HealthC

Fetch in-depth maintenance and responsiveness health metrics for a specific GitHub repository.

Inputs: full_name (required). Returns: object

pingPingA

Check if the skills-but-for-dev MCP server is operational and view version & token status.

Inputs: no named parameters. Returns: result

search_github_reposSearch Github ReposA

Search for fresh, healthy, and license-compliant GitHub repositories for an architecture stack layer.

When to use:

  • Before writing code for any project layer (e.g., 'speaker diarization', 'meeting bot').

  • To discover both proven industry leaders and fast-rising breakout repositories.

Inputs: query (required), limit, topics, language, +1 more. Returns: result

search_hf_modelsSearch Hf ModelsA

Search for open-source AI models on Hugging Face Hub with hardware/VRAM requirements.

When to use:

  • Looking for ML/AI models for a specific task (e.g. 'automatic-speech-recognition', 'text-to-speech').

  • User has a local GPU limit (e.g. max_vram_gb=12.0) or needs commercial licensing.

Inputs: query (required), task, limit, max_vram_gb, +1 more. Returns: result

search_hf_spacesSearch Hf SpacesB

Search Hugging Face Spaces for running web demos, reference architectures, or prototypes.

When to use:

  • User wants to see an interactive working demonstration of a model/framework in action.

Inputs: query (required), limit. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.7/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool maps to a distinct resource+action: repo health for a single repo, search across three separate sources (GitHub repos, HF models, HF spaces), readme fetching, comparison, and a server ping. The three search_* tools are cleanly partitioned by source type, and get_repo_health vs search_github_repos is clearly differentiated by single-repo inspection vs discovery.

    Naming Consistency4/5

    Nearly all tools follow a verb_noun pattern (get_repo_health, search_github_repos, search_hf_models, search_hf_spaces, get_readme, compare_candidates). The bare 'ping' is a conventional outlier, and 'repo' vs 'github_repos' abbreviates the same concept differently across tools.

    Tool Count5/5

    Seven tools is well-scoped for a discovery/evaluation server: three parallel search tools, two single-item fetchers, one comparison tool, and a health check. Every tool earns its place with no redundancy.

    Completeness4/5

    The discovery-to-evaluation workflow is well covered: search repos/models/spaces, read docs, check repo health, and compare candidates. Minor gap: no HF-side popularity/recency metrics tool (e.g. downloads/likes for models or spaces) equivalent to get_repo_health, though search results may partially compensate.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

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