LC

    local codegen workflow

    1

    Generate production code with a local LLM on the GPU you already own. Spec-driven codegen via LM Studio/Ollama/vLLM, with auto model-loading, a 13-bug review checklist, and a working genfile script — all model-agnostic.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more
    LC

    local codegen workflow

    Example session with this skill installed

    I need a Python module for an LRU cache. It should use the standard library only, support a max_size, and have get/put methods. Save it to lru_cache.py using the local Qwen model.

    • Read your context and instructions
    • Compiled the local codegen workflow

    I have generated the lru_cache.py module using the local qwen2.5-coder-14b-instruct model. The model was pre-loaded at a 32k context window to ensure VRAM stability.

    Generated LRU Cache Module

    from collections import OrderedDict

class LRUCache:
    """
    A simple LRU Cache using OrderedDict.
    """
    def __init__(self, max_size: int = 128):
        if max_size <= 0:
            raise ValueError("max_size must be positive")
        self.cache = OrderedDict()
        self.max_size = max_size

    def get(self, key):
        if key not in self.cache:
            return None
        self.cache.move_to_end(key)
        return self.cache[key]

    def put(self, key, value):
        if key in self.cache:
            self.cache.move_to_end(key)
        self.cache[key] = value
        if len(self.cache) > self.max_size:
            self.cache.popitem(last=False)

if __name__ == "__main__":
    # Smoke test
    lru = LRUCache(2)
    lru.put(1, "a")
    lru.put(2, "b")
    print(lru.get(1))  # a
    lru.put(3, "c")    # evicts 2
    print(lru.get(2))  # None

    Next steps

    1. Run pytest on the generated module to verify eviction logic.
    2. Review the OrderedDict implementation against your performance requirements.
    3. Commit with the message feat: add lru_cache (AI-gen, reviewed).

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    About this skill

    The problem

    Paying per-token API costs for repetitive boilerplate and large-scale code generation is expensive. Local LLMs often fail during generation due to OOM errors because they are loaded with default context windows that exceed available VRAM.

    What it does

    • Automates the spec-driven generation of production code using local LLM servers.
    • Manages VRAM-safe model loading via LM Studio v1 REST API to prevent OOM "terminated" errors.
    • Enforces a senior-level review checklist to catch common local model fumbles like connection leaks and empty-list SQL crashes.
    • Implements a 2-strike rule to prevent time-wasting loops on low-quality generations.
    • Generates both the implementation module and its corresponding test suite from a single source-of-truth spec.

    Frameworks & tools

    Python 3.11, httpx, LM Studio, Ollama, vLLM, Qwen2.5-Coder, DeepSeek-Coder.

    Why this beats prompting it yourself

    Standard prompting often results in code blocks wrapped in conversational prose that break automation scripts. This skill includes a specialized extraction layer and a model-management handshake that ensures the model is actually loaded at a stable context length before the first token is even generated.

    Use cases

    • Generating large volumes of boilerplate for new microservices without incurring API fees.
    • Prototyping internal tools where data privacy prevents sending code to third-party APIs.
    • Setting up a repeatable generate-review-test-commit pipeline for junior-level tasks.

    Known limitations

    Not recommended for surgical edits to existing complex files. Requires a GPU with at least 8GB VRAM for 7B models or 16GB for 14B models.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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