massive-context-mcp
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.
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443 skills found
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.
Outputs knowledge logs capturing project structure, implicit rules, and logic gaps during sessions.
Audit and synchronize the supported LLM model list in assets.py against the authoritative litellm registry.
Comprehensive reference for GrepAI configuration, detailing the .grepai/config.yaml schema, embedder settings, storage backends, and optimization parameters.
The foundational skill for the Superpowers methodology. Ensures agents correctly identify and invoke required development skills before starting any task or conversation.
Automated single-cell RNA-seq quality control pipeline following scverse best practices. Performs MAD-based outlier detection, cell filtering, and diagnostic visualization for .h5ad and .h5 datasets.
Enhance fuzzer effectiveness by providing domain-specific tokens, magic bytes, and protocol-specific keywords to reach deep code paths.
Resets workflow artifacts in the .otto/ directory. Safely removes tasks, specs, and browser sessions for a clean start.
Extract and document authentic writing voice from samples. Create comprehensive voice guides for AI training, ghostwriting, and brand consistency.
Audit outbound network requests and detect data exfiltration patterns in OpenClaw skills to ensure secure outbound communication.
Analyze business contracts for risks, gaps, and unfavorable terms. Generate structured risk reports for NDAs, MSAs, SaaS agreements, and SOWs with actionable redline recommendations.
Unified local ML inference server for ASR, TTS, Translation, Image Generation, and Vision on Apple Silicon, powered by MLX.