Massive Context Mcp
Handles 10M+ token contexts with chunking, sub-queries, and local Ollama inference.
tsa-find
Fast file + content search with code-aware sizing. Replaces Read/Grep/find for routine "where is this file" / "grep for X" / "show me lines 10-20 of Y" questions. Returns file paths + line numbers + a sized chunk, not the whole file. Use when: - "Find files matching <pattern>" / "show me all *.yml under config/" - "Grep for 'TODO' / 'FIXME' / 'TODO\\(perf\\)' / regex anywhere" - "How big is <file>" / "is this file too large to read fully" - "Show me lines 50-80 of <file>" / "read just the relevant slice" - "Find all files matching name + containing string" Replaces: native find + grep + cat invocations (~3-10k tokens for big repos) with single MCP calls (200-500 tokens).
runaway-context
Use this skill when working in a project that has a RunawayContext v3 install. It loads the project's brief from the auto-generated Tier 3 markdown, queries `knowledge.db` for relevant lessons and chunks, and routes writes through the contract-enforced Client (HR-2 / HR-3 / HR-9). Trigger when the conversation enters a new project directory, when the user asks for context on a slug, when correcting AI behavior to capture as a draft, or when the user invokes `/runaway:*` commands.
hyperfocus
ADHD-friendly output formatting for Codex. Restructures responses with evidence-based cognitive accessibility: chunking, visual hierarchy, front-loaded key points, and progressive disclosure. Three modes: clean, flow (default), zen. Use when user says "hyperfocus", "focus mode", "adhd mode", "adhd friendly", or invokes /hyperfocus.
ai-seo-optimization
Optimize content and websites for AI search engines (ChatGPT, Perplexity, Google AI Overviews) using GEO principles, content chunking, structured data, and brand visibility strategies. Use when working on SEO, AI visibility, content optimization, GEO, getting brand mentioned in AI, or implementing technical SEO for LLM search.
shelfai
Use this skill when the user wants to manage AI agent knowledge, organize agent configurations, or set up post-session learning systems. Triggers include: organizing agent skills or knowledge into a structured shelf, extracting insights from conversation transcripts, splitting monolithic agent config files into modular pieces, managing memory files, searching indexed knowledge, or diagnosing shelf health. Use ShelfAI whenever a user mentions managing agent context, chunking configs, learning from sessions, compacting memory, or organizing reusable agent knowledge pieces.