context▌

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity. Effective optimization can double or triple effective context capacity without requiring larger models or longer contexts.

Diagnose whether your AI workflows are context stuffing or context engineering, then implement structured practices to improve output quality. \n \n Distinguishes between context stuffing (volume-based) and context engineering (structure-based), with five diagnostic questions to identify Context Hoarding Disorder \n Guides two-layer memory architecture: short-term conversational memory plus long-term persistent memory via vector databases for semantic retrieval \n Implements the Research→Plan→Re

Ask an AI assistant what files it needs before answering your question. \n \n Prompts the assistant to identify required and optional files for context before attempting to answer \n Structures output into four categories: must-see files, helpful files, already-seen files, and remaining uncertainties \n Helps developers avoid incomplete answers by ensuring the assistant has examined relevant code upfront \n Reduces back-and-forth by clarifying dependencies and gaps in context before the actual q

Analyze codebases and map task-relevant files before implementing changes. \n \n Automatically searches for related files, dependencies, tests, and similar code patterns to establish full context \n Generates structured markdown output with files to modify, dependency relationships, test coverage, and reference patterns \n Includes risk assessment checklist for breaking changes, migrations, and configuration updates \n Designed as a pre-implementation step to prevent missed dependencies and unin

Context engineering curates the smallest high-signal token set for LLM tasks. The goal: maximize reasoning quality while minimizing token usage.

Establish foundational product positioning and audience context that other marketing skills reference. \n \n Captures product overview, target audience, personas, problems, competitive landscape, differentiation, objections, and brand voice in a single .agents/product-marketing-context.md document \n Offers two setup paths: auto-draft from your codebase (README, landing pages, marketing copy) for faster iteration, or conversational walkthrough for custom input \n Emphasizes capturing verbatim cu

税務法的コンテキスト（Tax Legal Context） \n このスキルは shinkoku の税務関連回答における法的・免責コンテキストを提供する。 \n 免責事項の提示 \n 免責事項が必要な回答を行う際は、 references/disclaimer.md を読み込んで以下を実行する: \n \n references/disclaimer.md の「標準免責文」を回答末尾に付記する \n 免責を強調すべきケース（グレーゾーン・高額案件等）に該当する場合は追加注意喚起を行う \n 税理士法第52条の観点から、個別具体的な税務代理行為に該当しないよう留意する \n \n 参照ファイル \n \n \n \n ファイル \n 内容 \n \n \n \n \n references/disclaimer.md \n 標準免責文・税理士法との関係・ツール制限事項・情報の正確性 \n \n \n

The agent rides the wave of peak context. When the wave crests, it commits. When it detects drift, it pulls out cleanly — saving state, handing off, and letting the next session catch the next wave.

Help a user create the Instructions field content for their Sanity Agent Context MCP. The goal is a concise set of pure deltas — only information the agent can't figure out from the auto-generated schema.

A toolkit that works with Claude Code's natural flow. Use what helps, ignore what doesn't.