'context-engineering' 검색 결과

Runtime router for context decisions during a task — decide read-more / stop / ask / assume / delegate, pick a minimal read-set, and time compaction. Front-page mental model is Write / Select / Compress / Isolate; a lazy pattern index routes to 25 context-engineering patterns, one reference per decision. Use when context is near budget, a long session is degrading, you must choose what to read for a task, or you are deciding /compact vs /clear vs a sub-agent. NOT for structural .claude/ overhead audits (see mk:context-audit); NOT for owning persistence (see mk:memory); NOT for USD cost (see mk:budget).

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Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

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• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Learn how instructions, documents, tool results, and conversation history form an AI model’s context. Understand attention limits, why information in the middle can be missed, and how relevance and placement inform decisions about what to include.

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• Learning the basics of context engineering
• Understanding why more input does not always help
• Explaining how instruction placement affects attention

Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.

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Audits and reorganises what an AI coding agent carries in its context window: instruction files, path-scoped rules, skills, subagents, hooks and the running conversation. Use when someone says "the agent ignores our CLAUDE.md", "set this repo up for Claude Code / Codex / Cursor", "my AGENTS.md is too long", "quality drops in long sessions", "it keeps inventing APIs", "what should go in a skill versus the rules file", or asks how to hand work to a subagent. Grounded in the documented loading behaviour of Claude Code, with the equivalents for Codex, Gemini CLI and Cursor.

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Expert en ingénierie du contexte IA (compression de prompts, chunking, gestion tokens, optimisation fenêtre)

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작업에 맞춰 AI 에이전트에 제공할 규칙, 명세, 관련 코드를 선택합니다. 긴 대화의 정보를 정리하고 결정 사항과 검증 결과를 다음 세션에 인계합니다.

• AI 개발에 필요한 규칙 정리
• 작업에 필요한 파일 선택
• 긴 대화의 정보 정리

Organize the instructions, conversation history, and tool information an agent receives. Use the skill to clarify business rules, structure SKILL.md files for loading when needed, and distinguish external source material from instructions to reduce prompt injection risks.

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• Reorganizing an agent's system instructions
• Reviewing SKILL.md structure and activation conditions
• Turning vague business rules into explicit decisions

Give an agent rules for consulting long-term memory before work and saving decisions, corrections, and failures afterwards. It helps carry preferences and project conventions across sessions while preserving dated history and making conflicting memories visible.

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• Carry preferences and project conventions across sessions
• Record user corrections for future tasks
• Preserve the history of changed decisions

Design systems where multiple agents work with separate task information. Choose coordination structures, define handoffs and shared state, and plan parallel work while checking whether communication costs and failure risks justify using more than one agent.

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• Split large tasks across separate agent contexts
• Run independent research tasks in parallel
• Define handoffs between specialized agents

Guide multi-source research through source checks, comparison of key claims, and recovery from failed tool calls. Produce reports that distinguish retrieved evidence from unresolved questions and clearly document remaining gaps.

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• Checking key claims against multiple sources
• Recovering from failed source retrieval
• Documenting evidence and gaps in research reports

Turn ePub books into training examples for models that reproduce an author's writing style. Covers text extraction, coherent segmentation, varied instructions, model training, and checks for copied phrases and style transfer to new scenarios.

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• Build training datasets from ePub books
• Train a model on an author's writing style
• Check for copied phrases in generated prose

Organize content, contacts, knowledge, and goals in structured files. Use recorded writing preferences and past posts to draft content, prepare meeting briefs from relationship history, and review weekly progress.

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• Drafting posts in a consistent personal voice
• Preparing meeting briefs from contact history
• Reviewing weekly progress and goals

Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.

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• Investigating repeated agent failures
• Reviewing incorrect tool choices
• Refining prompts through repeated evaluation

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

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• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

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• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Plan how an AI agent uses limited input space through reusable prompt prefixes, shorter tool outputs, summaries, and task splitting. Measure token use, response quality, and latency to choose optimizations that provide a net benefit.

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• Reducing token use from lengthy tool outputs
• Improving prompt cache reuse
• Planning context budgets for long conversations

Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

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• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs

Design tools that agents can select and call correctly. Clarify descriptions, input rules, response formats, and recovery messages, then review overlapping tools and naming conventions using examples of successful and failed calls.

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• Writing descriptions and input rules for new tools
• Investigating incorrect tool selection and calls
• Improving error messages with recovery steps

Use a structured starting point to write new context engineering skills. Organize activation conditions, responsibilities, practical steps, examples, and common mistakes, while keeping detailed references separate and clarifying boundaries with related skills.

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• Draft a new SKILL.md
• Clarify when a skill should activate
• Define boundaries with related skills

Design how an AI agent edits its own working conversation history, choosing what to retain, rewrite, or remove. Covers safeguards, capacity monitoring, recovery after overflow, and the computing cost of changing earlier context.

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• Designing agents that edit their own conversation history
• Preserving exact values during long tasks
• Recovering from context overflow

Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.

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• Designing agent self-improvement from failure records
• Defining acceptance checks for self-modifying workflows
• Diagnosing score gaming and stalled improvement

Design file-based storage for agent context, including large tool results, plans, logs, and handoff notes. Keep information available outside the conversation and retrieve relevant sections when needed, instead of loading everything at once.

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• Saving large tool results for selective reading
• Keeping plans available during long tasks
• Sharing findings between agents through files

Design persistent agent memory that retains knowledge across sessions and retrieves relevant facts when needed. Compare storage approaches, track people and changing information, and plan memory consolidation and evaluation based on the kinds of questions the agent must answer.

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• Retain user preferences across conversations
• Track people and entities consistently across sessions
• Retrieve facts valid at a specific point in time