“context-engine”的搜索结果

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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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

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

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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Use when Odylith context-engine packets, retrieval, anchor resolution, or routing support flows need operation-level work.

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Work with Odylith context-engine packets, retrieval, and routing support flows.

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

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按任务整理提供给AI智能体的规则、规范和相关代码，选择所需文件与实现示例，汇总长对话，并记录决定、验证结果和待办问题以交接到新会话。

• 整理AI辅助开发的规则
• 选择任务所需文件
• 整理长对话中的信息

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

Use OpenViking through OpenClaw to manage long-term memories, search archived conversations, and find imported resources or Agent Skills. Inspect recall traces to understand search results and recover full tool outputs stored outside the conversation.

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• Recall past preferences and decisions
• Find exact commands in archived conversations
• Import and search documents or Agent Skills

Send a founder’s strategic question to the appropriate executive advisor by matching its wording and intent. Questions spanning several roles go to a boardroom discussion, while unclear questions receive an intake to clarify the issue.

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• Finding the right advisor for a business question
• Framing fundraising decisions across multiple roles
• Routing sales or customer retention concerns

Turn a strategic question or intake notes into a one-page brief for discussion. Outline options, assumptions, constraints, success measures, and signals for reconsidering the decision, then save the brief as a Markdown file.

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• Preparing a pricing decision for discussion
• Comparing market-entry options
• Defining success and exit criteria before a decision

Load company background for executive advisor sessions, check whether it is outdated or incomplete, and propose updates learned during conversations. Apply rules for excluding sensitive details and anonymizing permitted information before external searches or API calls.

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• Loading company background before an executive advisor session
• Checking outdated or incomplete company information
• Proposing context updates from new conversation details

Route business questions to relevant executive advisor roles, coordinate discussions across multiple functions, and combine their findings into clear agreements, disagreements, and action items. Record decisions and flag approved decisions that are due for review.

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• Comparing fundraising with spending cuts
• Reviewing decisions across several business functions
• Clarifying conflicting advisor opinions

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

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

Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.

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• Summarize long coding sessions for continuation
• Preserve file changes and decision history
• Create durable summaries for task handoffs

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

Model what an agent believes, wants, and commits to doing. Connect external facts to goals and plans using structured relationships, with evidence and validity periods that help trace why the agent chose an action.

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• Convert RDF data into agent beliefs
• Trace the reasons behind agent decisions
• Track belief validity and changes over time

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 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

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