“context”的搜索结果

How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).

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通过反复搜索与评估，为承担部分任务的子代理收集相关代码和背景信息。根据发现的术语及信息缺口调整后续搜索，筛选出与修复或功能开发相关的文件。

• 寻找修复任务所需文件
• 新增功能前收集现有实现
• 按项目术语调整搜索

汇总保存当前Git状态、已作出的决定和剩余工作，将进展与判断依据留给下一次会话，便于中断后了解工作现状并继续推进。

• 中断工作前保存进展
• 向下次会话交接决定
• 记录剩余工作

管理AI上下文的RTK压缩设置与过滤器，用实际提示词预览压缩结果，验证并安装过滤器文件，检查加载诊断信息，也可读取已保留且经过脱敏的原始输出。

• 用实际提示词预览压缩效果
• 验证并安装RTK过滤器
• 检查过滤器加载诊断信息

Review code that manages the conversation history and extra information sent to Codex models. Apply rules for incremental history, size limits, cache stability, manual review of large additions, and required implementation structures.

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• Review changes to conversation history handling
• Check size limits on information sent to the model
• Identify large additions requiring manual review

Helps agents manage long tasks by checking for forgotten instructions and drifting conventions. It guides progress summaries, rule checks after context compaction, and handoff notes when work should continue in a fresh session.

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• Keeping rules consistent during long agent sessions
• Rechecking decisions after context compaction
• Preparing handoff notes for a fresh session

Create and maintain project documents in conductor/ covering the product, technology stack, development workflow, and work units. Extract context from existing code, check document consistency before implementation, and update documentation as the project evolves.

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• Set up context documents for a new project
• Document an existing codebase for onboarding
• Check project documents before implementation

Integrate external services by finding supported APIs and SDKs, the libraries used to call them. Use context-matic to check authentication, data definitions, and available methods, then guide implementation and record completed integration milestones.

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• Adding an external service’s API to an application
• Setting up an API client or SDK
• Checking authentication and request methods

Guide changes to SGLang runtime configuration and shared process state. Identify where resolved settings, runtime flags, resources, and per-forward state belong, and use the documented access patterns and checks when developing or testing.

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• Review how server settings are read and updated
• Choose where shared and per-forward state should live
• Check configuration publication order in worker processes

Analyze large logs, test results, API responses, and files outside the conversation, then return focused findings. It guides agents to save, process, index, and search outputs while limiting how much raw data enters their working context.

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• Extracting errors from large logs
• Summarizing test failures and build warnings
• Inspecting JSON and CSV data

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

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

Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.

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• Set up Wren and connect a database
• Load HubSpot or Stripe data through dlt
• Generate model definitions and add business metrics

Preserve essential information during long agent tasks by extracting URLs, IDs, relevant fields, and conclusions into working notes. It also specifies where to record task handoff details before older tool results leave the agent’s context.

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• Keep source URLs and key excerpts during research
• Preserve API fields and IDs needed later
• Record analysis conclusions before context pruning

Check the `.planning/` directory for missing files, invalid settings, inconsistent state, and orphaned plans. Report issues to address, optionally request repairs, or check context usage—the share of the session’s text capacity already used.

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• Checking planning files for missing content or inconsistencies
• Finding orphaned plans
• Requesting repairs after a planning directory check

Guides the design and review of agent startup context, including role files and links to authoritative records. It helps identify stale guidance, missing updates, overloaded startup files, and instructions that lose the purpose of the work.

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• Designing startup files for a new agent
• Reviewing stale guidance used by running agents
• Separating role information from reusable procedures

Analyze Mixpanel data with Python to explore usage trends, conversion steps, returning users, and differences between user groups. Also manage business context documents that explain an organization's structure and goals to AI assistants.

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• Find where users drop out before purchasing
• Compare retention across user groups
• Explore feature usage and revenue trends

Turn scattered notes and discussions into proposals, requirements documents, or decision records. The workflow gathers essential context, builds an outline, drafts sections with feedback, and checks whether readers without prior knowledge can understand the result.

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• Turning discussion notes into a team document
• Writing product requirements or technical proposals
• Recording decisions and tradeoffs

Create a handoff document that captures progress, decisions, failed approaches, and relevant files. Give a fresh agent the context needed to verify the current state and continue work across sessions.

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• Passing ongoing work to a fresh agent
• Saving context before a session ends
• Preparing for conversation context limits

Connect paid advertising accounts in NotFair and establish reusable brand and campaign context. Prepare a new workspace for agent-assisted advertising work, or refresh existing context across Google, Meta, X, LinkedIn, Reddit, and TikTok Ads.

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• Setting up paid advertising in a new workspace
• Connecting advertising accounts across supported platforms
• Refreshing brand context used for advertising work

Install Acontext, Login & Init Acontext Project, Add Skill Memory to Agent.

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Restore saved project context, architectural decisions, and work state. Use when resuming interrupted work, retrieving prior context, or merging context across sessions.

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Expert C4 Context-level documentation specialist. Creates high-level system context diagrams, documents personas, user journeys, system features, and external dependencies. Synthesizes container and component documentation with system documentation to create comprehensive context-level architecture. Use when creating the highest-level C4 system context documentation.

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Generate or update the project's authoritative repository context at process/context/all-context.md. Use when repo context is missing, stale, or contradicted by code.

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