Results for “context”

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).

The English summary is being prepared. Showing the original description.

Gather relevant code and background for subagents, agents assigned part of a larger task. Search broadly, assess each file, identify missing information, and refine the search for up to three cycles to assemble context for bug fixes or feature work.

• Finding relevant files for a subagent assigned a bug fix
• Gathering code patterns before implementing a feature
• Refining searches to match terminology used in a project

Save the current Git state, decisions made, and remaining work so a future agent session can resume with context. Use it before pausing work, then pair it with /context-restore when returning.

• Saving progress before pausing work
• Recording decisions for the next session
• Preserving a list of remaining work

Manage how context, the text supplied to an AI, is compressed with RTK filters. Preview compression on real prompts, validate and install filter files, and inspect filter loading diagnostics.

• Preview compression on real prompts
• Validate and install RTK filter files
• Inspect filter loading diagnostics

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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

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

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

The English summary is being prepared. Showing the original description.

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

The English summary is being prepared. Showing the original description.

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.

The English summary is being prepared. Showing the original description.

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.

The English summary is being prepared. Showing the original description.

This skill should be used when the user asks to "dev-context", "プロジェクトコンテキストを生成", "プロジェクトを分析", "generate project context", "analyze project", "コンテキストを更新". プロジェクトの技術スタック・テストフレームワーク・コーディング規約・アーキテクチャを自動分析し、コンパクトなコンテキストファイルを生成する。

The English summary is being prepared. Showing the original description.