'context' 검색 결과

List the local NeatContext Contexts available to the current Kimi Code session. Use when the user asks what contexts exist, what can be connected, or explicitly invokes this skill.

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Preview and permanently delete a NeatContext context while preserving any externally linked knowledge folder. Use only when the user explicitly invokes this skill or clearly asks to delete a context.

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Maintenance for agent-flow context. Runs /doctor, /sync-context, /audit-risk, /repair-docs, /garden, /debt and /audit-lean; re-verifies context claims against the code before refreshing timestamps; turns repeated agent mistakes into mechanical checks. Use on a schedule, after merges flagged [CONTEXT_STALE], or when asked whether AGENTS.md is stale or the risk baseline needs review.

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Restore working context saved earlier by /context-save. Loads the most recent saved state (across all branches by default) so you can pick up where you left off — even across Conductor workspace handoffs. Use when asked to "resume", "restore context", "where was I", or "pick up where I left off". Pair with /context-save. Formerly /checkpoint resume — renamed because Claude Code treats /checkpoint as a native rewind alias in current environments. (gstack)

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Build the smallest, highest-signal context package for an AI coding task — goal, constraints, repo facts, boundaries, and a verification plan. Load when prompts are underspecified, the agent is missing key files or decisions, the user says "use the right context", "here's the repo", or when work is drifting due to missing constraints. Also triggers on "context engineering", "gather context", "what do you need from me", "before you start". Not for cross-session continuity (use memory-startup/memory-recall).

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Analyzes bounded context boundaries in DDD projects. Detects cross-context coupling, shared kernel violations, context mapping issues, and ubiquitous language inconsistencies. Generates context map diagrams and boundary recommendations.

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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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작업 상태와 판단 근거를 로컬 Markdown에 저장해 Claude와 Codex 등에서 인계합니다. 공유 문맥 검색과 기록의 형식 오류 및 불일치 진단도 안내합니다.

• Claude에서 Codex로 작업 인계
• 다음 작업을 위한 문맥 저장
• 이전 판단과 근거 검색

Hermes에서 사용자 취향과 사실을 대화 간에 기억하도록 Honcho 설치·설정·문제 조사를 돕습니다. 기억 조회 방식과 여러 프로필 설정도 다룹니다.

• 클라우드 또는 자체 호스팅 기억 기능 설정
• 기억 누락 및 프로필 동기화 문제 조사
• 여러 프로필에서 사용자 정보 공유

Study Model Context Protocol (MCP), which connects AI applications with external tools, through guided lessons and exercises. Inspect requests and responses, practice implementation and security checks, and record progress alongside observed results.

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• Learn MCP fundamentals through requests and responses
• Practice building MCP clients and servers
• Study authorization, security, and operational failures

Find context from past coding sessions in Hindsight long-term memory. Reuse memories already loaded for the session, search for missing details, and reason over accumulated records when answering questions about decisions, project context, or preferences.

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• Revisit the reasons behind past implementation decisions
• Recover project context discussed in earlier sessions
• Check previously discussed preferences

Use Hindsight to recall past context, save reusable facts, and reason across accumulated memories. Supports continuity between conversations by preserving preferences, project decisions, and lessons learned, while helping explain the reasoning behind earlier choices.

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• Resuming earlier work with relevant context
• Saving lasting preferences and project decisions
• Reviewing the reasons behind past decisions

Retrieve relevant Hindsight memories before planning or answering a request. Searches the agent’s own and shared memory stores, plus other relevant stores when needed, to bring past decisions, user preferences, and project context into the task.

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• Checking past decisions before planning work
• Answering with previously recorded user preferences
• Finding context saved by other Bots or AI tools

Help agents continue work in projects using Superself. Read current goals and decisions, attach work to tracked units, record progress with supporting evidence, and preserve confirmed decisions so later sessions can resume with context.

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• Resume work using context from a previous session
• Track work units and evidence of progress
• Record confirmed decisions and their reasons

Manually sync OpenClaw chat transcripts to OpenViking and search stored memories using explicit commands. Receive a sync summary or relevant memory entries while leaving the OpenClaw contextEngine slot available for its existing use.

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• Manually sync recorded chat transcripts
• Find stored memories about a specific topic
• Check the summary of a manual sync

Generate database relationship diagrams from Entity Framework Core models, mappings, and migrations. Produce D2 diagram source showing tables, keys, relationships, and constraints, with display options for columns and grouping. The d2 CLI can render SVG or PNG images.

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• Document an EF Core project's database structure
• Review primary keys and foreign key relationships
• Visualize owned types and many-to-many join tables

Investigate excessive privileges inside Kubernetes pods by reviewing security settings, runtime events, and audit logs. Use admission policies to restrict risky configurations and Falco monitoring to detect suspicious privilege changes and container activity.

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• Finding pods running as root or in privileged mode
• Reviewing host filesystem access and dangerous Linux permissions
• Detecting privilege changes while containers are running

Write, review, and upgrade Effect v4 code for Composio CLI and its related packages. It guides service definitions, typed errors, command APIs, and version alignment, checking unfamiliar APIs against source files and installed type definitions.

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• Implementing and reviewing Effect v4 code
• Defining services and typed error recovery
• Checking effect/cli APIs

Design workflows in which AI agents handle multiple steps or specialist tasks. Choose a suitable structure, generate starter configurations, and define information handoffs, retries, validation checks, and limits on cost and context size.

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• Choosing between one agent and a multi-agent workflow
• Generating a starter configuration for a multi-step pipeline
• Defining information handoffs and error recovery rules

Track work across sessions with persistent issues, dependencies, and progress notes. Use the bd command-line tool to find unblocked work, claim issues, record completion, and recover task context after conversation compaction.

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• Track feature work across multiple sessions
• Find work that has no unresolved blockers
• Recover task context after conversation compaction

Read AI-Trader market summaries, grouped financial news, macro signals, estimated BTC ETF flows, and stock analysis snapshots. Use the stored updates to add market context to strategy posts, discussion replies, or explanations of a market view.

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• Check market context before posting a strategy
• Read financial news by category
• Review stock analysis and recent snapshots

Organize knowledge notes with links that explain their relationships, search saved information, review conflicting claims, and prepare weekly digests. Retrieve earlier work sessions and conversations by topic or date to recover context before continuing a task.

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• Find saved knowledge on a topic
• Add source material and update related notes
• Review conflicting claims across notes

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 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