'context' 검색 결과

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

Draft and refine Linear or Superset tickets using a consistent structure for context, references, and implementation notes. Clarify the desired outcome, record where requests came from, and add code details when enough context is available.

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• Draft a bug report or feature request
• Standardize ticket structure across a team
• Record feedback sources and links

Clarify implementation choices before planning a development phase. The skill reviews existing project context and code, asks about unresolved decisions, and records the answers in a context file for research and planning agents.

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• Clarifying unresolved specifications before planning
• Discussing only decisions that remain open
• Documenting implementation choices for other agents

Preserve the context of long Claude Code sessions across compaction, which shortens conversation history. Create a restore map linking decisions, files, and next steps, then use ranked readings and targeted transcript lookups to rebuild working context.

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• Preparing a restore map before conversation compaction
• Rebuilding working context after /compact
• Preserving decision history across long-running work

Find and load the appropriate skill for an agent working inside OpenRig. This index maps situations such as recovery, handover, debugging, and planning to relevant guidance, with commands that retrieve content matching the installed CLI.

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• Find relevant guidance when an agent first starts
• Locate skills for recovery or seat handover
• Find procedures for debugging or implementation planning

Collects and summarizes background research for a product feature, including customer needs, competitors, regulations, and internal decisions. Produces a source-linked background.md and updates shared context documents when findings are useful across features.

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• Gathering background research for a new feature
• Organizing customer requests and pain points
• Reviewing competitor approaches and relevant regulations

Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.

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• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

Understand how OpenRig organizes a team of coding agents. This skill explains the main terms, shows how to identify an agent’s role and peers, and clarifies where skills and working context come from.

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• Getting oriented in a new OpenRig environment
• Understanding rig, pod, and seat terminology
• Checking where skills and context come from

Build a documented understanding of unfamiliar code before a security audit. It records function assumptions, guarantees, dependencies, and open questions, producing a dossier and individual function notes to support later vulnerability analysis.

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• Understanding unfamiliar code before an audit
• Mapping dependencies for threat modeling
• Preparing background notes for an architecture review

Save and manage product references and design preferences for future Product Design work. Keep product URLs, Figma files, screenshots, brand assets, and other context available, and review or update what has already been saved.

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• Set up Product Design with initial references
• Save Figma files and product URLs for future work
• Keep screenshots and brand references available

Plan how Squad agents separate current tasks, summarized history, and lasting reference knowledge so each launch receives relevant context. The skill provides loading rules and prompt templates; the supporting runtime is still a design proposal.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan which context agents load at launch
• Separate session history from lasting knowledge
• Design context templates for interrupted work

Detects whether a task concerns software development or research and strategy by examining the request and project files. Workflow skills can use the result to choose agents, output formats, and review criteria suited to the work.

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• Switching between coding and strategy work in one project
• Choosing output formats for research or implementation
• Overriding an incorrect automatic context detection

Audit Facebook and Instagram advertising accounts and set up the business context for their management. Use it to review overall account health, consider what needs fixing, or begin using NotFair Meta.

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• Checking overall Meta Ads account health
• Reviewing what needs fixing in Facebook advertising
• Setting up business context for ad management

Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

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Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", "anti-corruption layer", "context mapping", "domain events", "strategic design", "the code doesnt match the business", or "how do we split this big system". Also trigger when breaking a monolith into services, defining service boundaries, or aligning code structure with business processes. Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.

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Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.

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When the user wants to create or update their app marketing context document. Also use when the user mentions "app context", "marketing brief", "app positioning", or when starting any ASO or app marketing project. This is the foundation skill — all other skills check for this context first.

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Context window monitoring and budget management. Keeps orchestrator at 15-30% context usage while subagents get full 200k tokens. Provides warnings at thresholds, context-aware summarization triggers, and wave-level budget planning.

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Capture and serialize project state, architectural decisions, and working context. Use when preserving knowledge for later sessions, handoffs, or searchable context retrieval.

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Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.

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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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Configures and verifies an expanded, model-aware context window for OpenAI Codex CLI and Desktop. Use when Codex shows ~258K context, you want 500K or 1M tokens, auto-compaction fires too often, or model_context_window/model_auto_compact_token_limit needs repair. Not for changing the selected model or other Codex settings.

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Plan a session's context window like the budget it is — what loads up front, what gets linked instead, what stays fetch-on-demand, and how to keep the stable prefix cache-friendly so repeated turns cost cents instead of dollars. Use when asked my agent keeps blowing its context, plan what to load into the session, why is every turn so expensive, or design the context for this workflow. Produces the load/link/fetch allocation, the cache-aware prefix layout, the per-turn cost shape, and the eviction rules for when the window fills anyway.

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Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.