「context-engineering」の検索結果

Template for creating new Agent Skills for context engineering. Use this template when adding new skills to the collection.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.

日本語の概要は準備中です。原文の説明を表示しています。

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used for personal operating-system workflows: content creation, voice consistency, relationship lookup, meeting preparation, weekly review, goal tracking, personal brand management, and network management.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.

日本語の概要は準備中です。原文の説明を表示しています。

Diagnose context stuffing vs. context engineering. Use when an AI workflow feels bloated, brittle, or hard to steer reliably.

日本語の概要は準備中です。原文の説明を表示しています。

Use when designing, reviewing, or debugging how an agent's context window gets filled, pruned, or shared — choosing what loads at boot versus on demand, sizing an install or an always-loaded file, fixing an agent that drifts, repeats itself, or forgets constraints mid-task, planning compaction or summarization, deciding single-agent versus subagents, engineering handoffs between agents, or picking a tool loadout. NOT for rewording a prompt's tone, choosing which model to pin, or debugging business logic — those are adjacent moments this skill does not serve. Historical 2024–2025 snapshot; not normative for present-day frontier models — see the Status section.

日本語の概要は準備中です。原文の説明を表示しています。

Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Dynamic context injection, mode switching (dev/review/research), selective loading, and strategic compaction for token optimization.

日本語の概要は準備中です。原文の説明を表示しています。

Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior

日本語の概要は準備中です。原文の説明を表示しています。

Guide for setup arXiv paper search MCP server using Docker MCP

日本語の概要は準備中です。原文の説明を表示しています。

Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agent evaluation

日本語の概要は準備中です。原文の説明を表示しています。

Display the current state of the FPF knowledge base

日本語の概要は準備中です。原文の説明を表示しています。

Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API documentation.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when need prioritize what changed code in repository human must review.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for setup Codemap CLI for intelligent codebase visualization and navigation

日本語の概要は準備中です。原文の説明を表示しています。

Use after writing tests to assess coverage quality across structural, mutation, requirements, and API/integration dimensions; organized knowledge for choosing and interpreting coverage analyses.

日本語の概要は準備中です。原文の説明を表示しています。

Add missing test coverage for your local code changes by generating new test files (covers uncommitted and untracked changes, or the latest commit if everything is committed). Use when you want write tests for new logic or increase test coverage.

日本語の概要は準備中です。原文の説明を表示しています。

Apply writing rules to any documentation that humans will read. Makes your writing clearer, stronger, and more professional.

日本語の概要は準備中です。原文の説明を表示しています。

Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests actually verify behavior by requiring failure first

日本語の概要は準備中です。原文の説明を表示しています。