「agent memory」の検索結果

Use when the user is investigating QML / Qt Quick performance — both vague complaints ("the UI feels laggy", "this is slow", "frames are dropping", "the app stutters") and explicit asks to profile, find hotspots, or optimize bindings, signals, or rendering. Runs qmlprofiler on a 2D QML application, parses the .qtd trace, and analyzes hotspots against the source with frame-time, memory, and pixmap-cache summaries. Does NOT cover Qt Quick 3D.

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Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.

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。

Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past sessions", "extract lessons from all sessions", "save learnings", "update CLAUDE.md with what we learned", "capture session insights", "remember this for next time", "extract lessons", "update skills from session", or "what did we learn".

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。

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

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。

Chunked N-D arrays with compression and cloud storage. NumPy-style indexing. Backends: local, S3, GCS, ZIP, memory. Dask/Xarray integration for parallel and labeled computation. For lineage use lamindb; for labeled arrays use xarray.

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。

Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.

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。

Build or revise private, cited Markdown notes from the user's WeChat history in the current agent session. Use for a maintained personal wiki or incremental notes; use greenbubbles-context for a one-off lookup or summary.

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。

Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for perception, and dev-vs-deploy container patterns. Use this skill when containerizing ROS2 workspaces, setting up docker-compose for robot software stacks, debugging DDS communication between containers, configuring NVIDIA Container Toolkit for GPU workloads, forwarding X11/Wayland for rviz2 and GUI tools, or managing USB device passthrough for cameras and serial devices. Trigger whenever the user mentions Docker with ROS2, docker-compose for robots, Dockerfile for colcon workspaces, container networking for DDS, devcontainer for ROS2, multi-stage builds for ROS2, or deploying ROS2 in containers. Also trigger for CI/CD with Docker-based ROS2 builds, CycloneDDS or FastDDS configuration in containers, or shared memory in Docker. Covers Humble, Jazzy, and Rolling distributions across Ubuntu 22.04 and 24.04 base images.

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。

Routes agents using or contributing to CrewAI, including crews, flows, CLI projects, tools, MCP, memory, RAG, LLM providers, observability, files, multimodal inputs, and repo 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。

Rewrite a memory or context file (CLAUDE.md, AGENTS.md, a todo or notes file) into Honey-terse form so it costs fewer input tokens every session, without losing meaning. Backs up the original first. Use when asked to shrink or compress context files, trim CLAUDE.md, or cut per-session input cost. Prose only — never code, config, or data.

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。

Rewrite a memory or context file (CLAUDE.md, AGENTS.md, notes) into Honey-terse form to cut per-session input tokens. Backs up 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。

Think and work like an expert Embedded Systems Engineer. Use when a task calls for Embedded Systems Engineer judgment. Reasons from hardware timing, interrupt latency, memory maps, and deterministic WCET resource bounds through DWT CYCCNT and Saleae logic-analyzer timing proofs, SWD/JTAG (J-Link/OpenOCD) with CFSR/HFSR/BFAR fault decode, FreeRTOS/Zephyr respecting configMAX_SYSCALL_INTERRUPT_PRIORITY, MISRA C:2012 and ISO 26262 ASIL discipline, Nordic PPK2 power profiling, and MCUboot signed OTA while treating priority inversion, Cortex-M7 uncached-DMA D-cache incoherence, CAN bus-off, I2C NACK storms, and brownout-during-flash-write NVS corruption as first-class failure modes.

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。

Think and work like an expert Behavioral Neuroscientist. Use when a task calls for Behavioral Neuroscientist judgment. Reasons from contingency structure, deprivation state, and latent arousal/motivation variables through Morris water maze, contextual/cued fear conditioning, operant schedules on Med Associates rigs, and DeepLabCut/SLEAP/SimBA tracking validated against ethograms, while treating thigmotaxis-as-anxiety, locomotor confounds of memory deficits, peripheral CNO sedation, and trials-as-n pseudoreplication as first-class failure modes.

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。

Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.

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。

Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more 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。

Use when the user wants WorkBuddy or another agent to remember an account's positioning, voice, boundaries, data, trust assets, personal IP story, and content rules across sessions. Trigger phrases: "账号档案", "记住我的账号", "写入记忆", "人设垂直", "信任名片", "brand profile", "account memory", "style guide". Do not use for one-off post drafting unless profile data is missing.

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。

Mandatory pre-code gate and definition-of-done for ANY Odoo change (add field, override method, inherit view/xpath, OWL/JS patch, wizard, cron, controller, report, security, migration, bug fix, refactor, "optimize", "clean code", "stick to base"), Odoo 16-19. Also when the user pastes an Odoo traceback or install/upgrade error (ParseError "cannot be located in parent view", "Field ... does not exist", missing xmlid) or asks where an Odoo model, field, method, view or xmlid is defined. Run BEFORE brainstorming or planning: Odoo design questions are answered by tracing the real source into a Context Brief (file:line), not by open-ended Q&A or memory. Resolves the Odoo runtime and version, loads the matching odoo-<major> pack, and closes with install, test, upgrade, i18n, security and hygiene checks before commit.

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 working with Neo4j command-line tools — neo4j-cli (modern unified CLI — Cypher via Bolt, schema inspection, Aura management, Docker containers, credential management, agent skill catalog install; install with curl -sSfL https://neo4j.sh/install.sh | bash), neo4j-admin (backup, restore, import, memory sizing), cypher-shell (ad-hoc queries, scripting, CI/CD), or neo4j-mcp (quick MCP server install). Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades — use neo4j-migration-skill. Does NOT cover full MCP editor configuration — use neo4j-mcp-skill.

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。

Build/install/launch proof, ANR/jank/memory triage, and stale-build debugging for native Android apps. Use when runtime truth, performance, or crash root cause is in doubt.

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。

Proves iOS build/install/launch truth and triages hangs, crashes, jank, memory kills, and stale builds. Use when simulator, bundle, or runtime performance state is in doubt.

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。

Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

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。

Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.

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。

Design a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.

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。

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

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。