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概要と使いどころ
Close a GitHub issue with a synthesis comment as a flowing graph — validate the synthesis, post the closing comment, close, then run a pluggable callback (e.g. memory store) detached. Use when closing an issue should also capture the LEARNING (not just the diff log) and when the post-close work shouldn't block the close ack.
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。
Ranked content search over a text corpus you point it at, using BM25 (via xhluca/bm25s). Corpus-agnostic: cloned repos, project knowledge stores, uploaded files and archives, any local directory. In-memory BM25 index per invocation, with a session-local disk cache for repeat runs against the same corpus. Use for "rank these documents", "search this corpus", "which files are most about X", "find content about Y", or any multi-word concept query against a known body of text where grep would return everything or nothing. Needs a corpus on disk. Not for searching stored memories or prior-session decisions (remembering), not for a named symbol or a file's structure (tree-sitting), and not for a literal string you could grep.
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。
Exploratory data analysis. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Small files get ydata-profiling HTML/JSON reports; large files (over 200MB or 5M rows) get fixed-memory DuckDB/sketch profiling. Also covers near-duplicate row detection, cross-file key overlap ("can these join?"), dataset drift vs a stored baseline, and time-series profiling.
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。
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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。
arkcli helper：为 Claude Code / Codex / OpenCode / OpenClaw / Hermes / ZCode 配置火山方舟 Plan 或 Platform Endpoint 的 model/provider，或给支持的 Agent（含 MCP-only Trae）配置 Harness 工具。真人 TTY 运行 `arkcli helper` 可选择模型和 Harness 能力；`helper mcp` 默认保持整组 Agent Plan MCP 行为，传 `--capability datapro|web-search|agent-memory` 只配单项，传 `--capability cua` 只给目标 Agent 安装云电脑 Skill；连 model/provider 一起配置用 `helper configure`，查状态用 `helper list`，移除用 `helper reset`。Agent Plan / Team 还支持专业数据集、豆包搜索、Agent 记忆、AI Native 应用开发底座、Agent 进化与 routing.v1 强制检索路由；上下文出现 ARKCLI ROUTING ENFORCEMENT / routing.v1，或用户要求在豆包搜索、DataPro、OpenViking、Supabase 之间强制分流时也使用本 Skill：调用这四类受管 Provider 前先提交 Route Plan，再调用唯一授权 Provider；原生 WebSearch/WebFetch/WebExtract 位于路由管理范围之外。Platform 只允许本人创建、Running、已验证为文本输出的 Endpoint，不配置 MCP、Supabase 或强制路由。豆包搜索配置成功后默认关闭目标客户端原生 WebSearch；传 `--keep-native-websearch` 可保留，并可用 `helper reset` 完整恢复 ArkCLI 配置。
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。
arkcli agent：管理 ARK Managed Agents，包括 Agent / Skill / Env / Session / File / Memory Store / Vault / MCP OAuth。控制面优先走 ForTop/OpenTOP，Session 运行时和 Files 走数据面直联。
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 Miller's Law — chunk information into groups of ~4 to work within working memory limits.
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。
Maps topics and keywords from user questions to QwenPaw official documentation paths and common source code entry points, reducing blind searching. Intended for the built-in QA Agent to quickly identify which files to read when answering questions about installation, configuration, skills, MCP, multi-agent, memory, CLI, etc.
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。
Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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 a full operational status report for the Aivena bot. Checks all subsystems: extensions, webserver, Telegram, chat bridge, heartbeat, cron, database, memory, CRM, calendar, task management, jobs/telemetry, and storage. **Triggers — use this skill when:** - User asks for "status", "bot status", "system status", "operational status" - User asks "is everything running?", "how's Aivena doing?" - User says "health check", "diagnostics", "systems check" - User asks "what's the state of the bot?"
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 the user asks anything about Adobe After Effects — to "write an After Effects expression", "make a wiggle / loopOut / inertial bounce", "rig a control with a slider / null", "build a MOGRT / Essential Graphics template", "export AE to Lottie or Media Encoder"; OR to "export from After Effects", "export with transparency / an alpha channel", "fix a black background on export", "choose ProRes vs H.264", "which codec / what bitrate for 1080p / 4K", "stop H.264 disappearing from the Render Queue"; OR when "After Effects is slow / laggy / crashing / froze", "RAM preview won't play in real time", "AE is using all my memory / render takes forever", "how do I speed up After Effects"; OR to "organize my After Effects project", "relink missing footage", "fix broken links after moving files", "set a folder structure / naming conventions", "batch rename layers", or "write an ExtendScript / .jsx script". Covers expressions & rigging, export recipes, performance triage, and project hygiene.
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。
Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher — covers graphdatascience client 2.x, GraphDataScience, gds.graph.project.native, gds.graph.project.cypher, snake_case endpoints, graph catalog operations, stream/stats/mutate/write modes, memory estimation, PageRank, Louvain, WCC, FastRP, KNN, Node Similarity, ML pipelines, and cleanup. Use for Aura Pro, self-managed, local, or offline Neo4j DBMS with the GDS plugin installed. Does NOT cover Aura Graph Analytics GDS Sessions, AuraGraphDataScience, GdsSessions, gds.graph.project.remote, or AuraDB Cypher API projection/session management — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver 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。
Use when the diff calls a model, exposes a tool to one, feeds it fetched or user content, renders or executes its output, stores agent memory, or changes agent settings, MCP configs, tool policies or prompt files - including this platform's own agents
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 in a Unity project that uses Entities (DOTS) or when a task needs thousands of simulated objects - when ECS is worth it, components/bakers/ISystem with IJobEntity and Burst, entity command buffers for structural changes, native memory rules, and testing systems in a test World.
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 dataframe code is slow or runs out of memory, when data outgrows pandas, or when writing pandas 3, Polars or DuckDB code — vectorisation, dtypes, Copy-on-Write, lazy and out-of-core execution, and measuring before and after
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。
Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile a Node.js app, optimize Core Web Vitals, reduce bundle size, or investigate slow response times. Do NOT use for database schema optimization (use db-sculptor), Docker image optimization (use docker), or CDN configuration.
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 the daily virtual trading process. Research markets, make trades, save snapshot, write diary and memory.
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。
What AutoCode says and does when a session opens — first-run welcome, memory load, the standing rules for every session.
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。