255 件 ・ 関連度順
概要と使いどころ
Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.
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。
Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search
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。
ローカルに保存したSlackの会話を検索し、期間を絞ったメッセージ確認や件数集計を行います。データの鮮度を調べ、必要に応じて同期やスレッド取得も扱います。
OpenClawのモデル通信やツール実行の不具合を、ログと再現操作で切り分けるスキル。保存済みの会話や添付資料も調べ、原因の修正と再検証につなげます。
文書や会話、仕事の情報を内容に合う保存先へ整理し、ローカルファイルや知識ベース、GitHubなどを横断して重複確認、同期、検索を進めるスキル。
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
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。
Import AI-assistant chat exports (ChatGPT, Claude, Perplexity) and agent session transcripts into the brain as one dated page per conversation under conversations/, validate each page against the native conversation parser, extract facts via the native conversation-facts flow, and keep the archive gap-free with a detect-and-backfill loop. Then answer archive questions: "when did I first discuss X", trace how an idea evolved across past conversations, pull a specific thread.
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。
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, ingest, contradictions, knowledge graph, retrieve, mine conversations, distill, weekly digest, cortex digest, context search, prior work, recall, remember, previous sessions, context recovery, what did we do, find session, search history, resume, pick up where we left off, cold start, yesterday's work, last week, the one about. NOT FOR published-content semantic search across blog/newsletter/X/LinkedIn, or one-shot URL/YouTube ingestion via the Arbol harvester pipeline.
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。
Run the Omnigent load test and produce a results file explaining the latencies. Load when the user wants to load-test / stress-test / benchmark Omnigent under concurrency ("load test omnigent", "stress test the server", "how many hosts/sessions/turns can it handle", "load test real agent turns / conversations", "run a load test"). The test makes each simulated user a real omnigent host that creates host-bound sessions and drives real multi-turn conversations with a mocked LLM; it boots its own local stack (dev/loadtest/run.py). Gather inputs, run it, then read the generated summary.md and explain the latency distribution (avg/median/p95/p99, throughput, failures). NOT for single-request latency micro-benchmarks (that is dev/benchmarks/).
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。
X (Twitter) DM automated chat end-to-end Skill: scan DM inbox to identify pending-reply conversations, read message history, generate persona-based replies and send; also supports searching users and starting new conversations. Built-in E2E passcode unlock, DM permission filtering, and rate control. Use when user mentions X auto-reply DMs, Twitter DM automated chat, auto-handle unread DMs, reply to X private messages with persona, X DM outreach campaign, batch send DMs to Twitter users, auto-process pending DM replies, Twitter DM bot, automated Twitter outreach, X direct message automation.
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 Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
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。
Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.
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。
Structured approach to workplace conflicts, performance discussions, and challenging feedback using preparation-delivery-followup framework. Use when preparing for tough conversations, addressing conflicts, giving critical feedback, or navigating sensitive workplace discussions.
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。
Work with Front: list inboxes and conversations, reply to conversations, assign teammates, add tags. Writes need --confirm. Trigger phrases: front, front inbox, shared inbox, support inbox.
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 situating an Accounting, Organizations and Society (AOS) manuscript in the literature — entering one of the journal's own long-running conversations, engaging the interdisciplinary sources (organization theory, sociology, psychology) those conversations draw on, and marking precisely what the paper adds. Positions the argument; it does not build the theory itself (aos-theory-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。
Writes turn-level tests for a LiveKit agent in the user's normal test suite: pytest (Python) or Vitest (Node.js). Use when the user asks to "write tests for my agent", "add a test for this tool", "test the handoff", "pin this bug", "why does my agent test fail", or after building or changing agent behavior that needs regression coverage. Covers the SDK's test session harness, assertions on messages, tool calls and handoffs, LLM judging of a reply against an intent, mocking tools, multi-turn tests, and judging whole conversations with the built-in judges. For interactive poking use debugging-livekit-agents. For grading whole conversations at scale use running-livekit-simulations.
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。
Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search
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。
You MUST invoke this skill before saying "I don't know," guessing, or treating any topic as new, no matter how trivial the question seems. It supplements other memory systems, which only hold partial records. Searching past conversations is the only way to recover what was actually said.
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。
Interact with Microsoft Teams — send and read messages, search conversations, look up people, check calendar, get meeting transcripts, and manage chats. Use this skill whenever the user mentions Teams, MS Teams, Microsoft Teams, wants to send a message, read chat history, search conversations, look up a colleague, check their calendar, find meeting recordings or transcripts, see direct reports or manager, or do anything involving Teams communication. Also trigger when the user asks about scheduling, org chart, people search, or wants to message someone.
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 for producing Tavus AI-human videos and real-time avatar conversations with Tavus Faces/Replicas, PALs/Personas, async Video Generation, CVI conversations, consent-safe likeness workflows, webhooks, backgrounds, localization, and avatar QA.
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。
Prepare for challenging conversations using Prepare-Listen-Empathize-Clarify-Solve framework.
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。
Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.
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。
Run concurrent scripted conversations against a target agent to measure whether it stays on task, responds correctly, and holds up in repeatable test cases.
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。
Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.
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。