「ember」の検索結果

Grow and manage a LinkedIn network with the Curviate CLI. Covers `connect` (send with a note, sent, received, accept, decline, cancel), `profile follow`/`unfollow`, `profile relations`, `profile followers` and `profile following`, the de-duplication signal for a connect loop, and the invitation propagation delay. Use when sending or withdrawing connection requests, triaging received invitations, following a member, or listing connections and followers.

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Read and write LinkedIn profiles and company pages with the Curviate CLI, and resolve human search terms into the opaque filter ids the search commands need. Covers `profile` (me, detail, sections, update, subscription, analytics, visitors, SSI), `company` (detail, employees, posts, jobs, followers, page inbox, follow-invite), `search parameters`, retrieval mode (`--mode`/`--max-age`), and the session/account commands (`login`, `config`, `account`). Use when fetching or updating a member or company profile, resolving a filter id, choosing between a stored copy and a live LinkedIn read, or connecting an account.

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we discuss", "remember when", "find previous conversation", "check history", or before starting work to recall prior decisions.

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Chat with a companion that knows your notes folder (a journal, an Obsidian vault, any .md or .txt files) and every earlier chat, using cognee memory with local databases. Use when someone wants to ask questions about their own notes, or to talk with a companion that remembers past conversations. Runs locally; reads the notes folder, writes only to cognee's memory.

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Use when cognee is slow, expensive, or hitting rate limits — speeding up or throttling ingestion (remember/cognify batching, chunk size, LLM and embedding rate limits, per-stage models), cutting read latency, estimating cost before ingesting, running work in the background, or planning how to scale a deployment.

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Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.

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Use when the user wants to install cognee and run their first remember → recall flow with the Python SDK — fresh setup, virtual env, extras selection, or a minimal working example.

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Create a Google Shared Drive and add members with appropriate roles.

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Add persistent graph-based memory via mnemon. Agents recall past context before responding and remember insights after each turn.

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Turns a task the owner keeps doing by hand into a named, reusable skill they can trigger by name or put on a schedule. Watches them do it once or listens to them describe it, works out what varies and what stays fixed, writes the skill with approval checkpoints in the right places, registers it with the router, and shares it with the team. This is how a business gets a workflow nobody shipped for them. Use this whenever the owner describes something repetitive they wish were automatic — including phrasings like "I do this every week," "can you remember how to do this," "make this a thing I can just ask for," "build me an agent," "automate this for me," "turn this into a workflow," or "I want this to happen every Monday." Reach for it when they describe a routine, even without asking for automation.

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Generate an onboarding checklist and first-week plan for a new hire. Use when someone has a start date coming up, building the pre-start task list (accounts, equipment, buddy), scheduling Day 1 and Week 1, or setting 30/60/90-day goals for a new team member.

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Use this skill when the user asks to save, recall, find, or organize notes. Triggers on: 'remember this', 'save this', 'note this', 'what did we discuss about...', 'check the notebook', 'find in carnet'. Also use proactively when discovering findings worth preserving across sessions.

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Create and render Table v9 with the ember adapter. Route reusable createTableHook defaults, and framework setup; use table-state for reactive ownership.

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Read and control Table v9 state in ember. Use for tracked reads, subscriptions, controlled slices, and framework-specific reactive boundaries.

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Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.

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Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads, click conversions, Google Analytics web or app events, or any other event ingestion use case supported by the Data Manager API. Don't use for uploading audience members (use the data-manager-api-audience-ingestion skill).

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How to CONNECT Microsoft Teams (`kortix channels connect --platform teams`, prints a tenant-admin consent URL) and how to answer in Teams as a teammate. Covers the live Adaptive Card stream (`teams step` with --detail/--output/--source, `teams send` to finalize the answer), sending and downloading files (the consent-card upload flow + `teams download`), reading teams/channels/members through connector calls, asking the user, and the tone the bot should use. Load this when the turn is triggered from Teams (the prompt mentions a Teams tenant/conversation, or `$MS_TEAMS_CONVERSATION_ID` is set in the env), or when the user asks how to do anything in Teams.

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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.

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Multi-agent collaborative debate producing visible round-by-round transcripts with real intellectual friction — members are topic-briefed custom agents, run as a 3-round DEBATE or a 1-round QUICK check, to find the best path. USE WHEN council, debate, multiple perspectives, weigh options, deliberate, get different views, what would experts say, pros and cons. NOT FOR pure adversarial attack (use RedTeam).

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How to work as a member of Rowboat Spaces, the team chat where people and agents work together. Load it once per session, before acting on any request that reaches you from Rowboat; it covers your setup, mentions and hand-offs, the tools, and files.

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Automate Docker Hub operations -- manage organizations, repositories, teams, members, and webhooks via the Composio MCP integration.

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Use when users /learn, save, remember, or distill lessons.

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。