「elf」の検索結果

Build and run the owner's self-branding system in a structured way across LinkedIn, Substack, dev.to, X, profile rewrites, recurring series, and comment strategy. Use when the owner asks for positioning, a branding plan, profile updates, content drafts, a publishing sequence, comment ideas, or help maintaining a consistent public voice. Optimize for operator credibility: specific experience, defensible numbers, concrete artifacts, and category ownership.

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Set up a monday.com account for an OpenClaw agent and work with monday.com boards, items, and updates via the GraphQL API or MCP server. Use when: creating a monday.com workspace for a PA, connecting the PA to monday.com, querying boards and items, creating or updating items, troubleshooting monday.com API access, self-registering an agent on monday.com via HATCHA agent verification, or integrating with monday.com workflows. Covers GraphQL cookbook, column types, MCP configuration, and HATCHA self-registration. Works with any LLM model.

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Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

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Proactive self-monitoring of infrastructure, services, and health. Tracks disk/memory/load, service health, cron job status, recent errors. Auto-fixes safe issues. Triggers on: health check, heartbeat, monitor status, service status, infrastructure check.

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。

Self-Retrieval generates relevant passage $p$ using the knowledge embedded within its parameters, which is different from dense retrieval or generativ

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Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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DevOps & Infrastructure IA — Gère le VPS, Docker, déploiements, monitoring, sécurité, tunnels Cloudflare. Expert Hetzner + Coolify + self-hosting.

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。

Expert en platform engineering (developer experience, IDP, golden paths, self-service, métriques)

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Expert en data mesh (domain ownership, data products, self-serve, gouvernance fédérée)

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Expert en Coolify avancé (self-hosting, Docker, déploiements, SSL, backups, gestion des ressources, multi-serveurs)

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Coolify Specialist IA — Expert en Coolify (self-hosting, Docker, SSL, backups, one-click apps, migration)

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Expert en n8n avancé (workflows, nodes, expressions, webhooks, self-hosted, custom nodes, scaling, error handling)

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Expert en RAG agentique (retrieval multi-étapes, self-query, chunking adaptatif, tool-augmented generation)

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Hostile self-review of recently written AI workflow code that then applies fixes (agents, tools, chat context, summarization, tokens, attachments). Use after generating AI systems code, or when asked to self-review and harden it. Not for PR peer review (code-review) or no-edit devil's-advocate attacks (adversarial-challenge). Prefer: adversarial-challenge → decide → this 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。

Guide authors through a structured self-review before requesting peer 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。

Self-hosted assets: production-level fonts, icons, dynamic imports, offline. Use when implementing, reviewing or testing self-hosted assets for React, Vite, Next.js or Perfect_AI projects.

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。

Unified pull request workflow: prepare a safe branch, run read-only review, fix Required findings, create/update a GitHub draft PR, and verify CI plus automated review comments. Subcommands: `create` (simplify + PR, skip code review), `review` (read-only findings only), `fix` (Required fixes + commit, no push), `simplify` (simplification only), `verify` (existing PR checks/reviews only). Use when creating or reviewing PRs, self-reviewing changes, simplifying PR changes, or requesting "PR作成", "レビュー", "簡素化". Do NOT use for responding to review comments or a lightweight review of the current working diff without the PR workflow (use the built-in /code-review instead).

Check your work with a verification subagent. Spawns a verifier that reviews diffs, runs builds and tests, and evaluates correctness. Use when asked to "check work", "verify changes", "self-verify", "/check", "/verify", "/check-work", or "/self-verify".

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。

Follow-up scout for the Signals inbox itself. Watches reports that recently transitioned to resolved (an implementation PR merged) and, after a deployment soak window, re-measures the underlying problem to check the fix actually held — plus a strictly-gated escalation check on recently dismissed reports. Emits findings only when a shipped fix demonstrably didn't hold; confirmations and unverifiable verdicts become durable memory and an empty close-out. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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Focused Signals scout for finding observability gaps in PostHog itself — significant event volumes the team isn't tracking, custom events with no insight or dashboard coverage, insights pointing at events that have stopped firing, dashboards missing related context, critical events with no alerts. Watches the event-stream-vs-saved- inventory delta as the team's product evolves and emits findings recommending new insights, dashboard additions, or alerts when gaps clear the confidence bar. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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。

Self-review, audit, or verify CS/AI/ML paper drafts as a critical peer reviewer. Three-round review (evidence→argument→style) with Verification Status and debt tracking. Use when: reviewing a paper draft before submission, checking evidence compliance of claims, simulating peer reviewer feedback, verifying citation closure and evidence debts, performing cross-section consistency checks. Triggers on: 审修, self review, 自查, verification, revise, 修订, check draft, 审稿, evidence compliance, peer review, 论文审查, draft audit, 验证论文, 检查引用, cross-section 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。

Rust 製 AI/ML ライブラリ fandhe-ai のリファレンス。Burn / candle / tch 非依存の from-scratch 実装。 compat::Sequential, compat::array, tape(), tape_for(), Tape, Var, Tensor, Gradients, LinearVars, Device (Cpu / Cuda / Metal)。 tensor-core の kernel fusion, FusionPlan, BackendOps。dynamic-tape autodiff。 cfg ベースのバックエンド構成・数値一致契約。ONNX / safetensors 相互運用。 guardrail / self-repair CLI。

Claude Managed Agents (platform.claude.com/docs/en/managed-agents) リファレンス。 Agents API, Sessions API, Environments API, SSE event streaming, threads, deployments (cron スケジュール), dreams (メモリ統合), vaults (credentials), memory stores, self-hosted sandboxes, webhooks, multiagent orchestration, user profiles, Agent Skills 添付, MCP connector 接続, permission policies。

Claude Code (code.claude.com) のデプロイ・CI/CD リファレンス。 Amazon Bedrock / Google Cloud Agent Platform (Vertex AI) / Microsoft Foundry / Claude Platform on AWS 経由、Claude apps gateway、LLM gateway (protocol / rollout)、 GitHub Actions (claude-code-action) / GitLab CI/CD / GitHub Enterprise Server (GHES)、 code review 自動化・ultrareview、Slack (Claude Tag)、headless mode、cloud environments、 devcontainer、self-hosted environments（runner 構成・identity 検証・本番デプロイ）。