「apa」の検索結果

Expert in Japanese honorific language covering 尊敬語・謙譲語・丁寧語 with deep understanding of situational usage, business contexts, and cultural nuances

Expert in Japanese business communication covering keigo, email etiquette, meeting protocols, and corporate culture nuances

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。

Plan team and infrastructure capacity before it becomes a crisis.

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 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.

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。

AI Agent skill marketplace integration for A2A Market. Enables agents to buy skills, sell skills, and earn money autonomously. Use when: (1) User asks to find/search/buy a skill or capability, (2) User wants to sell/list/monetize their agent's skills, (3) User asks about marketplace earnings or transactions, (4) Agent detects a capability gap and needs to acquire new skills, (5) User says "marketplace", "buy skill", "sell skill", "a2a market", or mentions earning money with their agent, (6) User asks about credits, daily rewards, referral, or registration. Supports x402 USDC payments on Base L2 and Credits payment system.

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。

Automate Apaleo tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Context optimization extends the effective capacity of limited context windows through strategic compression, masking, caching, and partitioning. The goal is not to magically increase context windows but to make better use of available capacity.

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Coordinates staged or parallel jobs across live-verified coding-agent runtimes (Claude Code, Codex, Cursor, and other installed CLIs) and in-session toolkit subagents. Routes each job by capability and risk tier, isolates parallel writers in git worktrees, captures redacted output, resumes interrupted runs, and blocks completion until an independent arbiter verifies the results. Use when work should be split across multiple runtimes or subagents, run as staged or parallel jobs, and reviewed before handoff. NOT for a single-agent task or the in-session 7-phase flow (use mk:cook / mk:workflow-orchestrator).

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。

Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.

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Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).

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。

Central catalog of available skills with metadata, capabilities, and performance history. Provides skill discovery and lookup services. Activate on 'skill registry', 'list skills', 'skill catalog', 'available skills', 'skill metadata'. NOT for matching skills to tasks (use dag-semantic-matcher) or ranking (use dag-capability-ranker).

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。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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。

Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

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。

Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peaksets, integrating with paired scRNA-seq, doublet detection (AMULET vs ArchR vs scDblFinder), or running pseudobulk differential accessibility per cluster.

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。

Interprets the postural stimulation test to detect angiotensin II‑unresponsive aldosterone‑producing adenoma (APA) or familial hyperaldosteronism type I (FH‑I) when aldosterone fails to rise with upright posture. Triggered by clinical cues such as “postural stimulation test shows no aldosterone increase”, “suspected APA with non‑responsive aldosterone”, or “evaluating FH‑I in young patients with early‑onset hypertension and hypokalemia”.

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。

Differentiates aldosterone-producing adenoma from idiopathic adrenal hyperplasia based on 18-hydroxycorticosterone levels, with APA patients generally having levels >100 ng/dL at 8:00 a.m. and IAH patients usually having levels <100 ng/dL. Use when reviewing 18-OHB test results to help subtype PA; triggers include 18-OHB >100 ng/dL (suggesting APA) or <100 ng/dL (suggesting IAH).

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。

Automate Apaleo tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Planifie le prochain sprint : calcule la capacité (vélocité glissante 3 sprints), reporte les tickets non livrés du sprint courant, puis complète depuis le backlog par priorité jusqu'à la capacité. Présente le plan, attend validation, puis assigne les tickets au sprint. Usage: /plan-sprint [<titre-sprint-cible>]

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。

Marketing de produto, posicionamento, estratégia GTM e inteligência competitiva. Inclui definição de ICP, metodologia de posicionamento April Dunford, playbooks de lançamento, battlecards competitivas e guias de entrada em mercados internacionais. Use ao desenvolver posicionamento, planejar lançamentos de produtos, criar mensagens, analisar concorrentes, entrar em novos mercados, capacitar vendas ou quando o usuário menciona marketing de produto, posicionamento, GTM, go-to-market, análise competitiva, entrada em mercado ou capacitação de vendas.

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。

Mescle múltiplos modelos ajustados usando mergekit para combinar capacidades sem retreinar. Use ao criar modelos especializados misturando expertise específica de domínio (math + coding + chat), melhorando performance além de modelos únicos, ou experimentando rapidamente variantes de modelos. Cobre SLERP, TIES-Merging, DARE, Task Arithmetic, mesclagem linear e estratégias de deploy em produção.

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。

Discover and install specialized agent skills from the open ecosystem. ALWAYS use this skill when the user asks 'how do I do X', 'find a skill for X', 'is there a skill for X', wants to extend agent capabilities, or needs help with a specific domain (design, testing, deployment, etc.). Make sure to use this skill whenever the user mentions skills, plugins, extensions, tools, or wants to find new capabilities for the agent. Integrates with the Skills CLI and skills.sh directory.

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。

確定 system spec と feature package から実装要件を導出したいとき、readiness 完了時だけ capability-build/task-graph build へ handoff したいときに使う。

VRIO resource analysis combined with Dynamic Capabilities assessment. Use this skill to identify real competitive advantages (Barney VRIO framework), then test whether the organisation can sense new opportunities, seize them, and transform itself to stay ahead (Teece dynamic capabilities). Runs in three modes — full diagnostic for deep first-use sessions, review mode for quick re-assessment, and alert triggers for defining early-warning signals. Updates company-context.md after every run. Triggers on questions about competitive advantage, defensibility, resource assessment, why competitors are gaining ground, organisational agility, or whether advantages will hold. Based on practice by Eterdis (eterdis.com).

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。

Show what this NanoClaw instance can do — installed skills, available tools, and system info. Read-only. Use when the user asks what the bot can do, what's installed, or runs /capabilities.

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。