「llm」の検索結果

Drafts a U.S. C-TPAT Security Profile for CBP submission covering physical, personnel, procedural, conveyance, and IT security domains. Use when preparing C-TPAT enrollment, certification, validation, or recertification profiles, or assembling a CBP-ready security narrative. Trigger: C-TPAT, CBP security profile, supply chain security, trusted trader, customs validation.

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。

Drafts a Bring Your Own Device (BYOD) policy for U.S. employers governing personal device access to company systems. Covers MDM enrollment, encryption, remote wipe authority, privacy expectations, data classification, and regulatory overlays (HIPAA, GLBA, SOX, GDPR). Use when creating or updating BYOD policies, mobile device security policies, or personal device programs.

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。

当用户希望对内部 AI 用例、AI 产品功能、LLM 工作流或第三方 AI 供应商进行 AI 治理、法律风险、隐私、合规、采购或供应商风险审查时，使用本技能。当事实或证据缺失时，技能首先提出受理和澄清问题，识别所需的文件和缺失的证据，将用例映射到 AI 治理框架和适用的法律领域，并产出带记分卡、发现、责任方、补救行动和后续问题的初步或最终治理审查。

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。

Holistic evaluation framework for language models measuring accuracy, calibration, robustness, and fairness

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Prepare high-quality datasets for LLM fine-tuning with filtering, deduplication, augmentation, and RLHF data formatting. Activate on: fine-tuning data, training data curation, RLHF dataset, data quality filtering, SFT dataset. NOT for: model training infrastructure (ai-engineer), prompt engineering without fine-tuning (prompt-engineer).

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Expert in developer tool documentation as a product. Activate on: docs site, getting started guide, API reference, developer documentation, Docusaurus, Mintlify, Nextra, ReadMe, migration guide, troubleshooting page, docs information architecture, documentation testing, llms.txt. NOT for: marketing copy (use seo-content-blogging), API design itself (use api-architect), general technical writing (use technical-writer).

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Build daemon/background processes that start on boot, run continuously, and manage their own lifecycle. Covers macOS launchd (plist files, agents vs daemons), Linux systemd (unit files), Windows services, process supervision, logging, health checks, graceful shutdown, auto-restart, and AI-powered daemons that manage LLM API connections and rate limits. Activate on: "daemon", "background process", "launchd", "systemd", "service file", "plist", "launch agent", "launch daemon", "auto-start", "always running", "process supervisor", "pm2", "background service", "boot service", "AI daemon", "long-running process". NOT for: container orchestration (use devops-automator), cron jobs that run and exit (use task-scheduler), web server deployment (use backend-architect).

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Expert bot developer specializing in Discord, Telegram, Slack automation with deep knowledge of rate limiting, state machines, event sourcing, moderation systems, and conversational AI integration. Activate on 'Discord bot', 'Telegram bot', 'Slack bot', 'chat automation', 'moderation system'. NOT for web APIs (use backend-architect), general automation scripts (use python-pro), or frontend chat widgets (use frontend-developer).

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Prepare for Anthropic-specific technical interviews covering Constitutional AI, RLHF, interpretability, scaling laws, MCP, agentic systems, and AI safety. Activate on "anthropic interview", "constitutional AI prep", "alignment interview", "safety interview", "RLHF opinion", "interpretability prep", "anthropic technical". NOT for general ML interview prep, system design interviews, coding challenges, or behavioral interview practice.

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Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).

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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.

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Chief Security Officer mode: infrastructure-first audit of secrets, dependency supply chain, CI/CD, LLM/AI security, OWASP Top 10, STRIDE, active verification. Daily/comprehensive, trend tracking.

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Pack an EXTERNAL repository into a single AI-friendly file (markdown/xml/json). Use for third-party library analysis, security audits, or handoff to external LLMs. Do NOT use to pack the current project for inbound context — the host runtime already reads files lazily. Triggers: 'pack this repo', 'snapshot of X', 'export codebase', 'repomix'.

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Use when transforming existing code into agent-consumable surfaces — CLI (npm-publishable), MCP server (stdio/SSE/Streamable HTTP), and a companion skill. Triggers on 'agentize', 'henshin', 'expose as MCP', 'wrap as CLI', 'publish to npm', 'make LLM-accessible', 'turn into agent tool', 'expose feature as tool'. Runs when the user has existing code and wants to ship it to agents. NOT for building new code from scratch (see mk:bootstrap); NOT for reviewing or verifying existing code (see mk: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。

Chief Security Officer mode. Infrastructure-first security audit: secrets archaeology, dependency supply chain, CI/CD pipeline security, LLM/AI security, skill supply chain scanning, plus OWASP Top 10, STRIDE threat modeling, and active verification. Two modes: daily (zero-noise, 8/10 confidence gate) and comprehensive (monthly deep scan, 2/10 bar). Trend tracking across audit runs. Use when: "security audit", "threat model", "pentest review", "OWASP", "CSO 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。

Generate a grounded Human Design BodyGraph and whole-chart reading from exact birth data. Use for Human Design charts, BodyGraph, decisions, profile, channels, talents, work, wealth, relationships, mission, or chart-based follow-up questions. Supports Chinese and English.

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人类图 (Human Design) 全盘解读技能。根据出生日期、准确时间和出生地点生成 BodyGraph，并以用户语言综合解读类型、策略、权威、人生角色、身体、天赋、财富、关系与使命；支持基于真实盘面的连续问答。触发词: 人类图, Human Design, BodyGraph, 人类图排盘, 人类图解读, 天赋, 使命, 职业, 关系, 类型, 策略, 权威, 人生角色, 通道, 闸门.

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Repository loader for the grounded Human Design BodyGraph and whole-chart reading skill. Use for chart calculation, channels, talents, work, relationships, mission, and follow-up consultation.

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 best practices for Cypher query optimization, graph data modeling, indexing, and safe execution of LLM-generated Cypher

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.

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。

Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).

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。

Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

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Reconcile baseline-versioned files that the `create-baseline upgrade` CLI staged for LLM-assisted semantic merge. Use when the CLI prints "Open Claude Code and run /upgrade-project to reconcile". Reads the stage manifest at `.claude/state/upgrade/<ts>/manifest.json`, reasons through each pending file's three-way delta in main context, writes a reconciled LOCAL, then deletes the stage when every file lands. Supports `--dry-run` (preview the reconciled diff without writing) and a structured "needs-user-input" fallback when the conflict cannot be disambiguated automatically.

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。