「nes」の検索結果

Build production RAG pipelines and persistent agent memory using Pinecone as the vector database backend. ALWAYS USE THIS SKILL when the user mentions Pinecone, wants to index documents for semantic search, build a retrieval-augmented generation system, store agent memory across sessions, implement hybrid search, or connect an LLM to a searchable knowledge base — even if they don't say "Pinecone" explicitly. Also use when the user asks about vector databases for RAG, namespace isolation for multi-tenant agents, embedding pipelines, or scaling a knowledge base beyond what local storage can handle. DO NOT use for local-only vector stores (Chroma, FAISS, pgvector) or pure keyword search with no semantic component.

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。

Adopt repository-level harness engineering for coding agents. Use when a user wants to prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.

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 skill for building web-based game engines and games using HTML5, Canvas, WebGL, and JavaScript. Use when asked to create games, build game engines, implement game physics, handle collision detection, set up game loops, manage sprites, add game controls, or work with 2D/3D rendering. Covers techniques for platformers, breakout-style games, maze games, tilemaps, audio, multiplayer via WebRTC, and publishing games.

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。

Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. Load this skill when asked to: tag or classify flows, set business impact, assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app

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。

Design or review infrastructure, deployment, CI/CD, Docker, Kubernetes, health checks, rollback, feature flags, production readiness, and mobile release workflows. Use for DevOps, platform, container, pipeline, or release tasks.

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。

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.

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 tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.

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。

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

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Find, use, create, install, share, update, and migrate agent skills stored in OpenViking (viking://~/skills and viking://agent/skills). Use it when a search result, or the session's <available-skills> list where the harness injects one, names a skill that fits the task; when a task looks like one a stored skill would cover; when the user asks to write, save, install, or share a skill from text, a Git repository, or a local folder; when a skill should work in every harness and on every machine; or when the user wants to move local skills (~/.claude/skills, ~/.agents/skills, ~/.cursor/skills) into OpenViking — "upload my skills", "save this as a skill", "迁移本地 skill", "把 skill 存到 OpenViking", "新建一个 skill". Covers the add_skill tool, running a skill's helper files, and which local skills must stay local.

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。

Scan the portfolio for the highest-leverage AI opportunities and rank where to deploy operating-partner time. Ingests quarterly updates and financials across multiple portfolio companies, identifies quick wins at each, and stacks them into a single ranked action list. Use during quarterly portfolio reviews, annual planning, or when deciding which companies get AI investment first. Triggers on "AI readiness", "AI opportunity scan", "where should we deploy AI", "AI across the portfolio", "AI quick wins", or "which portcos are ready for AI".

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。

Perform relative value analysis on bonds by combining pricing, yield curve context, credit spreads, and scenario stress testing. Use when analyzing bond richness/cheapness, computing spread decomposition, comparing bonds, assessing bond value vs curves, or running rate shock scenarios.

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。

Writing-time discipline for documents agents consume (the five surfaces, specs, tickets, .omc/skills/) — every rule checkable and carrying a why, steps before reference, one meaning in one home, no restating what the environment already says. Mandatory at drydock seed generation and the launch C5 sediment pass; opt-in for any other agent-facing doc edit. The companion of minimal-code-discipline: that one disciplines code, this one disciplines papers.

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Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

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 CloudFox's read-only Describe/List/Get enumeration (all-checks, role-trusts, secrets, endpoints, and permissions commands) to map AWS and Azure attack paths and surface exploitable misconfigurations. Use immediately after obtaining a cloud credential to build situational awareness, find exposed resources and secrets, or map sts:AssumeRole trust relationships for lateral-movement/privilege-escalation planning.

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。

Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).

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。

Designs and implements VLAN-based (802.1Q) network segmentation on managed switches to isolate zones such as corporate, servers, DMZ, guest, and IoT, and to limit lateral movement paths. Use when segmenting an enterprise network into isolated security zones, meeting compliance mandates (PCI-DSS, HIPAA, SOC 2) for network isolation, or reducing blast radius from a security incident.

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。

Design and execute a social engineering penetration test combining OSINT-driven target profiling with phishing, vishing, smishing, and physical pretexting campaigns using tools like GoPhish, the Social Engineer Toolkit (SET), and Evilginx to measure human security resilience. Use when scoping or running an authorized human-attack-surface test and reporting results to identify security awareness training gaps.

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 and execute authorized vishing (voice phishing) pretext calls to assess employee susceptibility to social engineering and evaluate security awareness controls.

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。

Builds real-time incident response dashboards in Splunk, Elastic, or Grafana to provide SOC analysts and leadership with situational awareness during active incidents, tracking affected systems, containment status, IOC spread, and response timeline. Use when IR teams need unified visibility during incident coordination and post-incident reporting.

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 forensic super-timelines with Plaso's log2timeline.py, pinfo.py, psort.py, and psteal.py CLI tools (fusing file-system MACB, registry, EVTX, browser history, prefetch, LNK, and more), then triage and filter the results in Timesketch. Use when reconstructing the full sequence of events on a compromised or forensically imaged host during a DFIR investigation.

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Implement a vulnerability aging dashboard and SLA tracking system that measures time-to-remediation against severity-based deadlines (e.g. 14 days critical, 30 days high, 60 days medium, 90 days low), with automated escalations and compliance metrics reporting. Use when designing SLA policies, building aging/remediation dashboards, or proving compliance with remediation timelines.

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。

Builds a structured vulnerability scanning workflow using tools like Nessus, Qualys, and OpenVAS to discover, prioritize, and track remediation of security vulnerabilities across infrastructure. Use when SOC teams need to establish recurring vulnerability assessment processes, integrate scan results with SIEM alerting, and build remediation tracking dashboards.

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。

Analyzes encryption algorithms, key management, and file encryption routines used by ransomware families to assess decryption feasibility, identify implementation weaknesses, and support recovery efforts. Covers AES, RSA, ChaCha20, and hybrid encryption schemes. Activates for requests involving ransomware cryptanalysis, encryption analysis, key recovery assessment, or ransomware decryption feasibility.

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。