341 件 ・ 関連度順
概要と使いどころ
"Design conversational AI chatbots including intent recognition, slot filling, dialogue flow, and response generation. Use this skill when the user needs to build a chatbot, design conversation flows, implement intent classification, or improve chatbot accuracy — even if they say 'build a chatbot', 'our bot doesn't understand users', 'design a FAQ bot', or 'improve our chatbot's responses'.".
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 customer service operations including tiered support (L1/L2/L3), response templates, SLA definitions, escalation procedures, and complaint handling. Use this skill when the user needs to set up a CS team, create service standards, design escalation flows, or improve response quality — even if they say 'our CS is a mess', 'how should we handle complaints', 'set up support tiers', or 'create CS SOPs'.".
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 analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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。
Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing, journal paper improvement, response-to-reviewers drafting, and deciding when additional data, experiments, theory, modeling, or author judgment is required before further revision.
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-level biomaterials covering biocompatibility, metals, ceramics, and polymers for medical implants, surface modification, host response, and regulatory considerations.
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。
When scoring whether an agent response defended against an injection — treated untrusted content as data, refused role-takeover, ignored fake boundaries. Inverted axis; never auto-gates.
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。
Pre-fills a customer's or prospect's security or third-party risk questionnaire from the organisation's own answer sources (prior questionnaires, answer library, policies), returning one row per question with a draft answer marked Reused, Adapted, Missing or Routed, its source, date, scope match and owner, plus the Missing list by owner. Never invents an answer, certification or control and never submits. Use when the user asks to "pre-fill this security questionnaire", "take a first pass at the customer's due diligence form", "answer this vendor assessment from our previous responses", "populate the third-party risk questionnaire" or "which questions can we reuse answers for". Do not use for screening a vendor's own answers, use vendor-risk-screening-brief instead; for RFP or tender answers use rfp-response-drafter. Drafts for human review; never approves, authorises or signs off.
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。
Reads two or more supplier, bid or tender responses against a requirements list and returns a DRAFT comparison pack: a requirement-by-supplier matrix, gaps per supplier, clarification questions and neutral observations, for an evaluation panel. Never scores, weights, ranks or recommends a winner; the award stays with the panel. Use when the user asks to "compare these bids", "matrix the tender responses against our requirements", "collate the RFQ replies", "show me where each supplier falls short" or "draft clarification questions for the bidders". Do not use for scoring or weighting against agreed criteria, use supplier-evaluation-matrix instead, or for writing the requirements, use rfp-requirements-pack instead. Drafts for human review; never approves, authorises or signs off.
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。
Build AI-focused SRE incident response practices for LLM outages, degraded quality, runaway cost events, and safety regressions.
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。
Personal App-in-Skill customer-support desk (help desk) that triages incoming support tickets from email, WhatsApp, web chat, contact forms, and WeChat, drafts KB-grounded replies, and proposes actions, then holds every send behind a human approval queue with a pre-send quality gate (support-qa) that outputs SHIP/FIX/BLOCK, plus SLA and CSAT tracking. Use when the user invokes $kelly-support or /kelly-support, mentions 客服 / 工单 / 支持台, a support desk, help desk, customer service, support tickets, ticket triage, a knowledge base or canned macros, SLA / first-response time, CSAT, escalations, or refund requests, wants incoming tickets triaged into a needs_review → approved → done pipeline, agent-drafted replies grounded in a knowledge base and reviewed before sending, refunds and commitments gated behind human approval, or breaching-SLA tickets surfaced. This is the OPERATOR desk (agent + human), distinct from a visitor-facing chat bubble.
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。
OpenAI API (developers.openai.com) の画像・動画生成リファレンス。 image generation（gpt-image, Images API `images.generate` / `images.edit`）、 image edit（mask, reference images）、image generation tool（Responses API 組み込み）、 vision（画像入力, image tokens, vision fine-tuning）、 video generation（Sora, `sora-2` / `sora-2-pro`, videos エンドポイント）、 extend / edits, characters。
Apply Boyd's OODA loop to navigate fast-moving, uncertain, or competitive situations — Observe what is actually happening, Orient through mental models and context, Decide on the clearest path, Act quickly, then loop again. Load when the situation is changing faster than the current plan, when a competitive response is needed, when the user is stuck in analysis paralysis in a dynamic environment, when shipping under uncertainty, or when deep-thinking diagnoses a fast-moving / competitive frame. Triggers on "what should we do right now", "the situation is changing", "how do we respond to this", "competitive response", "we need to move fast", or "we're stuck deciding". Based on John Boyd's OODA loop — Observe, Orient, Decide, Act — adapted for product and business contexts (OODA Canvas 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。
Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning:
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。
Tests APIs for excessive data exposure where endpoints return more data than the client application needs, relying on the frontend to filter sensitive fields. The tester intercepts API responses and analyzes them for leaked PII, internal identifiers, debug information, or sensitive business data that the UI does not display but the API transmits. This maps to OWASP API3:2023 Broken Object Property Level Authorization. Activates for requests involving API data leakage testing, excessive data exposure, response filtering bypass, or API over-fetching.
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 AWS GuardDuty threat detection findings processing using EventBridge and Lambda to enable real-time incident response, automatic quarantine of compromised resources, and security notification workflows.
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。
Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach. Implements short-term and long-term containment using network segmentation, endpoint isolation, credential revocation, and access control modifications. Activates for requests involving breach containment, lateral movement prevention, network isolation, active threat containment, or live incident response.
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。
Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
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。
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.
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。
Cross-cutting methodology for the CVE-hunting pipeline (ported from find-cve-agent): when a bug is design-vs-real, false-positive avoidance, version checking before reporting, responsible disclosure norms, acceptance-rate expectations per vuln class, vulnerability chaining playbook, known false-positive patterns, maintainer-response patterns, secure-pattern reference (what NOT to flag), and a self-criticism checklist to run before claiming a finding. Use this alongside the cve-hunter/cve-exploiter/cve-validator agents and the /cve-hunt pipeline — load it whenever judging if a finding is real, worth reporting, or already fixed.
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。
リアルタイム画面やデータ配信の遅延、情報の鮮度、処理待ちを個別に測り、イベント発生から画面表示までの経路を改善して、稼働環境で結果を確かめるスキル。
LLMの出力をJSONや正規表現、Pydanticの型定義に合わせて生成するスキル。文章からの情報抽出や分類結果を、後続の処理で扱いやすい形式に整えます。
NVIDIA GPU上で大規模言語モデルの応答生成を高速化し、APIとして提供する設定を支援します。量子化や一括処理、複数GPUへの分散も扱います。
Webサイトの通信をHARファイルに記録し、JSON取得に使われるAPIを読み解いてHTTPクライアントを作るスキル。検索などの処理をブラウザなしで再実行できます。
重みが公開されたLLMの回答拒否に関わる仕組みを分析し、再学習なしで重みを変更する実験を支援。計算環境に合うモデルや手法の選択、変更前後の品質評価まで扱います。