「ops」の検索結果

Suede-owned marketing attribution discipline. Use when the user wants to figure out which marketing actually drives conversions and revenue, choose or interpret an attribution model, or reconcile conflicting numbers across tools. Also use when the user mentions 'attribution,' 'attribution model,' 'first-touch vs last-touch,' 'multi-touch,' 'which channel drives revenue,' 'what's my real CAC,' 'my dashboards disagree,' 'Google/Meta says X but GA says Y,' 'MMM,' 'incrementality,' 'geo lift,' 'holdout test,' 'how did you hear about us,' 'self-reported attribution,' 'dark social,' or wants to instrument attribution themselves, 'stitch my bookings to their source,' 'SavvyCal/Calendly attribution,' 'close the identify gap,' 'track conversions on a third-party domain,' 'first-party attribution.' NOT FOR: general analytics instrumentation and reporting (use suede-analytics) or revenue-ops pipeline work (use suede-revops).

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。

This skill should be used when the user needs to consume the Pier Cloud (Lighthouse) API for cloud cost management — including JWT authentication, listing contexts, workspaces, workspace groups, and FinOps data views. Trigger whenever there is a need to integrate, automate, or debug calls to the Pier Cloud platform via Python, Node.js, or cURL.

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 well-structured agent loops with best-practice coaching and cross-model review gates before you run them. Use when the user wants to design, build, or set up an agent loop, iterative agent workflow, self-review loop, LLM-as-judge loop, multi-model council, reviewer/judge gate, or goal-driven looping process. Guides goal refinement, typed verification criteria, reviewer/judge selection, privacy boundaries, termination guards, and observability, then emits a RUN_IN_SESSION.md handoff prompt plus portable loop.yaml, loop.resolved.json, LOOP.md, and run-loop.py.

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。

Investigate a monitoring alert end-to-end. Pulls metrics, logs, traces, and recent code changes to identify root cause. Works with any monitoring MCP.

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。

Create a structured on-call log by pulling alerts from monitoring and writing a summary to your team's wiki. Requires a monitoring MCP (recommended: Datadog) and a wiki MCP (Confluence or Notion).

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 sales and revenue operations skill. Use when building a sales team, doing founder-led sales, hiring first sales reps, navigating enterprise deals, implementing product-led sales, designing sales compensation plans, defining ICP, mapping buyer personas, or optimizing the revenue engine (RevOps). Activates for: sales strategy, rev ops, revenue operations, sales enablement, sales compensation, ICP, ideal customer profile, buyer persona, sales process, deal execution, lead scoring, lead routing, lead lifecycle, MQL, SQL, pipeline management, CRM automation, sales qualification, BANT, MEDDIC, founder sales, enterprise sales, product-led sales, startup sales, SDR, AE, quota, ramp, commission plan.

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。

Prove a deploy landed — the served revision is the commit you meant, its env is present, and the product does its job — instead of trusting the deploy command's exit code. Also catches server drift before a pull or deploy. Use after any deploy, rollout, restart or server-side fix ("задеплой", "выкати", "проверь на проде", "почему не обновилось"). Loaded by devops, l3-support, infra-provisioner.

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。

Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy gate).

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。

Use when optimizing, auditing, or preparing to ship web animations or rendering performance work: frame loops, scroll/viewport reveals, mount/unmount transitions, canvas/WebGL lifecycles, reduced motion, lazy rendering, and deferred off-screen work. Use for jank, per-frame allocations, forced reflows, render loops, animations that do not pause off-screen, content-visibility, requestIdleCallback, analyzing Chrome DevTools performance traces for animation or rendering issues, or choosing between browser-driven CSS/WAAPI, minimal JS, phase, and Motion/GSAP. Use when the user explicitly asks about phase APIs or behavior. Do not use during exploratory animation ideation, prototyping, visual iteration, or trying variants, even when the code already imports phase; wait until the user asks for performance guidance, an audit, production implementation, ship-readiness review, or about phase itself.

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。

Runs production ML and LLM systems. Use when deploying, canarying, rolling back, monitoring drift or feature skew, promoting champions, migrating providers, or handling incidents.

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。

Diagnose HitKeep traffic drops, spikes, source shifts, and suspicious changes. Use when traffic, sessions, visitors, pageviews, events, or conversions changed; when a deploy may have affected measurement or behavior; or when search, referral, direct, campaign, and bot explanations need to be separated.

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。

Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).

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。

Running autonomous loops for nexus-autoloop. Generates script sets from goals, designs operation contracts, audits live loops, and recovers state — runners that complete reliably.

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。

Optimizing FinOps and cloud cost: IaC-based estimation, right-sizing, RI/SP recommendations, anomaly detection, budget alerts, AI/GPU workload economics. Use to forecast or cut cloud spend.

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 Docker containers, images, volumes, networks, and Compose stacks — lifecycle ops, debugging, cleanup, and Dockerfile optimization.

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。

Search TikTok e-commerce store leaderboards via Kalodata and query detailed information for specific stores. Supports viewing high-ranking, high-sales TikTok Shop stores by region, currency, language, and date range, and using shopId to retrieve revenue, sales volume, on-sale product count, self-operated/affiliate/shopping mall channel revenue, and creator collaboration count. Trigger when users mention TikTok store ranking, TikTok shop leaderboard, TikTok top shops, TikTok store ranking, TikTok Shop store ranking, kalodata store search, kalodata store ranking, TikTok store detail, TikTok shop profile, store revenue, store sales, TikTok shop ranking, TikTok shop leaderboard, TikTok top shops, TikTok store ranking, TikTok shop detail, TikTok store detail, kalodata shop search/detail. Even if the user does not explicitly mention "kalodata", trigger this skill whenever their need involves viewing TikTok store leaderboards or detailed data for a specific TikTok store.

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 the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing.

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 viral loops into your product — referral programs, invite systems, and share mechanics that drive exponential growth. Includes K-factor calculations and real examples from Dropbox, Slack, and Notion. By @WeiYipei.

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。

🇺🇸 GitHub Star Sustained Growth Playbook — Monthly 300+ star growth SOP. Content rhythm, community ops, event-driven growth, contributor & ambassador systems. Battle-tested: AFFiNE 0→60K+, Client Project A 0→10K stars. 🇨🇳 GitHub Star 持续增长实操手册 — 月增 300+ Star 的完整 SOP。内容节奏、社区运营、活动驱动增长、Contributor 与 Ambassador 体系。实战验证：AFFiNE 0→60K+、Client Project A 0→10K Star。 🇯🇵 GitHub Star 持続成長プレイブック — 月間300+ Star成長SOP。コンテンツリズム、コミュニティ運営、イベント駆動成長、Contributor＆Ambassadorシステム。実戦検証：AFFiNE 0→60K+、Client Project A 0→10K Star。 🇰🇷 GitHub Star 지속 성장 플레이북 — 월 300+ 스타 성장 SOP. 콘텐츠 리듬, 커뮤니티 운영, 이벤트 기반 성장, Contributor & Ambassador 시스템. 실전 검증: AFFiNE 0→60K+, Client Project A 0→10K 스타. Triggers: "GitHub star growth" | "sustained star growth" | "monthly star growth" | "star growth SOP" | "contributor system" | "ambassador program" | "open source community" | "community growth" | "developer community growth" | "star持续增长" | "Star增长" | "开源社区运营" | "贡献者体系"

React Flow (@xyflow/react) for workflow visualization with custom nodes and edges. Use when building graph visualizations, creating custom workflow nodes, implementing edge labels, or controlling viewport. Triggers on ReactFlow, @xyflow/react, Handle, NodeProps, EdgeProps, useReactFlow, fitView.

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 a full design-thinking engagement (Empathize, Define, Ideate, Prototype, Test) grounded in real user evidence. Use whenever the user wants to understand users deeply and design a solution for them: "run design thinking", "understand our users", "design user interviews", "synthesize these interview notes", "build personas", "write How-Might-We questions", "prototype this concept", "design a usability test", "our users are churning and we don't know why", in any language ("tư duy thiết kế", "nghiên cứu người dùng", "phỏng vấn khách hàng", "デザイン思考", "设计思维"), even when they never say "design thinking". Also use when the user drops raw interview notes, transcripts, or survey exports and wants insights, personas, or product decisions. Not for "is there a market for X" desk questions (market-researcher).

Deploy and operate the vehicle insurance data analysis platform. Use when user asks about local development setup, production deployment, server configuration, build process, service management, or troubleshooting deployment issues. Focuses on the project's actual simple deployment model using start_server.sh, not complex enterprise setups.

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 configure model serving infrastructure — endpoint scaling, batching, GPU allocation. Use when asked to "serve this model", "design an inference endpoint", or "size GPU allocation for serving".

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。

Audit current model deployment topology — serving config, latency profile, version inventory. Use when asked "what models are deployed", "audit our serving setup", or "inventory our model versions".

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。