4.9万 件 ・ 関連度順
概要と使いどころ
Generates and delivers all outreach for OUTREACH_READY leads. Automated delivery via Chrome DevTools MCP -- email, LinkedIn, Instagram. Gig leads auto-applied on platform. No approval 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。
Reallocate ad budget across campaigns and channels using ROAS and marginal return analysis
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。
Turns client wins into structured case studies in multiple formats. Gathers before/after metrics, structures as Problem-Solution-Results, and outputs landing page copy, one-pager, social snippets, and email insert.
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 hyperpersonalization packet for each lead by chaining company-researcher, cro-auditor, and person-researcher. The packet feeds into message-generator for Tier 3 personalized outreach.
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。
Routes qualified leads to the right team member or sequence based on score, segment, and source. Applies configurable routing rules to assign leads to founder direct, SDR sequences, or nurture campaigns.
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。
Deep technical SEO audit covering Core Web Vitals, crawlability, indexation, structured data, canonical/hreflang, redirect chains, and site architecture. Complements seo-auditor which focuses on on-page/off-page factors.
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。
Revenue forecast from pipeline data with stage-weighted probability and sensitivity analysis
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。
Generates video production plans including shot lists, scripts, B-roll suggestions, music mood, thumbnail concepts, and platform-specific specs for reels, YouTube, and video ads.
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 presentation slides as self-contained HTML files with brand-consistent styling
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 the web for companies showing buying signals matching the agency ICP. Enforces 75/25 geo split and 20 gig + 5 company daily targets.
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 validated JSON-LD structured data markup for any page type
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。
Generates detailed design briefs for social media creative assets. Covers image specs per platform, copy overlay text, color palette, and format specs for stories, carousels, singles, and reel covers.
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。
Find phone numbers for decision makers via Lemlist using Chrome DevTools MCP. Takes LinkedIn profile URLs and returns phone numbers where available.
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 a website for CRO issues and AI automation opportunities, returning actionable findings with outreach hooks
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。
Multi-channel personalized message generation with 3 tiers of personalization
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。
Generates full long-form content: whitepapers, eBooks, research reports, and pillar pages. Takes a content brief and produces 2000-5000 word drafts with sections, data points, and CTAs.
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。
Decision-theoretic framework for agents embedded within the environments they model and act upon
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 and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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 introducing a feature flag system, choosing between release flags / experiment flags / operational flags / kill switches / permission flags, designing percentage rollouts, building kill-switch + circuit-breaker patterns, structuring evaluation contexts (user IDs, plan tier, region), or building flag-cleanup discipline. Triggers: LaunchDarkly / GrowthBook / Unleash / Flagsmith / OpenFeature, percentage ramp, sticky bucketing, kill switch wired to alerting, "we still have flags from 2 years ago", flag debt, OpenFeature provider, evaluation context, default value on provider failure. NOT for A/B test statistical analysis (separate skill), traffic-split routing at the edge, build-time bundler defines, or environment-based config.
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 landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).
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。
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
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。
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
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。
S3, multipart uploads, CDN integration, and presigned URLs for file storage. Activate on: file upload, S3, presigned URL, multipart upload, CDN, blob storage, image upload, media storage. NOT for: data warehouse storage (use lakehouse-architect), database design (use dimensional-modeler).
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 running analytical SQL over Parquet/CSV/JSON without a warehouse, replacing pandas for data wrangling, joining S3 data in-place, building local data marts, or embedding OLAP into an app. Triggers: read_parquet/read_csv setup, partitioned dataset queries, hive partitioning, glob patterns for S3, COPY TO export, attach Postgres/MySQL, UDFs in Python/R, MotherDuck cloud sync, columnar performance vs row stores. NOT for OLTP workloads (concurrent writes), distributed analytics at petabyte scale (use Spark/Trino), or vector search (use pgvector/Lance).
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。