4,253 件 ・ 関連度順
概要と使いどころ
Write an implementation plan that leads with the decisions the user is most likely to change, and buries the mechanical work at the bottom. Use when planning is requested before a build, especially after brainstorming or an interview.
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。
JSON/data schema validation for construction data exchange: API payloads, file imports, BIM exports. Ensure data structure compliance before processing.
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。
Excel/spreadsheet processing for construction: estimates, schedules, tracking logs, quantity takeoffs. Formulas, formatting, 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。
Generate rolling look-ahead schedules for construction. Create 2-week, 3-week, or 6-week look-aheads with constraint analysis and crew coordination.
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 Telegram bots for construction field workers. Real-time reporting, photo uploads, task assignments, progress tracking. Integrate with n8n for automated 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。
Push Excel data back to BIM models. Update parameters, properties, and attributes from structured spreadsheets.
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。
Building energy simulation and analysis for construction. Calculate heating/cooling loads, evaluate envelope performance, optimize HVAC sizing, and ensure energy code compliance.
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。
Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.
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 guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
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。
Analyze spreads, over/unders, prop bets. Historical trends, situational stats, value bet identification. For entertainment/education only.
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 properly tagged UTM links with consistent naming conventions. Maintains a UTM registry file (utm-registry.json) to enforce naming consistency, prevent duplicates, generate short links, and output ready-to-copy links for LinkedIn, email, social, and 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。
Auto-generates weekly KPI reports from multiple data sources including Supabase analytics, CRM data, financial spreadsheets, and email metrics. Produces executive-ready reports with dashboards, trends, highlights, concerns, and action items.
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。
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
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。
Reviews pitch decks and investor presentations. Reads slide content, evaluates narrative flow, problem/solution clarity, market sizing, competitive positioning, financial projections, team credibility, and ask clarity. Generates a scored pitch-review.md with slide-by-slide feedback, overall score, top improvements, investor objection predictions, and comparisons to successful decks. Use when reviewing fundraising materials, investor decks, or pitch presentations.
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。
Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
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 30+ LinkedIn posts that attract your target prospects. Creates industry insights, thought leadership, engagement prompts, and comment strategies. Use when building personal brand to attract inbound leads through social selling.
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。
Simulate a panel of your real prospects and buyers to pressure-test sales and marketing before it goes out — cold emails, pitch decks, landing pages, pricing pages, demo scripts, proposals. Each simulated prospect reacts in character, raises the objection they'd actually raise, and tells you whether they'd reply, book, or ghost. Use to de-risk outbound and messaging without burning real leads.
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 comprehensive operational runbooks for any system or process. Reads codebase, infrastructure config, and deployment scripts to produce structured runbook.md files formatted for on-call engineers. Use when you need operations documentation, incident response guides, deployment procedures, or disaster recovery plans.
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。
Production incident response automation. Reads logs, checks recent deploys, identifies root cause, suggests fixes, drafts incident comms, creates post-mortem templates. Severity classification (SEV1-4), escalation paths, status page updates. Generates incident-report.md with timeline, root cause, impact assessment, remediation steps, and prevention measures.
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 custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
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。
Writes conversion-focused landing page copy - headlines, subheads, value propositions, feature and benefit blocks, social proof, FAQs, and CTA button text - using PAS, AIDA, or StoryBrand. Use when the user wants words for a landing page, sales page, product or pricing page, waitlist page, homepage hero, or ad-to-page flow, including rewriting weak copy, generating headline or CTA variants for A/B tests, or turning a product description into page sections. Use landing-page-optimizer instead for auditing an existing page's performance, and a frontend skill for building the page 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。
Sweep your folders and downloads for tax documents and build a CPA-ready package -- checklist by form type, everything renamed and organized, a missing-document chase list, and flagged items worth asking your accountant about. Organization, not tax advice.
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。
Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".
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。
Merges, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas - mapping mismatched column names to one schema, normalizing keys, resolving conflicting values, tracking which file each row came from, and verifying the row math. Use when the user wants to combine spreadsheets or exports, stack monthly files, consolidate contact or lead lists, VLOOKUP-style join two sheets on an ID or email, or dedupe records across sources, even if they only say "put these together" or "clean up these lists into one".
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。