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Turns a product or startup idea into a ranked list of riskiest assumptions, the cheapest test for each, pass/kill thresholds, and a go/pivot/kill readout, before anyone builds. Use when someone says "validate my idea", "is this worth building", "what's the riskiest assumption", "design an MVP", "fake door", "smoke test", "will people pay", "pre-sell", "customer discovery plan" or "should we build this". Draws on 82 Lenny's Podcast guests incl. Mike Maples Jr., Eric Ries and Jake Knapp.

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Convert documents and text to audio using ElevenLabs text-to-speech. Use this skill when the user wants to create a podcast, narrate a document, read aloud text, generate audio from a file, or convert text to speech.

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。

Remove AI-generated code slop from branches. Use after AI-assisted coding sessions to clean up defensive bloat, unnecessary comments, type casts, and style inconsistencies. Focuses on identifying and removing AI artifacts that degrade code quality.

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Implement event-driven communication using BlueprintEventBus for cross-module coordination. Use this skill when modules need to communicate without tight coupling, broadcasting domain events (task.created, member.added), subscribing to events with proper lifecycle management, and implementing event-driven workflows. Ensures events follow naming conventions ([module].[action]), include Blueprint context, and use takeUntilDestroyed() for automatic cleanup.

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Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.

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。

Batch Whisper transcription of video or audio (WAV, podcasts) with a re-runnable provenance record. Use to transcribe recordings.

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。

Volatility estimation, forecasting, and regime classification using GARCH, EWMA, realized volatility, and volatility cones

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。

Venue- and market-type-agnostic strategy, sizing, and backtesting layer for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers the durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation methodology.

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Daily temperature high/low bracket and threshold contracts on Kalshi — contract structure, forecast→P(YES) map, settlement rules, cross-venue divergences, and weather-specific pitfalls

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Operate the Resend platform from the terminal — send emails (including React Email .tsx templates via --react-email), manage domains, contacts, broadcasts, templates, webhooks, API keys, logs, automations, and events via the `resend` CLI. Use when the user wants to run Resend commands in the shell, scripts, or CI/CD pipelines, or send/preview React Email templates. Always load this skill before running `resend` commands — it contains the non-interactive flag contract and gotchas that prevent silent failures.

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Use when working with the Resend email API — sending transactional emails (single or batch), receiving inbound emails via webhooks, managing email templates, tracking delivery events, managing domains, contacts, broadcasts, webhooks, API keys, automations, events, viewing API request logs, or setting up the Resend SDK. Always use this skill when the user mentions Resend, even for simple tasks like "send an email with Resend" — the skill contains critical gotchas (idempotency keys, webhook verification, template variable syntax) that prevent common production issues.

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。

Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.

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Enables interaction with Google NotebookLM for advanced RAG (Retrieval-Augmented Generation) capabilities via the notebooklm-mcp-cli tool. Use when querying project documentation stored in NotebookLM, managing research notebooks and sources, retrieving AI-synthesized information, generating audio podcasts or reports from notebooks, or performing contextual queries against curated knowledge bases. Triggers on "notebooklm", "nlm", "notebook query", "research notebook", "query documentation in notebooklm".

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Create game-ready fixed-cell pixel-art character spritesheets from text concepts, reference images, or existing character art. Use when Codex needs to design, generate, clean up, validate, and package 2D game characters with native 32x32, 64x64, or 128x128 cells; 4-way or 8-way directions; idle, walk/run, jump, attack, archer, caster, or other animation strips; transparent GIF/WebP previews; and per-direction targeted regeneration.

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 construction budget variances. Compare estimated vs actual costs, identify trends, forecast final costs, and generate variance reports for cost control.

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。

Forecast project outcomes using historical data: cost overruns, schedule delays, risk probabilities. Machine learning models for construction prediction.

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 weather impact on construction schedule. Predict delays and adjust activities based on forecast.

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。

Fetch weather data for construction scheduling. Historical data, forecasts, and risk assessment for outdoor work.

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 budget vs actual cost variances. Identify overruns, forecast final costs, and generate variance reports.

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 organizational capacity for construction projects. Forecast resource needs, identify capacity gaps, and support strategic planning for project pursuit and staffing.

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Analyze weather data impact on construction schedules. Predict weather delays, optimize work scheduling based on forecasts, and calculate weather-related risk factors for project 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。

Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL and PySpark pipelines without managing model endpoints. Also covers document parsing and building custom RAG pipelines (parse → prep_search → index → query).

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 business spreadsheet models (.xlsx, .xlsm, or Google Sheets exported to .xlsx) for structural integrity errors and explains each fix -- budgets, forecasts, pricing models, commission calculators, FP&A packs, ops trackers, not only banking models. A bundled openpyxl script finds hardcoded numbers inside formulas, typed values pasted over formulas, inconsistent formulas across a row or column,

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。

Master skill that transforms ANY content into 8+ formats: slides, infographic, video script, podcast, social posts, email series, flashcards, quiz, one-pager, interactive game.

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。