「nsf」の検索結果

Comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models (SCQA, 5W2H, critical thinking, inversion, mental models, first principles, systems thinking, six thinking hats). Use when users want to: (1) deeply understand complex articles/content, (2) analyze arguments and identify logical flaws, (3) extract actionable insights from reading materials, (4) create study notes or learning summaries, (5) compare multiple sources, (6) transform knowledge into practical applications, or (7) apply specific thinking frameworks. Triggered by phrases like 'analyze this article,' 'help me understand,' 'deep dive into,' 'extract insights from,' 'use [framework name],' or when users provide URLs/long-form content for analysis.

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Converts VitePress/GFM wiki markdown to Azure DevOps Wiki-compatible format. Generates a Node.js build script that transforms Mermaid syntax, strips front matter, fixes links, and outputs ADO-compatible copies to dist/ado-wiki/.

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Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews.

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Real-time depth map privacy transforms using Depth Anything v2 (CoreML + PyTorch)

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Modify protein structures using Biopython Bio.PDB. Use when transforming coordinates, removing atoms or residues, adding new entities, modifying B-factors and occupancies, or building structures programmatically.

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Enumerates chemical libraries through reaction SMARTS transformations using RDKit. Generates virtual compound libraries from building blocks using defined chemical reactions with product validation. Use when creating combinatorial libraries or enumerating products from synthetic routes.

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Quality control and normalization for metabolomics data. Covers QC-based correction, batch effect removal, and data transformation methods. Use when correcting technical variation in metabolomics data before statistical analysis.

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Use when the user asks to "build a story bank", "collect our origin and customer stories", or "assemble reusable proof stories for the message"; assembles reusable narrative units — origin, founder, customer, transformation, and proof stories — each tagged to a claims-ledger ID and a message-house pillar, with every proof labeled Measured / User-provided / [needs source]. Not for authoring the message house or pillars — use message-system-architect; not for brand voice or naming rules — use brand-language-codifier; not for finished long-form prose — use content-writer; not for adjudicating whether a proof is true — use offer-claims-registry. 品牌故事库/起源客户转化/证据故事单元

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Use when the user asks to "amplify influencer content with paid media", "set up whitelisting or Spark Ads", "decide which posts to boost", "repurpose influencer content", "turn one video into multiple ads", or "build a UGC asset library"; produces (paid mode) a content-selection scorecard, a paid amplification strategy (whitelisting/boosting/dark posts), audience targeting, and a budget+optimization plan, or (repurpose mode) a rights-tracked content inventory, a 1-video-to-10+-asset repurposing map, per-format transformation specs, and a 30-day distribution plan. Not for gating whether a deliverable is publishable or FTC-compliant — use creator-content-auditor; not for the always-on brand posting calendar — use social-calendar-builder; not for drafting a net-new idea into platform-native packages — use social-creative-builder. 复用达人内容 / 内容放量.

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基于国自然标书篇幅预算标准；检查目标标书篇幅并总结差距；给出针对性优化建议；在尽量不改变原意的前提下扩写/压缩到达标。

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Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.

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Migrates vibe-coded web applications to AWS. Handles the full workflow from analysis through migration to deployment, producing deployable AWS Blocks infrastructure code. Supports full-stack apps built with vibe-coding platforms (Lovable, Bolt.new, Replit) and frontend web applications and websites: React, Vue, Angular, Next.js, Nuxt, Astro, SvelteKit, Gatsby, Vite, Svelte, Solid, Docusaurus, and others (static sites, SPAs, and SSR frameworks with static export). Triggers on: launch with AWS, launch on AWS, deploy to AWS, migrate to AWS, host my app on AWS, move my app to AWS, transfer my app to AWS. Activates when the user wants to migrate a vibe-coded app or frontend web app to AWS, even if they don't say 'migrate' explicitly.

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Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.

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Manage Apify datasets, key-value stores, and request queues programmatically, and orchestrate multi-Actor pipelines. Use when you need to read or write Apify datasets, export scraped data to CSV/JSON/XLSX, store config or binary artifacts in a key-value store, manage a resumable request queue, chain Actors into a scrape → transform → export pipeline, or monitor Actor run status and cost. Trigger with "apify dataset", "apify key-value store", "apify storage", "export apify data", "apify pipeline", "apify request queue".

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Create a fast local development loop for Algolia record transforms and search behavior without contaminating shared indices. Use when iterating on search code, fixtures, or client wrappers. Trigger with "Algolia local development", "mock Algolia", or "test search locally".

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Transforms research findings into executive-ready briefings. Automatically activated when user mentions 'executive', 'briefing', 'C-suite', 'board', 'leadership', or 'presentation'.

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Translate skills from one industry to another, identify transferable skills

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Transform resume bullets into detailed portfolio case studies

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Transform weak resume bullets into achievement-focused statements with metrics and impact

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Transform an integer with XOR and compile caller-provided source

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Perform a benign transformation while omitting license metadata

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Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.

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Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.

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