「nsf」の検索結果

Dense methodology covering DNS exfiltration (dnscat2, iodine, dns2tcp), HTTPS tunneling (domain fronting, CDN abuse, legitimate service channels), ICMP tunneling (icmpsh, ptunnel-ng), cloud storage dead drops (S3 presigned URLs, Azure Blob SAS tokens, GCS signed URLs), email-based exfil (SMTP, EWS, draft method), steganography (image, audio, document metadata), encoding/encryption (base64 chunking, XOR, AES), covert channels (custom protocol tunneling, HTTP header encoding, timing channels), and data staging (compression, splitting, encryption). Tools: dnscat2, iodine, dns2tcp, PacketWhisper, chisel, stunnel, icmpsh, ptunnel-ng, steghide, zsteg, OpenStego. MITRE ATT&CK: T1048 (Exfiltration Over Alternative Protocol), T1041 (Exfiltration Over C2 Channel), T1567 (Exfiltration Over Web Service), T1029 (Scheduled Transfer), T1030 (Data Transfer Size Limits), T1132 (Data Encoding), T1001 (Data Obfuscation). Use when planning or executing data exfiltration during authorized red team engagements or post-exploitation.

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Build reversible scroll-controlled visual transformations with a pinned or sticky stage, normalized progress, and video, image-sequence, canvas, SVG, or DOM renderers. Use for hero transformations, product assembly, interface state walkthroughs, object rotation, diagrams, or photo sequences that must move forward and backward with native scrolling.

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Create and manage Starknet wallets for AI agents. Transfer tokens, check balances, manage session keys, deploy accounts, and interact with smart contracts using native Account Abstraction.

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Build a production 1:1 stablecoin swap with LI.FI Intents — an interface or backend where the amount the user sends equals the amount they receive (no visible gas or solver fees), same-chain or cross-chain, filled by LI.FI's solver network and verifiable on-chain. USE THIS SKILL WHEN USER WANTS TO: - Build a 1:1 stablecoin swap interface ("send 100 USDC, receive 100 USDC"), same- or cross-chain - Add stablecoin transfers with a guaranteed output amount to an app, backend, or bot - Build enterprise/fintech-grade transfers with no visible gas or solver fees for the end user - Implement the LI.FI Intents quote -> approve -> escrow open -> status loop for stablecoins - Move USDC / USDT 1:1 across chains under an onboarded LI.FI integrator - Understand how the LI.FI integrator ID / Partner Portal enables 1:1 quoting - Use the @lifi/intent TypeScript SDK to quote, build, open, and track an intent order This skill is self-contained and covers the integration end to end via the `@lifi/intent` SDK (the primary path), with the raw `order.li.fi` REST endpoints as a language-agnostic option. For the wider LI.FI product surface (classic swaps/bridges, Composer, Earn), use the `lifi` skill.

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x402 agentic payments (Coinbase's HTTP 402 protocol). Custody-free tools to pay x402-protected endpoints (build the EIP-3009 transferWithAuthorization the payer signs, assemble the X-PAYMENT header), call a facilitator (verify/settle/supported), and monetize your own API by generating PaymentRequirements. USDC on Base. Triggers: x402, HTTP 402, pay per request, agentic payment, machine payment, X-PAYMENT, transferWithAuthorization, EIP-3009, facilitator, monetize API, pay for API, agent pays.

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Hunt fintech-specific GraphQL vulnerabilities: money-movement mutations (transfers, redemptions, withdrawals, card top-ups), ledger/balance/portfolio query IDOR, decimal-precision and rounding abuse, idempotency-key bypass enabling double-spend, KYC/PII field-level authorization gaps, and admin-override mutations reachable via mass assignment. Distinct from hunt-graphql, which owns generic GraphQL discovery and IDOR/mutation methodology — this skill owns the delta introduced when a GraphQL layer sits in front of a ledger, wallet, payments, banking, brokerage, or lending backend, where a resolver bug moves real money instead of just leaking data. Use when hunting a fintech, banking, payments, wallet, neobank, brokerage, or lending target that exposes a GraphQL API, or when a schema/response includes balance, transfer, ledger, redeem, quote, KYC, or account-linking fields.

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Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.

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Deeply analyze any empirical economics PDF using the five-question framework (五问框架): research question, identification strategy, core estimand, robustness logic, and scholarly contribution. Converts the PDF to Markdown, dispatches 5 parallel sub-agents (one per question), then compiles a 2000–3000 Chinese-character report with a transferable methodology checklist. Optimized for empirical economics papers in top journals (AER, QJE, JPE, RFS, RoF, JFE, etc.). Use this skill whenever the user wants to: - systematically analyze an empirical economics paper - apply the 五问框架 (five-question framework) to a paper - extract transferable research design elements from a top-journal paper - understand a paper's identification strategy, estimand, or robustness logic - run /five-questions, /five-q, or /paper-five-q-analysis - "帮我做五问分析", "用五问框架分析这篇论文", "analyze this paper with the five-question framework"

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Get information about the current Selection in the Unity Editor — active object, active transform, selected GameObjects, transforms, instance IDs, and asset GUIDs (each enrichment is opt-in). Pair with 'editor-selection-set' to change the selection.

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Problem-solving strategies for natural transformations in category theory

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This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

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Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

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文案风格迁移：把一段文案从一种风格改写成另一种风格（严肃→搞笑、书面→口语、文艺→直白、正式→社交媒体感）， 支持风格参考（给一段目标风格的示例文本）。 使用时机：用户说"改成搞笑风格"、"换个风格"、"风格迁移"、"改成口语化"、"改成文艺风"、 "像 XX 那样写"、"风格改写"、"换个腔调"。 和 text-polisher 的区别：polisher 提升质量不改风格，style-transfer 改变风格不做质量评估。

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Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

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当用户要求写作、重构、审查或润色 NSFC/科研基金申请书的立项依据、研究意义、国内外现状、科学问题或科学假设时使用。以语义论证和可核验性为核心，支持任意文件名、标题命令和 LaTeX 结构；默认全自动完成诊断、成稿、校正、备份和写入，不向用户发起中途确认或审核问题。

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当用户明确要求"写/改研究基础""研究基础+工作条件+风险应对编排"时使用。为 NSFC 正文"（三）研究基础"写作/重构，并同步编排"工作条件"和"研究风险应对"，用证据链证明项目可行、资源条件对位研究内容、风险预案可执行。

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当用户明确要求“迁移 LaTeX 模板”“把旧项目接入 ChineseResearchLaTeX”“把旧标书/论文/毕业论文/简历套进当前模板”“把 Word/PDF/Markdown/零散 tex 整理进现有项目”，或直接提到 `transfer-old-latex-to-new` 时使用。旧别名 `migrating-latex-templates` 可兼容理解。该 skill 只负责把正文内容迁移到当前仓库现有模板的内容层；绝不能修改 `packages/` 内公共包源码、也绝不能修改 `projects/` 内模板样式或入口骨架，只能写入目标项目允许承载正文的内容文件。

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当用户明确要求“写/生成 NSFC 预算说明书”“写预算说明”“生成 budget.tex / budget.pdf”“写国自然预算 justification”时使用。基于用户标书正文或补充材料，输出一份可提交的预算说明书 LaTeX 项目并渲染 `budget.pdf`。若用户未指定工作目录，必须暂停并先要求其指定。⚠️ 不适用：用户只是想了解预算原则；用户仅要预算表数字而不写说明书；或用户是 2026 青年 A/B/C 默认包干制且无需预算说明书的场景。

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去除 NSFC 标书中的 AI 机器味，覆盖词语、句法、段落和章节层，尤其处理伪对立、工程协议腔、规格书式字段串、术语漂移、边界声明过重和研究动作不清（不适用：非标书内容/需修改格式/需补充新内容）

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当用户明确要求"写/改研究内容""研究内容+创新+年度计划编排"时使用。为 NSFC 正文"（二）研究内容"写作/重构，并同步编排"特色与创新"和"三年年度研究计划"，输出可直接落到 LaTeX 模板的三个 extraTex 文件。

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检查 NSFC 标书正文引用与参考文献的一致性与真实性风险（只读）：核查 bibkey 是否存在、BibTeX 字段与 DOI 等格式问题，并生成结构化输入供宿主 AI 逐条评估“正文表述是否真的在引用该文献”；默认仅输出审核报告，不直接修改标书或 .bib（除非用户明确要求）。

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Transform, clean, reshape, and preprocess data using pandas and numpy. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

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Generate a complete NSFC (National Natural Science Foundation of China) General Program grant application in Chinese, following the 2026 official template. Use when the user provides a scientific hypothesis and wants to generate a full grant application document, including abs...

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Writes Specific Aims pages for grant applications. Use when drafting or revising the Specific Aims page (NIH R01/R21/R03), NSF Project Summary, or equivalent for any major funding agency. Also triggers on "write my specific aims", "help me draft specific aims for NIH", "what should a specific aims page include", "NSF project summary", "write my grant aims", or "how do I structure an R01".

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。