「traction」の検索結果

請求書やフォームなどの規則的なテキストから情報を取り出す処理を、正規表現とLLMで設計し、抽出結果の信頼度が低い項目だけを確認・修正する流れを示します。

• 問題文・選択肢・解答の抽出
• フォームや請求書の抽出処理設計
• 正規表現とLLMを選びたいとき

Extracts structured data from large sets of legal documents into tabular format for review, analysis, and reporting. Processes contracts, agreements, correspondence, filings, and other legal documents in bulk — extracting key terms, dates, parties, obligations, risks, and custom fields into organized tables. Use when conducting due diligence document review, bulk contract extraction, compliance audits across document sets, lease portfolio analysis, employment agreement review, regulatory filing review, or any task requiring structured extraction from multiple documents. Trigger keywords: tabular review, bulk extraction, document review table, data room review, batch document analysis, contract extraction, portfolio review, structured extraction, document comparison table.

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Pick the right LLM for LEGAL INFO EXTRACTION — pulling facts, clauses, dates, parties, obligations, and structured fields out of contracts and legal documents. Vendor-neutral routing grounded in mid-2026 benchmarks (legalbenchmarks.ai Info Extraction; CUAD/MAUD/ACORD). Asks up to 4 quick questions (cost, speed, accuracy/stakes, privacy/jurisdiction/language), then recommends a primary model + fallback + what to avoid + what a human must verify. Use when someone asks "which model should I use to extract clauses/data from these documents", "best AI for contract data extraction", "route this extraction task", or is about to pull structured fields from legal docs without a fixed 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。

Comprehensive patterns for AI-powered document understanding including PDF parsing, OCR, invoice/receipt extraction, table extraction, multimodal RAG with vision models, and structured data output. Use when "document parsing, PDF extraction, OCR, invoice processing, receipt extraction, document understanding, LlamaParse, Unstructured, vision document, table extraction, structured output from PDF, " mentioned.

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。

Claude Codeのセッション終了時に、エラー解決や修正から再利用できる手順を抽出・保存する旧版スキル。現在は非推奨で、既存環境の互換性確認や仕組みの参照向けです。

• 旧版のセッション終了時設定の確認
• 抽出基準や対象分類の調整
• 保存された学習スキルの見直し

Derive new Office files from structural selections without touching the original. Use when the user points at part of a spreadsheet, Word document, PDF, or PowerPoint deck (a worksheet range, paragraph, page, slide, shape, or a pasted selection-ref block) and wants it extracted, converted, or edited — the result is always a NEW file; the source file is never modified. Covers xlsx range extraction to csv/markdown/xlsx, docx paragraph extraction and text replacement, pdf page extraction, pptx slide/shape/table-cell extraction and editing, and targeted xlsx cell edits.

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Analyze the photograph uploaded by the user, deconstruct its visual evidence, abstract and distill the essential relationships, then reconstruct them as a new nonliteral lower-panel composition guided by 19 bundled references. Use for CLEAN upper-photo/lower-abstraction editorial artworks with a number, date, and short phrase in the lower panel. This is abstraction and reconstruction, never style conversion or a miniature redraw. Preserve the exact user-uploaded photograph unchanged above.

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Applies the Bullseye Framework from Traction by Gabriel Weinberg and Justin Mares. Use when choosing growth channels, testing customer acquisition strategies, or deciding where to spend marketing effort. Covers all 19 traction channels with selection methodology, testing protocol, and phase-matched channel advice. Triggers include 'how do we get customers', 'which marketing channel should we use', 'we have a product but no users', 'our growth has stalled', 'should we do content marketing or paid ads', 'how do we test traction channels', 'what channels work at our stage'. NOT for product development (use Lean Startup), not for positioning/messaging (use Obviously Awesome), not for pricing (use Monetizing Innovation), not for enterprise sales methodology (use SPIN Selling).

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Upload video/audio media to BytePlus VOD (Video on Demand) storage, returning the Vid and playback references; supports local file upload (ApplyUploadInfo + TOS + CommitUploadInfo) and URL pull upload (UploadMediaByUrl); also submits frame extraction jobs on ingested media (StartExecution / Operation.Task.Snapshot), including specified time, fixed interval, specified frame, scene-change, sprite image, and output index modes. Trigger keywords: VOD frame extraction, frame extraction, extract frames, video snapshot, thumbnail, screenshot from video, StartExecution Snapshot.

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。

给定 PMID/PMCID 取结构化全文（NLM BioC XML/JSON）。封装 BioC PubMed（标题+摘要）和 BioC PMC OA（开放获取全文）端点，输出按章节切分的段落、表格上下文、RAG 友好 chunk，适合大批量入向量库。Fetches structured fulltext from NLM BioC API for PMID/PMCID, outputs section-segmented paragraphs and RAG-ready chunks. 触发词：全文抽取 / BioC / RAG 入库 / 文献全文 / PMC 开放获取 / structured fulltext / 段落抽取 / 表格抽取 / chunking / fulltext to vector store / paragraph extraction.

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。

Specialized skill for testing file system operations using System.IO.Abstractions. Use when you need to test File, Directory, Path operations, or simulate file system. Covers IFileSystem, MockFileSystem, file read/write testing, directory operation testing, etc. Keywords: file testing, filesystem, file testing, file system testing, IFileSystem, MockFileSystem, System.IO.Abstractions, File.ReadAllText, File.WriteAllText, Directory.CreateDirectory, Path.Combine, mock file system, file abstraction

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。

Detect MITRE ATLAS AML.T0024 attacks (model stealing, inversion, membership inference) performed via inference-API abuse, by monitoring per-principal query volume/distribution, rate-limiting and perturbing outputs, and red-teaming your model's extractability. Use for a public or partner inference API needing cloning/inversion/membership-inference detection, or a pre-deployment red-team exercise to measure extraction risk.

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-agent web research with mandatory URL verification, confidence-tagged output, and four depth modes (quick to deep investigation). USE WHEN research, do research, quick research, extensive research, deep investigation, find information, investigate, extract alpha, analyze content, retrieve content, AI trends, enhance content, extract knowledge, web scraping, YouTube extraction, map landscape, competitive analysis, find it, find this, find this product, identify this, what is this, what's that thing, track down, locate, help me find, I can't find X online, can't find it online, source this — never substitute raw WebSearch/WebFetch for a multi-source find/identify/investigate request. NOT FOR people/company/entity deep background, academic papers (use ArXiv), JSON entity extraction, or content-adaptive wisdom extraction (use ExtractWisdom).

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。

Detects when Luminance, Kira, or a similar bulk-review tool is in use, hands off the high-volume clause extraction to it, and QAs its output per the trust level in `~/.claude/plugins/config/claude-for-legal/corporate-legal/CLAUDE.md`. Use when user says "send to Luminance", "bulk review", "AI extraction", or when diligence-issue-extraction hits a high-volume category.

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 secrets extraction methodology targeting CI/CD environments across all major platforms. Covers environment variable extraction from build contexts, exploitation of vault and secrets-manager misconfigurations (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager), runner and agent token abuse for lateral movement, OIDC federation attacks exploiting trust relationships between CI/CD providers and cloud platforms, build log leakage analysis for inadvertently exposed credentials, cache poisoning techniques for credential exfiltration, platform-specific credential store exploitation (GitHub Actions secrets, GitLab CI variables, Jenkins credential providers), service connection and service account abuse in Azure DevOps and GCP, and Docker registry credential theft from build environments. Maps to MITRE ATT&CK T1552 (Unsecured Credentials) and its sub-techniques. Each section provides enumeration procedures, extraction techniques, and post-exploitation pivoting guidance for using recovered secrets to expand access.

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。

DO NOT USE when the target already consumes an injected interface or built-in abstraction such as IFileSystem or TimeProvider, even if the request says "generate a wrapper"; no new wrapper is needed. Use only when C# source calls an ambient/static dependency and no injectable seam exists: first-time TimeProvider, IHttpClientFactory, or System.IO.Abstractions adoption; minimal Environment/Console/Process wrappers; IProcessRunner; DI registration; or an ambient seam that preserves a static API. Exclude static detection (detect-static-dependencies), migration to an existing/registered abstraction (migrate-static-to-wrapper), one blocked behavior plus deterministic tests (testability-obstacle), and general interface design.

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。

X (Twitter) data extraction and monitoring via Xquik: tweet search, user lookup, follower extraction, giveaway draws, trending topics, account monitoring with webhooks, reply/retweet/quote extraction, community and Space data, follow checks. 22 MCP tools + REST API.

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。

Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".

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。

Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".

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。

DO NOT USE when the target already consumes an injected interface or built-in abstraction such as IFileSystem or TimeProvider, even if the request says "generate a wrapper"; no new wrapper is needed. Use only when C# source calls an ambient/static dependency and no injectable seam exists: first-time TimeProvider, IHttpClientFactory, or System.IO.Abstractions adoption; minimal Environment/Console/Process wrappers; IProcessRunner; DI registration; or an ambient seam that preserves a static API. Exclude static detection (detect-static-dependencies), migration to an existing/registered abstraction (migrate-static-to-wrapper), one blocked behavior plus deterministic tests (testability-obstacle), and general interface design.

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。

Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative

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。

Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.

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。

Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.

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Comprehensive expertise in ERC-4337 account abstraction, smart contract wallets, paymasters, bundlers, and user operation handling. Covers social recovery, session keys, gas sponsorship, and wallet SDKs. Use when "account abstraction, ERC-4337, smart wallet, paymaster, bundler, user operation, gasless transaction, session keys, social recovery, smart account, " mentioned.

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。