「extract」の検索結果

Routes FlashText tasks for keyword extraction, replacement, fuzzy matching, keyword loading, and keyword-inspection 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。

Use Docling to convert, parse, configure, extract, chunk, serve, and maintain document-processing workflows across Python APIs, CLI commands, optional pipelines, and repository edits.

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。

Route Torchreid deep-person-reid workflows for person re-identification training, evaluation, feature extraction, metrics, visualization, and model export.

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 the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic regression, scikit-learn Kaggle-style tabular classifiers, and Twitter JSONL 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。

Use OpenAI CLIP for zero-shot image-text inference, prompt engineering, feature extraction, and responsible evaluation.

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 Adversarial Robustness Toolbox (ART) for estimator wrappers, adversarial attacks, defences, poisoning/privacy/extraction, metrics, and certification 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。

Operate the Agriculture_KnowledgeGraph agricultural Neo4j graph, Django demo, entity labeling, crawler, and relation-extraction 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。

Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".

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。

Synthesize raw recon/test output into engagement state. Reads everything dropped in targets/<active>/ingest/, extracts hosts/assets/creds/paths, merges into state.md/loot.md/Killchain.md, logs it, archives the raw files. Works for pentest, bugbounty, and ctf. Use when asked to "ingest", "synthesize findings", "process recon", or after dropping tool output in the ingest folder.

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。

Recover and review local human-AI conversations from Claude Code, Codex, opencode, Grok Build, and Cursor. Use when the user wants to revisit, search, extract, summarize, or compare work across Agent session stores, time ranges, projects, or named conversations. Follow the user's requested output; question clustering, problem-space mapping, artifact review, drift analysis, and insights are optional lenses.

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。

Vendor termination data return and deletion procedures per GDPR Article 28(3)(g). Covers data extraction formats, deletion certification requirements, transition planning, residual data handling, and post-termination verification.

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。

Assessing privacy risks in large language model outputs including training data memorisation, PII leakage in generated text, prompt injection leading to data extraction, and hallucinated personal data. Covers output filtering, guardrails, and monitoring. Keywords: LLM privacy, output risk, memorisation, PII leakage, prompt injection, hallucinated PII.

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 when configuring Spring Boot as an OAuth2 resource server, validating JWTs from an external auth provider (Keycloak, Auth0, Okta, Cognito), extracting claims, or implementing scope-based authorization.

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。

Reconstruct workflow behind a past coding-agent artifact using local ax sessions/commits/skills/tool traces. Use when asked how X was built.

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。

Provide comprehensive techniques for penetration testing AWS cloud environments. Covers IAM enumeration, privilege escalation, SSRF to metadata endpoint, S3 bucket exploitation, Lambda code extraction, and persistence techniques for red team operations.

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 product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.

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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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 this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', '.dotx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx or .dotx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.

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。

Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting them into logical folders. Turns hours of manual bookkeeping into minutes of automated organization.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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。

Owns project and build-level work for Compose and Compose Multiplatform apps: bootstrapping a new project, adopting the kit in an existing project, adding or extracting a module, convention plugins in build-logic, the version catalog, and CI plus agent hooks. Use when starting a Compose Multiplatform app, adding a module, or editing settings.gradle.kts, build.gradle.kts, build-logic, libs.versions.toml, or packaging. Do NOT use for routing and architecture choices (compose-architecture), feature code (compose-feature), repositories and persistence (compose-data), composables or resources (compose-ui), or commonMain vs platform code (compose-platform).

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。

Turn PRD.md into FEATURES.md: permanent feature IDs, MoSCoW priorities, acceptance criteria and the PRD requirement each feature comes from.

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。

Art direction for FPS, TPS, shooter, battle royale, and extraction-shooter maps and props in Blender with sightline and cover language.

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。

Plans an architecture-driven change to code that already exists: module boundaries, type responsibilities, layering, coupling, and the public surface a revision moves. Produces a buildable change plan that names the types, modules, and methods involved and records the surface delta each change makes. Use when the user wants to plan, scope, or sequence a restructure, extraction, split, consolidation, responsibility shift, or API revision of existing code, including "these responsibilities are wrong, plan the fix". Does not assess an area and stop at findings — use architectural-analysis. Does not specify new behavior — use plan-a-feature. Does not diagnose a bug — use investigate. Does not write the code — use refactor to restructure it or tdd to build it.

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。