「extract」の検索結果

Analyze images with AI - extract text, describe content, detect objects

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。

Search the Wayback Machine for archived versions of websites. Extract cached pages, customer lists, testimonials, and partner directories from sites that have changed or gone offline. Uses the free CDX API — no API key needed.

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 scraping with structured data extraction - define your output schema

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。

Process PDFs - extract text, tables, and structured data from documents

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。

Prepare for investor calls by pulling upcoming meetings from Google Calendar, deeply researching each investor and their firm (website scraping, portfolio analysis, thesis extraction), checking for competitor conflicts, and outputting an honest prep sheet with compatibility assessments. Use when asked to prep for investor meetings, fundraising calls, VC meetings, or demo day.

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。

Scan an AgentMail inbox for newsletter signals using configurable keyword campaigns. Extracts matched keywords, context snippets, and company mentions from incoming emails. Use for monitoring accounting industry newsletters for buying signals like acquisitions, migrations, and staffing news.

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-powered web scraping - extract data using natural language prompts

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。

End-to-end lead prospecting from Luma events. Searches Luma for events by topic and location, extracts all attendees/hosts, qualifies them against a qualification prompt, outputs results to a Google Sheet, and sends a Slack alert with top leads. Use this skill whenever someone wants to find qualified leads from events, prospect event attendees, or run an event-based lead gen workflow. Also triggers for "find people at events and qualify them" or "who's attending X events that matches our ICP."

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。

Find ICP-fit leads from KOL audiences on LinkedIn. Given a list of KOLs, scrapes their most relevant high-engagement post from the last 30 days, extracts engagers (reactors + commenters), pre-filters by position, enriches top profiles, and ICP-classifies. Cost-controlled: 1 post per KOL. Use when someone wants to "find leads from KOL audiences" or "scrape engagers from influencer posts" or after running kol-discovery.

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 speaker names, titles, companies, and bios from conference websites. Supports direct HTML scraping and Apify web scraper fallback for JS-heavy sites. Use for pre-event research and outreach targeting.

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 buying intent from job postings. When a company posts a job in your problem area, they've allocated budget and are actively thinking about the problem. This skill finds those companies, qualifies them, extracts personalization context, and outputs everything to a Google Sheet. Does NOT do outreach — just delivers qualified leads with reasoning.

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。

Find leads by scraping engagers from a competitor's top LinkedIn posts. Given one or more company page URLs, scrapes recent posts, ranks by engagement, selects the top N, extracts all reactors and commenters, ICP-classifies, and exports CSV. Use when someone wants to "find leads engaging with competitor content" or "scrape people who interact with [company]'s LinkedIn posts".

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。

Portable visual skill pack for the Agent Skills ecosystem (Claude Code, Claude Desktop, Claude Cowork, Claude Design, Goose, Cursor, Codex). Discovers community-published styles + formats via the gooseworks CLI, runs an extract-style workflow on reference images, and exports rendered PNGs via Playwright.

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。

Generate a personal voice guide for X (Twitter) and/or LinkedIn by scanning a user's past posts and iteratively refining with sample-and-feedback loops. Produces a structured markdown voice guide that sibling skills (create-x-content, create-linkedin-content) consume to draft in-voice posts. Different from brand-voice-extractor, which analyses company blogs/landing pages — this skill is for personal social voice.

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 competitor and customer intelligence from any company's landing page HTML. Discovers tech stack, analytics tools, ad pixels, customer logos, SEO metadata, CTAs, hidden elements, and more. No API keys required.

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。

学习笔记自动化：视频/播客转录 → 知识点提取 → 闪卡生成 → 知识图谱更新。触发词：学习笔记、闪卡、Anki、知识提取、视频学习

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 and normalize pricing tiers from any SaaS, API, cloud, or LLM vendor's pricing page. Use this skill whenever the user says "pricing for X", "how much does X cost", "pricing tiers", "cost comparison", provides a URL ending in `/pricing` or `/plans`, or asks to monitor pricing over time. Pairs well with `exportSkill` to turn a run into a cron-friendly workflow. Scrape-driven; no interact needed for typical pricing pages.

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Navigate e-commerce sites to extract products, pricing, categories, and inventory. Handles pagination, variants, and JS-heavy storefronts.

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。

Patterns for building entity actors with Akka.Hosting - GenericChildPerEntityParent, message extractors, cluster sharding abstraction, akka-reminders, and ITimeProvider. Supports both local testing and clustered production modes.

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Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.

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Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.

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Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.

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Assembles haplotype-resolved diploid and telomere-to-telomere (T2T) genomes from PacBio HiFi reads with hifiasm (HiFi-only, Hi-C, or trio phasing) and verkko (HiFi + ultralong ONT for T2T), extracting contigs from GFA and routing phasing QC to k-mer/trio metrics. Covers why a primary assembly is a haplotype mosaic that exists in no cell, partial-vs-full phasing (the .bp. vs .dip. filename convention), the purge-default trap on inbred samples, the --hom-cov coverage-estimate alarm, and verkko-vs-hifiasm for T2T. Use when assembling a diploid eukaryote from HiFi, phasing haplotypes with parents (trio) or Hi-C, deciding whether to chase T2T, or diagnosing switch errors invisible to N50/BUSCO/QV.

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Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

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。