データ分析、機械学習、LLMアプリ、プロンプト設計（9,155 件）
Add or revise source-level JSDoc for Shift APIs. Use this skill before writing or editing documentation comments for exported classes, methods, constructors, domain data structures, render frames, reactive state, or any API where caller intent, side effects, lifetime, ownership, or nullability are easy to misunderstand.
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。
Pulls financial statement numbers for US public companies straight from SEC EDGAR's free official XBRL APIs (companyfacts, companyconcept, frames, submissions) into a cited table. Every value carries its accession number, form, fiscal period, filed date, XBRL tag and a link to the filing, and the tool handles fiscal-year ends, quarterly vs YTD vs TTM, derived Q4, restatements and units. Use this whenever the user wants 10-K numbers or 10-Q numbers, says "pull financials for", asks for public company revenue, net income, EPS, cash, debt or capex, wants comps or a competitor side-by-side, a credit check, prospect research, or model inputs, or mentions SEC filings or EDGAR, even if they never say XBRL. Free and primary-source, no paid data terminal needed. Do not use it for private companies, stock prices or valuation multiples.
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 30+ LinkedIn posts that attract your target prospects. Creates industry insights, thought leadership, engagement prompts, and comment strategies. Use when building personal brand to attract inbound leads through social selling.
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。
Predicts client renewal likelihood based on health score signals. Analyzes engagement, support, adoption, satisfaction, billing, stakeholder, and usage data to calculate a weighted Health Score (0-100), classify renewal risk, and generate a prioritized action plan with early warning signals and recommended save plays.
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。
Input your best customers and find 100+ companies that match the profile. Uses firmographic data, tech stack, growth signals, and similarity scoring to identify ideal prospects. Use when building target account lists or expanding to new markets.
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。
Construct statistical arguments for MVP/awards. Narrative framing, comparison to past winners, advanced metrics, counter-arguments.
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。
TAM/SAM/SOM calculator with deep market research. Produces comprehensive market-sizing.md with top-down and bottom-up estimates, methodology, data sources, assumptions, sensitivity ranges, growth projections, competitive landscape, and Mermaid visualizations. Use when user needs market size estimates, addressable market analysis, go-to-market sizing, investor-ready market analysis, or business plan market validation.
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。
Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the 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。
Wire a System One model (Jev, or an open reproduction like Von) into a product feature — routing, guardrails, scoring, classification. Use when replacing an LLM call that returns a label rather than prose, when adding a typed decision to an agent loop, or when deciding between the hosted Jev API and a local open model. Covers question design, the eval-set-first workflow, threshold calibration, confidence gates, and the traps measured on real data.
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。
Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.
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。
Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".
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 analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
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。
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
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。
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
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 and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.
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 YOUR data room for a fundraise, acquisition, or bank diligence -- builds the checklist for your deal stage, sweeps your folders for what you already have, produces a gap report, and organizes everything into the structure buyers and investors expect. The sell-side complement to cowork-deal-room.
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。
When the user needs to draft, review, or update a privacy policy for their product, or needs to understand data privacy obligations across jurisdictions.
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。
When a founder needs to qualify inbound leads, define their ICP, build a lead scoring model, set MQL criteria, or route prospects through pipeline stages. Activate when the user mentions lead scoring, ICP, MQL, SQL, lead qualification, inbound leads, or pipeline 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。
When the user wants to prepare a due diligence data room for fundraising, or when an investor has requested additional materials after a pitch. Also activates for "data room", "due diligence", "DD checklist", or "what documents do investors need?".
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。
JSON/data schema validation for construction data exchange: API payloads, file imports, BIM exports. Ensure data structure compliance before processing.
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。
Validate construction data inputs before processing: cost estimates, schedules, BIM data, field reports. Catch errors early with domain-specific rules.
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。
Assess company readiness for construction industry uberization. Analyze data transparency, process automation, and competitive positioning against open data platforms.
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。
Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM performance.
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。
Reusable prompt templates for construction AI tasks: cost estimation, schedule analysis, document processing, BIM queries. Structured prompts for consistent results.
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。