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概要と使いどころ
Licensed insurance agent with 10+ years specializing in personal and commercial insurance. Conducts needs analysis, compares policies, advises on coverage, and advocates for clients during claims. Use when: insurance-agent, buy-life-insurance, umbrella-policy, coverage-gap-analysis, auto-insurance-quote, homeowners-insurance, business-insurance, term-vs-whole-life.
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。
Compare health insurance plans, estimate total yearly costs, and choose coverage that fits medical usage, prescriptions, and financial 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。
Validates certificates of insurance against lease and contract requirements. Compares coverage types, limits, endorsements, and expiration dates field-by-field, flags deficiencies, and generates cure notices.
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 and compare car rentals — sedans, SUVs, and luxury vehicles with insurance options, pickup locations, and best rates for self-driving adventures. Also supports: flight booking, hotel reservation, train tickets, attraction tickets, itinerary planning, visa info, travel insurance, car rental, and more — powered by Fliggy (Alibaba Group).
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。
Compare auto insurance options with coverage analysis. TRIGGERS - Use when user needs help with auto-insurance-comparison related tasks.
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。
Design direction advisor — generates 3 differentiated visual directions with live HTML demos for selection. Use when the brief is vague ("make something nice", "pick a style", "explore directions"), when no design system exists, or when the user wants to compare visual approaches before committing. Richer than /form-style: produces actual visual demos, not just recommendations. Trigger words: "design direction", "style exploration", "pick a direction", "what should this look like", "give me options", "explore styles", "design philosophy".
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。
Competitive analysis ending in a clear positioning call — where to play, how to win. Use when asked to "analyze competitors", "competitive landscape", "how do we compare to X", "competitive positioning", "where should we play", "find our white space", or "who else does this".
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。
PR and community reconnaissance — audit current press coverage, social presence, community health, and competitor PR. Use when asked to "audit our PR", "what's our community state", "how do we compare in press", or before planning a launch or community initiative.
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 a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.
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。
Reads and analyzes academic papers (arXiv preprints, conference / journal PDFs, Zotero items) at three configurable depths: quick skim (2 min), standard read (10 min), or deep analysis (30 min). Produces structured digests covering problem, method, key innovation, results, limitations, reproducibility, hidden assumptions, and connections to the user's other work. Use when the user shares an arXiv link, PDF, or paper title and asks to read / summarize / digest / TL;DR / analyze / review / critique / explain / break down a paper, asks about a paper's contributions / methods / results / equations / figures, wants to compare two papers side by side, or needs a reading note for their records.
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。
Runs the description-drift experiment — spawns all Claude Code agents simultaneously to collect self-reported capabilities, then compares them against static frontmatter descriptions to reveal how reliable orchestrator routing based on descriptions actually is. Use when measuring description drift across the agent fleet, re-running the capability collection experiment, analyzing a specific agent's self-reported capabilities, or auditing whether frontmatter descriptions accurately reflect agent behavior.
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。
Compare explicit design/specification contracts with the implementation in a defined change scope. Use to verify signatures, types, schemas, invariants, and other stated contracts without expanding into general code quality review.
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。
Track and analyze US government shutdown liquidity impacts by monitoring TGA (Treasury General Account), bank reserves, EFFR, and SOFR data from FRED API. Use when user wants to (1) analyze current or past government shutdown effects on financial markets, (2) track liquidity conditions during fiscal policy disruptions, (3) assess "stealth tightening" effects, (4) compare shutdown episodes across different monetary policy regimes (QE vs QT), or (5) generate liquidity stress reports with historical context. Recommended usage frequency is weekly on Wednesdays after TGA/reserve data releases.
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。
An AI analyst for buying B2B software. Investigates what vendors claim, checks the evidence, surfaces contradictions and unanswered questions, and produces a shareable Decision Brief with the questions to ask before you buy. Quick Eval by default; Deep Eval on request. Use when asked to evaluate, compare, vet, or research B2B software vendors.
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 when a human needs to review a Deepline Play result and hand feedback, labels, or approval back to the agent for revision, evaluation, comparison, or bounded iteration. Triggers on “put this run in a Sheet,” “review these results,” “read my feedback,” “make this a standing rule,” “never regress on this case,” “compare these runs,” “keep improving,” or “add the revised run to the same spreadsheet.” Skip ordinary Play authoring or execution with no human review loop, generic spreadsheet work, and one-off CSV 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 when the user wants a last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain 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。
Use for Deepline GTM work that searches, enriches, scores, collects signals, or automates a workflow: find companies or people, enrich a CSV, find emails or LinkedIn, compare providers, build a waterfall, create a webhook or cron, or write a Play. For live information work, run a small heterogeneous experiment, exploit the observed winner, and reopen misses. Skip pure copywriting and non-GTM research.
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。
Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.
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。
Grill the user about a requirement, decision, or idea before implementation, then produce an actionable planning report without writing code. Use when the user wants to clarify requirements, stress-test an idea, compare approaches, or plan before coding.
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。
Develops B2B SaaS positioning from your ICP, competitor and win-loss evidence. Maps what buyers compare you to, chooses between leading with a category or a problem, picks a primary and secondary anchor and up to three defensible differentiators, lays out 2 to 4 worked positioning options with a comparison canvas so you can decide, then locks the chosen one and tests it (clarity ladder, guarantee test). Triggers: "positioning", "how do we position", "what category are we in", "how do we differentiate from", "our homepage is confusing", "positioning options". Run before /messaging. Not for taglines or a messaging library (use /messaging).
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 semantic + keyword match score. Compares resume content against job description to calculate a fit score and identify gaps.
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。
Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.
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 whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.
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。
Evaluate alternatives through a weighted multi-criteria decision matrix. Use when the user calls for the *evaluation* of *alternatives*, wants to *compare* things, or asks what the best is from a list of choices.
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。