「deep sea」の検索結果

Earnings-season triage across ALL active theses (not just Live Portfolio holdings) — every earnings call reported inside the window gets a Quartr event summary, a Conviction-Trigger/Outstanding-Questions cross-reference, and a coverage check against existing Research notes, producing a ranked deep-read queue for /transcript. Use when user says "earnings sweep", "who reported", "triage earnings", "catch up on earnings season", or after any earnings-heavy stretch. Default window 14 days; `/earnings-sweep 90` covers a full season.

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Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `$numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

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Conduct deep, multi-source research by cross-referencing data from web search, news, social media, academic sources, and proprietary databases. Enforces APA 7th citations, evidence hierarchy, and 2-cycle verification per research theme. Built for competitive intelligence analyst workflows.

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Multi-source deep research agent. Searches the web, synthesizes findings, and delivers cited reports. No API keys required.

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Research synthesis from sources — "summarize these URLs or papers", "research this topic with these sources", "create a podcast about", "audio overview", "mind map", "quiz from these sources", "flashcards", "deep research on". Wraps Google NotebookLM via notebooklm-py.

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Deep research on a prospect company using WebSearch for outreach personalization

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Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.

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Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

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Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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Write a publication-grade 8-part deep-dive series on a single company (~120k words total): cognitive reset / moat / profit engine / hidden assets / era variable (e.g. AI) / financials Buffett-style / management / valuation+redlines. The core IP is NOT writing but REVISING — a strict fact-check checklist catches pseudo-precision (probability-weighted expectations, third-party MAU discrepancies, linear extrapolation), absolute language, and cross-article number inconsistencies that most finance long-forms violate. Each piece stands alone but shares one valuation/management/price framework. Use when the user wants textbook-level depth on one company for public publishing (a single research report or earnings note is NOT this — use investment-research / earnings-review instead).

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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

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Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cross-referencing, synthesis, and citation formats

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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

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Procedure for answering a question with a thorough, cross-checked, cited web-research report using the Keenable search + fetch tools.

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Deep consistency audit of the entire repository — launches 4 parallel specialist agents to find factual errors, code bugs, broken references, count mismatches, and cross-document inconsistencies, then fixes all issues and loops until clean. Make sure to use this skill whenever the user wants a comprehensive repository-wide check — not a targeted review of a single file. Triggers include: "audit", "deep audit", "find inconsistencies", "check everything", "run a full audit", "are there any broken references", "check the whole repo", "something feels off", "run the audit loop", or after making broad changes across multiple files.

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Deeply analyze any empirical economics PDF using the five-question framework (五问框架): research question, identification strategy, core estimand, robustness logic, and scholarly contribution. Converts the PDF to Markdown, dispatches 5 parallel sub-agents (one per question), then compiles a 2000–3000 Chinese-character report with a transferable methodology checklist. Optimized for empirical economics papers in top journals (AER, QJE, JPE, RFS, RoF, JFE, etc.). Use this skill whenever the user wants to: - systematically analyze an empirical economics paper - apply the 五问框架 (five-question framework) to a paper - extract transferable research design elements from a top-journal paper - understand a paper's identification strategy, estimand, or robustness logic - run /five-questions, /five-q, or /paper-five-q-analysis - "帮我做五问分析", "用五问框架分析这篇论文", "analyze this paper with the five-question framework"

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Research a topic across the web, GitHub, and X/Twitter with DeepAPI and produce a concise Markdown report. Use for varied-search or requests to compare web coverage, relevant repositories, and social discussion.

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Event prospecting skill. Takes a conference / event speakers URL, extracts the people, filters their companies against the user's ICP, then deep-researches only the speakers at ICP-fit companies. Outputs a person-first HTML report where each card answers "why should the AE talk to this person?" with all public links and a one-click DM opener. Use when the user wants to: (1) find leads at a specific conference, (2) prep for an event, (3) research event speakers, (4) build a target list from a sponsor/exhibitor page, (5) scrape conference speakers and rank by ICP fit. Triggers: "find leads at {event}", "research speakers at", "prospect this conference", "stripe sessions leads", "ai engineer summit prospects", "event prospecting", "scrape conference speakers", "who should I meet at".

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Search for scientific papers, preprints, and publications on arXiv. Extract metadata, abstracts, and download full-text PDFs or HTML versions of papers. Use when the user asks to find research papers, literature, or specific arXiv IDs.

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Identify domains, families, and sites in proteins; find all proteins in a family or sharing a domain; explore species distribution for a domain; annotate genomes with protein families and GO terms. InterPro combines 14 databases (e.g., Pfam, CDD) into one searchable resource. InterPro-N significantly expands annotation and sequence coverage with deep learning. Includes domain architecture (IDA) search.

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将deep调研结果汇总为markdown报告，覆盖所有字段，跳过不确定值。

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读取调研outline，为每个item启动独立agent进行深度调研。禁用task output。

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Add field definitions to existing research outline.

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。