调研与学习技能

Build a lasting wiki from papers, links, and notes, with Markdown pages that cite their sources. Original material stays separate from compiled explanations, while a full-text search index helps agents retrieve knowledge across sessions.

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• Build a wiki for an ongoing research topic
• Organize a detailed study of one paper
• Maintain product or project knowledge

Search existing wikis for passages relevant to a question and return excerpts with source references. Use it to find recorded knowledge, check citations, and review existing coverage before drafting a new wiki page.

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• Find answers already recorded in a wiki
• Check existing coverage before writing a page
• Verify sources before quoting a claim

Grow a wiki by researching queued topics through web, arXiv, and GitHub sources. The loop checks claims against existing pages, writes or updates entries, and queues follow-up questions within budget, page-count, and research-depth limits.

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• Growing a wiki around a research topic
• Collecting paper abstracts and repository information
• Limiting research by budget and page count

Build an AI or machine learning literature review from a public starting source. Organize real papers into topics and sections, generate a cited Markdown wiki page, and add bibliography records using a configured model provider.

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• Map an unfamiliar AI or machine learning field
• Synthesize sources already collected in a wiki
• Prepare a bibliography for further research

Learn the basic skill format and check whether your skills setup is working. This minimal example responds with a friendly confirmation when you ask to test the setup or invoke the hello world skill.

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• Learning the basic skill file format
• Checking whether a skills setup works
• Trying the example prompts in Copilot

Collect cryptocurrency headlines from more than 50 sources, then filter by coin, topic, and time range. The skill ranks matching articles by relevance and recency, shows source links, and can export results as JSON for further use.

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• Review recent crypto headlines across multiple sources
• Track Bitcoin or other coin-specific updates
• Follow DeFi, NFT, or regulatory news

Plan authorized external reconnaissance through a staged workflow, from asset discovery and enrichment to exposure analysis and reporting. Organize evidence, rate confidence and severity, map identity relationships, and prepare client findings with remediation guidance.

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• Plan an authorized external reconnaissance
• Map an organization's exposed assets
• Investigate identity and breach connections

Turn a broad research topic into linked sub-questions, investigate independent questions in parallel, and follow up on important gaps before producing a synthesized report with sources and open questions. The workflow is designed for Claude Code and needs no external API.

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• Breaking a broad topic into research questions
• Comparing findings from parallel research
• Producing a sourced research report

Create detailed English prompts for AI image tools to depict ML or RL paper figures in a modern pastel style. Specify white panels, subtle shadows, rounded fonts, colorful tokens, inline illustrations, and flexible layouts.

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• Prompting a pastel ML framework figure
• Designing an RL paper diagram
• Adding tokens and illustrations to figure panels

Turn a paper or reference figure into detailed English prompts for AI image tools. Specify the diagram type, sections, data flows, formulas, dimensions, annotations, and restrained color palette for academic figures.

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• Creating a framework diagram from a paper
• Prompting a neural network architecture figure
• Planning comparison or ablation figures

Study the documented Unusual Options Activity method as a reference. It explains the focused research topic without fetching data, creating alerts, or treating legacy API notes as a current live analysis service.

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• Learning what unusual options activity research covers
• Reviewing historical notes about the method
• Separating documented techniques from measured market data

Use this reference to study the Market Sentiment method and understand its historical implementation notes. It does not fetch live data, call an API, refresh profiles, or create alerts, so it is suited to research rather than current market analysis.

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• Learning how the Market Sentiment method works
• Reviewing historical implementation notes
• Researching a technique without making live API calls

Analyze a published news item with AlphaGBM by separating reported claims from impact inferences. Get related assets, evidence limits, and next verification checkpoints from the bundled Python runner, without substituting demo results for unavailable real output.

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• Reviewing a published news item with AlphaGBM
• Listing assets related to a news event
• Separating reported facts from impact inferences

Explain the documented Strategy Builder method for studying option strategies. This reference package does not make API requests, fetch live data, create alerts, or save account records, so it supports method research rather than live strategy analysis.

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• Study the documented Strategy Builder method
• Review historical notes about option strategy building
• Understand a strategy method without live data

Use AlphaGBM to research a stock and organize returned fundamentals, sentiment, and risks into supporting and opposing evidence. The result preserves missing data and source dates, then identifies concrete questions to verify next.

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• Researching a stock with supporting and opposing evidence
• Reviewing risks and missing data in a stock report
• Identifying the next questions to verify

Use AlphaGBM to find published research, institutional views, and related assets, then summarize them with sources and dates. The workflow preserves missing information and distinguishes institution opinions from disclosed facts and your interpretation.

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• Finding published semiconductor research
• Comparing institutional views with original reports
• Reviewing a public report’s ratings, risks, and assumptions

Compare two authorized local AlphaGBM research records to identify changes in facts, scores, and judgments. The tool checks whether identities, versions, dates, and units match, and reports gaps without accessing account history or making a new paid query.

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• Comparing two local stock research records
• Finding changes in facts, scores, and judgments
• Checking whether two reports are comparable

Run a focused stock analysis through the published AlphaGBM interface. It returns the successful API response with asset identity, dates, units, missing-data flags, and score type, helping you inspect the result without invented demo data.

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• Running an AlphaGBM analysis for a stock
• Checking the score type and dated results
• Reviewing missing data in a stock response

Explains a focused method for studying prediction markets as a reference. It does not make API requests, fetch live data, create alerts, or save account records, so readers can distinguish a documented technique from measured results.

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• Learning a prediction market research method
• Understanding historical implementation notes
• Distinguishing a described technique from live results

Study a reference method for finding supply-chain bottlenecks. It explains the documented approach for research and discussion, while distinguishing a technique from measured results; it does not fetch live data, run API requests, or create alerts.

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• Learning a method for studying supply-chain bottlenecks
• Discussing the documented research approach
• Distinguishing a research technique from measured results

Study the documented put-spread backtest method as a reference. This skill explains the technique and helps keep documented methods distinct from measured results; it does not fetch market data or run an API analysis.

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• Learning how a put-spread backtest is described
• Reviewing historical implementation notes as research
• Distinguishing a documented method from measured results

Explain a documented method for understanding option Greeks, the measures that describe how option values respond to changing conditions. This reference package does not fetch live data or run API requests, so it helps with conceptual study rather than measured analysis.

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• Learn how a documented Greeks method works
• Understand option pricing sensitivity measures
• Review historical implementation notes as research material

Break down a published research report into institutional views, original ratings, assumptions, and risks, keeping sources and dates attached. It helps readers verify what the report says and identify follow-up checks without triggering paid research.

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• Summarizing a published research report
• Checking original ratings and assumptions
• Identifying report risks and follow-up checks

Explains the volatility smile method as a focused reference for study. It does not connect to an API or fetch live data, allowing readers to learn the documented technique while keeping it separate from measured market results.

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• Learning how the volatility smile method is described
• Reviewing historical implementation notes as reference
• Separating a documented technique from live market data