Results for “research”

Run a structured, multi-source deep-research loop on a question until the answer stops changing.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

Structured planning methodology with research, brainstorming, phased plan creation, risk assessment, and plan-to-build continuity.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

Searches the web, legal databases, case law, patents, and case.dev knowledge base via the casedev CLI. Use when the user mentions "search", "legal research", "find cases", "case law", "patent search", "web search", "fetch URL", "webfetch", "legal skills", or needs to research legal topics, find similar cases, or retrieve web content.

STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.

Run the full STORM pipeline end-to-end. This skill should be used when the user asks to "generate a storm article", "write a wikipedia-style article about X", "research and write a long-form piece on X", or invokes /storm:generate. Orchestrates research -> outline -> write -> polish, skipping already-completed phases.

Multi-source search engine powered by AIsa API. Combines Tavily web search, Scholar academic search, Smart hybrid search, and Perplexity deep research — all through a single AIsa API key. Includes confidence scoring and AI synthesis. Use when: the user needs web search, research, source discovery, or content extraction.

Use only for video game projects; route research, validation, roadmap, metrics, and launch work through the game pack

Feature specification creation from codebase research. Produces requirements, acceptance criteria, architecture decisions, implementation plans, and risk analysis.

Guidance on web content analysis, data extraction, and online research techniques for legal and regulatory information gathering

Introduction to literature search & review skills - systematic paper finding, screening, extraction, and citation traversal

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Search command center for web, academic, Tavily, and Perplexity-backed research through one AIsa API key. Use when: the user needs one flagship skill for live search, source discovery, or citation-ready research. Supports fast lookup, answer generation, and deep research reports.

Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.

AI-optimized web search using Tavily Search API. Use when you need comprehensive web research, current events lookup, domain-specific search, or AI-generated answer summaries. Tavily is optimized for LLM consumption with clean structured results, answer generation, and raw content extraction. Best for research tasks, news queries, fact-checking, and gathering authoritative sources.

AI-optimized web search using Tavily Search API. Use when you need comprehensive web research, current events lookup, domain-specific search, or AI-generated answer summaries. Tavily is optimized for LLM consumption with clean structured results, answer generation, and raw content extraction. Best for research tasks, news queries, fact-checking, and gathering authoritative sources.

Choose the right prototyping fidelity and method for the design question.

Transform research findings into actionable implementation plans with stakes-based rigor, test-first strategy, and granular task decomposition.

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

Use when the user asks about academic literature, research papers, scholarly works, authors, citations, institutions, journals, or any academic metadata. Trigger when users want to search for papers, find author profiles, track citations, discover related works, or explore academic topics. Also use when users mention DOIs, ORCIDs, h-index, publication venues, or research metrics.

金融经济学实证论文计划书生成器。根据用户提供的研究方向，生成包含标题、假说、数据来源、实证策略、 预期结果表格、稳健性检验、异质性分析、机制检验等12个完整模块的研究计划书。 内置中国微观/宏观数据库（皮皮侠1599个数据集、马克数据377个数据集）和WRDS国际数据库索引， 自动匹配可用数据源。融合Edmans (2024) "Learnings From 1000 Rejections"的编辑视角作为质量护栏， 确保选题具有真正的边际贡献而非"just another determinant of Y"。 当用户提到以下任何情境时触发：写研究计划书、research proposal、论文开题、选题+计划、 帮我设计一个实证研究、empirical research design、我想研究X对Y的影响怎么做、 帮我找个能发表的选题、generate proposal、写一个可以投稿的研究方案、 研究设计、identification strategy、DID/RDD/IV研究设计。 即使用户只是描述了一个经济金融现象并想知道"能不能做成论文"，也应考虑使用此技能。

用 Serenity 的供应链卡点逆向投资逻辑分析美股/AI 供应链标的,产出专业投研报告,包含逆向链显形、卡点判据、估值与证伪门。

Security awareness demo - demonstrates supply chain risks in AI coding assistants. See the full research: https://x.com/theonejvo/status/2015892980851474595

Search, read, create, and update Notion pages and databases. Supports knowledge capture, meeting prep, research documentation, and spec-to-task workflows.

Unified skill for content strategy and marketing content strategy: content pillars, editorial calendars, keyword research by buyer stage, positioning, messaging hierarchy, trust-building, GEO/AI search optimization, and ROI measurement. Also handles content briefs, SEO briefs, content outlines for writers, on-page SEO optimization, meta descriptions, title tags, keyword density, content research, source discovery, expert sourcing, and information gathering. Use when planning content strategy, creating content briefs for writers, optimizing existing content for SEO, researching topics and sources, or managing editorial operations. Triggers: content strategy, marketing content strategy, content marketing strategy, content planning, editorial calendar, content pillars, messaging hierarchy, content brief, SEO brief, content outline, keyword research, buyer journey content, GEO optimization, AI search optimization, content ROI, content operations, content roadmap, brand messaging, positioning

Use this skill when a user asks for SEO keyword research, keyword discovery, search volume analysis, keyword difficulty, search intent mapping, topic clusters, content opportunities, competitor keyword gaps, or a keyword strategy for a domain, URL, product, market, or seed topic. When a website is provided, crawl and interpret the site first, then use AIsa API access to DataForSEO keyword, SERP, trend, Labs, and OnPage endpoints plus AIsa LLM reasoning to find non-brand keyword opportunities. Use when: the user needs web search, research, source discovery, or content extraction.

Transforms Chan Theory technical analysis into an executable, reproducible research workflow with strict level, structure, and invalidation gates for A-shares, Hong Kong stocks, ETFs, indices, and futures. For research and study only; never issues stock tips or promises returns.

Research Twitter/X profiles, tweets, and trends, then take approved engagement and posting follow-through actions through the AIsa relay. Use when: the user needs Twitter/X research plus likes, follows, unfollows, posting, or post-action follow-through without sharing passwords. Supports relay-based reads, OAuth-approved writes, and media-capable posting flows.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.

Spawns GLM-5 powered teammates with thinking mode for coding, review, testing, planning, and research tasks.

Guides a context-driven release workflow with research, planning, verification, and autonomous execution phases.

Full specialist analysis via parallel agent dispatch. Researcher, Architect, and PM produce a prioritized report of what to build next (30-60s).

Routes ambiguous multi-stage UI work across research, concept, Figma, implementation, and QA. Do not use for a clear leaf task.

Use the model-recommender skill, Workflow C (Roster Refresh), to research and update the model roster from live benchmarks.