Research, academic papers, education, and exam prep (4.4K skills)
Search the web with a specific question using the included Python script. Receive an agent-generated answer with reference details, suggested follow-up questions when available, and token usage; optional console settings enable richer presentation features.
Extract titles and content from URLs pointing to web pages, PDFs, or Douyin videos. Submit several URLs together and receive a JSON list, a structured format containing the title and content for each link.
Read 小红书 / RedNote posts from shared links and summarize captions separately from information inside images. Check the post's identity and image order, then report sources, reading coverage, and claims that remain unverified.
Monitor changes in the Top Lean AI ranking, keep a history of updates, and produce reports analyzing those changes. It uses an existing OpenClaw environment and saves tracking state for later analysis.
Search the web or find images through the Ask-Echo API from 火山引擎. Retrieve result details, request an AI-generated summary, and narrow searches by time range or selected websites.
Retrieve an arxiv paper's full text from its HTML version using an ID or URL. Extract readable text with section structure, optionally save it to a file, and prepare it for reading, notes, or technical analysis.
Find learning materials across papers, official documentation, and code repositories, then prioritize them using source evidence, current goals, and reading history. It also supports detailed reading explanations and integrating findings into a specified note.
Collect industry information from RSS feeds, websites, and APIs using over 207 configured sources. Remove duplicates, apply AI scoring, categorization, and summaries, and produce Markdown reports or JSON data.
Research product, design, and technical questions using external sources. Check where claims come from, distinguish observed behavior from interpretation, and turn findings into comparisons, recommendations, or concrete next steps for implementation.
Gather recent AI products, papers, tools, and viewpoints into a Chinese and English HTML daily report. Select up to ten supported items, check original sources where possible, and summarize shared trends with practical next actions.
Research complex topics through repeated web searches, adjusting the next search based on earlier findings. Combine the collected information into a detailed Markdown report with source references, conclusions, and remaining uncertainties.
Organize Marin experiment reports into a maintained index, or archive explicitly named older experiments using lasting tags and issue references. Existing conclusions and canonical links are preserved so experiment records remain traceable.
Work through topology problems about connectedness, the property of a space having no separation into two nonempty open parts. It outlines proof strategies, path construction, and connected components, with command examples for symbolic calculation and proof tools.
Plan solutions to topology problems involving open sets and continuous maps. Choose criteria based on distances or preimages, the points mapping into a set, and organize reasoning about interiors, closures, boundaries, and epsilon-delta conditions.
Work through limits in real analysis by choosing among substitution, algebraic manipulation, derivative-based methods, and bounding arguments. The skill also guides rigorous epsilon-delta proofs and provides commands for symbolic calculations and proof checks.
Organize topology problems about compactness, a property defined through finite selections from open covers. Choose tests suited to the space, examine products, and apply compactness to continuous images and maximum or minimum values.
Review progress while solving a math problem by organizing previous attempts, checking intermediate results, and assessing growing complexity. The skill recommends whether to continue, adjust the approach, switch strategies, or tackle a smaller problem first.
Work through convergence problems in real analysis by identifying sequence and series types, choosing suitable tests, checking limits and bounds, and calculating sums when a series converges.
Solve optimization problems with equality constraints, inequalities, or variable bounds. Guides method selection, symbolic equations, and optimality checks, with SciPy examples for finding numerical solutions that respect the stated constraints.
Work through convex optimization problems by checking convexity, putting objectives and constraints into a standard form, and choosing a solver. The skill provides SciPy examples and outlines checks for constraint satisfaction and mathematical conditions used to assess optimality.
Helps solve contour integral problems by choosing suitable paths, locating singularities, and applying the residue theorem, which relates an integral to values around poles. Includes Sympy commands for finding poles, calculating residues, and checking definite integrals.
Work through linear algebra problems involving vector spaces, from checking subspaces and linear independence to finding bases, dimensions, and coordinate changes. The skill connects each task to symbolic calculation or proof scripts for checking the results.
Work through predicate logic problems by examining quantifiers, which express “all” or “some,” organizing formulas, and choosing proof steps. The skill also guides resolution proofs and countermodels that show why an argument is invalid.
Work through integration problems in measure theory by matching the problem's conditions to suitable theorems. The skill covers simple functions, exchanging limits and integrals, lower bounds, and changing integration order, with commands for symbolic calculations and proof tools.