Results for “skill-analysis”

For analysis/task/review agents using a Professional Skill on kernels, drivers, ABI/FFI, memory, concurrency, or syscalls; not for work without a native boundary.

Routes commercial inquiries to specialized sub-skills for pricing, deal desk, partnerships, channel economics, policy, RFP response, and forecasting. Provides deterministic decision support with canon-cited analysis and human-approver routing.

Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances.

Analyzes an existing Vue project's UI to map components, extract design tokens, and identify code patterns, then generates a project-specific contextual skill file for consistent future development.

Do NOT invoke this skill as a routine first step. Do NOT invoke when any installed skill (see the available skills list) can handle the task, or when general capabilities (writing, coding, analysis, translation, web search) suffice. Invoke ONLY in two cases: (1) the user explicitly asks to find, install, or browse skills / the skill marketplace; (2) no installed skill fits AND the task very likely needs a dedicated skill to be done well (e.g. a specialized file format, a vertical platform workflow). Searches the official QwenWork marketplace, the skills.sh community library, and enterprise skill markets (if available via MCP).

This skill should be used when the user asks to "analyze competitors", "assess competitive landscape", "identify differentiation", "evaluate market positioning", "apply Porter's Five Forces", or requests competitive strategy analysis.

Use when evaluating competitors, market positioning, or assessing competitive threats. This skill provides a structured framework for analyzing competitors' offerings, strategies, and market positioning to inform strategic decisions.

Routes product requests to one of 12 bundled skills covering prioritization, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, and SaaS scaffolding.

Query the Reactome REST API for pathway analysis, over-representation/enrichment, gene-to-pathway mapping, disease pathways, molecular interactions, and expression analysis. Use when running pathway enrichment on a gene list, mapping genes to curated biological pathways, or exploring disease pathways for systems biology studies. Part of the AlterLab Academic Skills suite.

Provides shared C language infrastructure for OpenClaw skills, including AST parsing, CMake analysis, Unity test generation, and security rule checking.

Analyzes codebases to understand existing features, tracing entry points, execution paths, architecture layers, patterns, and dependencies before new development.

Systematically evaluates alternatives against weighted criteria, builds a comparison matrix, and recommends the best option with documented rationale and trade-offs.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

针对某一行业（半导体、新能源、医药、消费等）的中长期基本面与产业研究，覆盖行业定义与规模、产业链与竞争格局、政策与驱动力、景气周期、趋势研判与三情景、盈利质量与落地建议。先想清楚这篇报告要证明什么判断（判断主线），再用三级数据分级取证、按固定五大板块写透，最终交付一份可直接用于战略规划、投资决策与商业化落地的飞书深度报告。对于一句话能答的问题，不要凭训练记忆或随手搜索口头作答，一律走本 Skill 的结构化多源论证。不触发并转其他场景：问该行业今天涨跌/热度/资金流/龙头股；问某个具体公司；问宏观事件对市场影响；问行业内某公司的财报。

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

Write and optimize Kusto Query Language (KQL) for Azure Data Explorer, Log Analytics, and monitoring workflows. Use this skill when users ask for KQL queries, telemetry analysis, incident investigation, or query optimization.

Seaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.

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.

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

Use this skill for content decay analysis, pruning, merging, updating, redirecting, recovery prioritization. Trigger when the task involves seo work related to Content Decay Pruning, implementation, audits, debugging, strategy, or validation.

End-to-end finite element analysis in MATLAB PDE Toolbox — geometry creation, model setup, solve, and post-processing in one skill. Use when building geometry from primitives or file import, setting up femodel with BCs/loads/materials, solving thermal/structural/EM problems, and extracting or visualizing results. Covers fegeometry, multicuboid, multicylinder, multisphere, decsg, boolean ops, mesh generation, femodel, all AnalysisTypes (thermalSteady, thermalTransient, structuralStatic, structuralTransient, structuralModal, structuralFrequency, electrostatic, magnetostatic, dcConduction, harmonic EM), materialProperties, faceBC, faceLoad, cellLoad, vertexLoad, solve, interpolation, von Mises stress, principal stress, reaction forces, heat flux, pdeplot3D visualization. Triggers on: PDE Toolbox, finite element, FEA, thermal analysis, structural analysis, electromagnetic analysis, femodel, mesh, boundary conditions, stress, displacement, heat transfer, post-processing.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

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.

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

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.