Claude Configs

Use when you need to run a binary, trace execution, or observe runtime behavior. Runtime analysis via QEMU emulation, GDB debugging, and Frida hooking - syscall tracing (strace), breakpoints, memory inspection, function interception.

This skill should be used when moving from design to implementation. Triggers on "let's build", "implement this", "looks good let's code", "ready to implement". Presents options for parallel agent competition (cookoff), single subagent, or local implementation.

中国互联网品牌感知监测统一工具。聚合全网热榜追踪（hub）、小红书/抖音/公众号近30天深度讨论研究（cn30）、豆包/Kimi/DeepSeek AI 搜索品牌可见度分析（geo）三个互补模块，帮助品牌方/市场运营/内容创作者从「话题热度—真实口碑—AI 回答」三层视角全面感知品牌。当用户需要研究中国社交媒体热点、分析品牌舆情、对比竞品口碑、分析品牌在 AI 搜索中的表现、做 GEO 优化、追踪全网热点时使用。触发词：品牌感知、舆情监测、社媒研究、热榜聚合、AI 搜索可见度、GEO 分析、跨平台舆情、热点追踪、品牌口碑、品牌竞品对比、社媒热榜。

短剧-公众号信息源 — 每日扫描公众号短剧爆款文章,按阅读量筛选热门内容,智能聚类题材方向后生成包含封面图、互动数据与创作洞察的HTML日报。支持按题材(穿越/霸总/重生等)、公众号、时间范围定向查询。⚠️查询前脚本先做输入校验：关键词需命中短剧题材词库（topic_keywords 中规定的题材名+全部相关词，如「打脸」命中逆袭题材相关词），命中后直接使用该关键词查询数据；不满足时提醒'关键词不满足查询条件'并推荐相关词，且**不发起接口请求**；日期超出有效查询范围时提醒并自动回退最近有数据日期，无需用户确认。**无数据确认制**：查询无匹配数据或数据不足时禁止自动发起任何额外查询（禁止自…

公众号内容创作与运营分析聚合技能。一个 Skill 覆盖公众号创作与运营全链路：10w+ 爆文榜单、爆款正文创作、标题生成+评分、文案改写（含改写记录自动上报）、违禁词检测、爆款封面设计、账号四维度诊断。基于红狐数据每日收录的海量公众号爆款数据，把灵感打磨成可直接发布的成品。当用户需要写公众号文章、起标题或评估标题、改写文案、设计封面、检测违禁词、查 10w+ 爆文、诊断公众号账号时使用。触发词：公众号、公众号文章、公众号标题、公众号改写、公众号封面、公众号违禁词、10w+、爆文、账号诊断。

小红书爆款内容创作专家。一个 Skill 覆盖笔记生成、文案改写、爆款标题生成与评分、封面设计、违禁词检测五大能力，基于红狐数据每日更新的爆款笔记库，把灵感打磨成可直接发布的成品。当用户需要写小红书笔记、改写文案为小红书风格、起标题或评估标题、设计封面、检测违禁词时使用。触发词：小红书笔记、小红书改写、小红书标题、小红书封面、小红书违禁词、小红书创作、小红书爆款。

Systematically investigate bugs, test failures, build errors, performance issues, or unexpected behavior by cycling through characterize-isolate-hypothesize-test steps

Use when creating distinct website directions, a client review picker, asset catalog, previews, and Cloudflare-ready handoffs.

Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.

Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++.

Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.

Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido.

GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.

De novo and known TF motif enrichment in ChIP-seq/ATAC-seq peaks via HOMER. findMotifsGenome.pl finds over-represented patterns vs background; annotatePeaks.pl assigns context (TSS distance, gene, repeat).

Poisson-model peak caller for ChIP-seq/ATAC-seq BAMs. MACS3 callpeak finds enriched regions (TF sites or histone marks) vs input/IgG; outputs BED narrowPeak/broadPeak for motif analysis, annotation, and differential binding.