Przydatne Agent Skills ocenione według realnego użycia

alexgaoth

find-reverse-engineered

Find an open-source, reverse-engineered tool for a closed app, API, device, protocol, file format or game, then read or fetch it. Use when a task needs to talk to something that has no official API, SDK, driver or spec, or only a limited one. Examples include "control my Tuya plug locally", "read a .psd or .dwg file", "unofficial WhatsApp, Instagram or Bilibili client", "Bambu printer protocol", "talk to my car's API", "Garmin or CGM data", "decompilation of a game" and "OpenAI-compatible proxy

instalacje: 1
GitHub Stars: 162
Aktualizacja: 20 paź

MIT2 Pliki

pixel-point

animate-text

Curated text animation catalog with exact JSON specs for headings, labels, counters, and text swaps. Use when an agent needs to pick or translate named effects like soft blur in, typewriter, shared axis, line reveal, stagger, crossfade, or kinetic builds into WAAPI, Motion, Framer Motion, GSAP, CSS, Lottie, Rive, or similar stacks.

instalacje: 1
GitHub Stars: 150
Aktualizacja: 24 kwi

63 Pliki

logfire-evals

Run offline Python (pydanticevals) or Node.js (logfire/evals) evaluations and review them in Logfire. Also redirect existing Braintrust Eval() suites. Use for eval setup, test datasets, AI scoring, agent checks, LLM judges, Braintrust migration, or Logfire Datasets & Experiments. Not for live traffic or infrastructure monitoring.

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT1 Pliki

logfire-infrastructure

Monitor hosts, Docker containers, Kubernetes clusters, database/queue/cache servers, and cloud-provider metrics with Pydantic Logfire — no application code required. Use this skill whenever the user asks to "monitor my host/server/VM", "monitor my Docker containers", "monitor my Kubernetes cluster", "send infrastructure metrics to Logfire", "watch my database/Postgres/Redis/MongoDB/Kafka", "collect cloud metrics" (AWS/GCP), or mentions the OpenTelemetry Collector in the context of Logfire. This

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT2 Pliki

logfire-query

Query and analyze Logfire telemetry data — traces, logs, spans, metrics, summaries, and SQL results. Use this skill when the user asks to "query logfire", "search traces", "find logs", "query data", "search spans", "look up errors in logfire", "get metrics from logfire", "analyze telemetry", "summarize errors", "find root cause", or add Logfire querying capabilities to code. Do not use this skill for direct Logfire UI, browser, live-view, Explore-page, or link-opening requests; use logfire-ui in

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT3 Pliki

migrating-agno-to-pydantic-ai

Migrate Python Agno applications to Pydantic AI and, only when needed, Pydantic AI Harness. Use when source code imports agno or relies on Agno agents, teams, workflows, sessions, memory, knowledge, tools, hooks, guardrails, approvals, skills, streaming, or AgentOS.

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT4 Pliki

migrating-google-adk-to-pydantic-ai

Migrate Python Google Agent Development Kit (ADK) applications to Pydantic AI. Use for LlmAgent, Runner, sessions, state, memory, tools, callbacks, plugins, graph or dynamic workflows, resumability, events, artifacts, MCP, and ADK deployment boundaries.

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT5 Pliki

migrating-pi-to-pydantic-ai

Migrate TypeScript Pi coding-agent applications, extensions, or packages to Python with Pydantic AI and Pydantic AI Harness. Use when source code imports @earendil-works/pi-, calls createAgentSession, registers Pi extensions, or relies on Pi tools, hooks, skills, sessions, compaction, providers, TUI, RPC, or packages.

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT4 Pliki

pydantic

Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.

instalacje: 1
GitHub Stars: 144
Aktualizacja: 16 paź

MIT1 Pliki

chen-yiwei-perspective

陈奕蔚（会计视野论坛版主，中审众环会计师事务所合伙人）的专业咨询思维框架。基于2003-2026年24年、约3000万字答疑文章提炼，涵盖5个核心心智模型、10条决策启发式、完整的表达DNA和回答工作流。用于会计准则实务解读、审计问题咨询、财务处理判断。触发词：陈版主、陈奕蔚、会计准则咨询、审计实务、合并报表、收入确认、权益性交易、会计处理、企业会计准则、CAS 14、CAS 21、CAS 33。

instalacje: 1
GitHub Stars: 135
Aktualizacja: 23 sie

MIT67 Pliki

chenyiwei-bbs

陈版主实务问答检索 Skill。当用户想查询"陈版主"、"陈奕蔚"、"审计问答"、"视野论坛"、"会计准则"、"审计实务"、"CPA问答"、"注册会计师"、"会计处理"、"审计问题"、"收入确认"、"合并报表"、"商誉减值"等任何审计/会计实务问答时使用。Skill 会直接 curl 公开 REST API 拉取数据并整理成中文 markdown，不需要 API Key 或 MCP server。不要 undertrigger——用户问审计/会计实务问题而你不调本 Skill 就是把过时的训练数据当作最新实务解答，对用户有害。

instalacje: 1
GitHub Stars: 135
Aktualizacja: 23 sie

MIT1 Pliki

local-rag

本地向量知识库，支持按项目管理文档（docx/doc/pdf/md），语义检索。默认用硅基流动免费 API，零模型安装即可使用。支持多项目隔离、中文制度文档专用切片、Embedding+Rerank 两阶段检索。触发词：知识库、向量检索、RAG、制度检索、文档入库、语义搜索、local rag、搜制度、查条款、文档检索、从文档中搜、语义匹配。即使用户只是说"帮我从这些文件里找到关于XX的规定"或"对比两份制度的差异"，也应使用本 skill。

instalacje: 1
GitHub Stars: 135
Aktualizacja: 23 sie

MIT18 Pliki

opencode-session-visualizer

将 OpenCode 会话导出为可视化的 HTML 网页，复刻官方 share 页面的暗色主题样式。从本地 SQLite 数据库提取完整对话数据（包括上下文压缩前的早期消息），生成的 HTML 包含 Markdown 渲染、代码高亮、工具调用折叠卡片（含完整 input/output）、思考过程折叠、左侧对话导航边栏、删除/截断对话功能。触发词：可视化会话、导出会话、会话分享、session visualizer、share session、导出完整对话、查看压缩前对话、生成会话网页、会话 HTML、conversation visualizer、 opencode 会话导出、完整对话记录、删除对话、清理会话。当用户提到任何与会话导出、可视化、分享、完整记录查看相关的需求时都应使用此 skill。即使用户只说"帮我看下之前那个对话"或"导出这个会话"也应触发。

instalacje: 1
GitHub Stars: 135
Aktualizacja: 23 sie

MIT4 Pliki

related-party-identification

基于工商数据自动识别"隐形关联方"的审计核查工具。读取被审计单位及其客户、供应商的工商完整维度（52个Excel），自动比对地址/电话/邮箱/网址/法定代表人/董监高/股权穿透/客商异常画像/无形资产共用/担保链/历史关联痕迹等八大维度，输出多sheet Excel核查报告（汇总判断sheet + 各维度证据sheet），揪出未披露的关联方关系。当用户提到关联方、关联方核查、关联方识别、隐形关联方、隐性关联方、未披露关联方、关联交易非关联化、关联关系、客户供应商关联关系、客户是不是关联方、供应商有没有猫腻、股权穿透、实际控制人核查、关联方尽调、审计关联方、related party、关联方核查底稿时使用。即使用户只说"查一下这几家公司之间有没有关联""帮我看下这批客户供应商的工商数据""这公司是不是空壳" "审计要核查关联方"也应触发。配合 cicpa-company-query skill 取数，规则集对齐证监会/财政部近年处罚案例。

instalacje: 1
GitHub Stars: 135
Aktualizacja: 23 sie

MIT6 Pliki

shaoniannu-perspective

少年怒（审计小哥）的思维框架与表达方式。基于241篇微信公众号文章 + 评论区1114条作者回复（中位11字，76%≤20字）的统计分析，提炼6个核心心智模型、7条决策启发式和完整的表达DNA。核心特征：区分对话回应（默认，该短则短该长则长，给实质判断但不灌水）/ 评论极简子模式（仅模拟评论区时，一句话搞定、怼人不论证不接产品）/ 正文回应（显式触发，才用故事堆叠/情绪曲线/算账说理）。何时触发显式触发（用户直接点名）： - 「用少年怒的视角/角度」「少年怒会怎么看」「少年怒会怎么做」 - 「审计小哥模式」「切换到审计小哥」「少年怒 perspective」 - 「帮我用少年怒的角度想想」「以审计小哥的身份回答」场景触发（用户没点名，但问题明显需要实务派判断）： - 审计实务问题（底稿、合并报表、现金流、四表一注）+ 暗示想听"行内人怎么说" - 审计职业选择（进四大/八大、跳槽、考 CPA、城市选择、转行） - 审计效率/工具（Excel、VBA、审计软件、RPA、AI 替代） - 知识付费/审计创业商业模式当这类问题伴随"说说你的判断""实务上怎么办""行内人怎么看"等暗

instalacje: 1
GitHub Stars: 135
Aktualizacja: 23 sie

MIT7 Pliki

chakrit

kien-thai

Write Thai-language prose (technical documentation, marketing copy, explainers, blog posts, translations) that reads like a real Thai writer — not generic AI output. Counters training-data skew toward over-formal, over-polite, calqued Thai. TRIGGER when: user asks for any non-trivial Thai prose output (paragraph or longer — blog post, landing page, doc page, README in Thai, email, announcement, section); user asks to translate English into Thai; user asks to edit, rewrite, or review existing Tha

instalacje: 1
GitHub Stars: 96
Aktualizacja: 26 lip

MIT12 Pliki