3,758 Open Skills

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Import Skills

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heurist-network heurist-network
from GitHub Development & Coding
市场 数据库 Python

heurist-skill-marketplace-admin

Admin operations for the Heurist Skill Marketplace. Use this skill when the user asks to add, ingest, approve, reject, remove, update, or list skills in the Heurist marketplace. Also triggers for checking upstream changes, updating external API dependencies, updating metrics (stars/downloads), re-ingesting skills, or any marketplace database management task. Working directory is heurist-agent-framework. Always use uv run python to run scripts.

⬇0 ❤787 4 days ago · Uploaded Detail →
2217173240 2217173240
from GitHub Databases & Storage
图式 数据库 典型对象

cybernetic-systems-engineering

用“系统工程 + 工程控制论 + 总体设计（GDA）”的方法做软件工程闭环控制。 适用：真实代码库中的 bugfix、feature、refactor、性能、迁移、事故复盘、测试设计、架构审计、门禁验证。 尤其适合：问题复杂、跨模块、需要最小可验证变更、需要离线/在线双层验证、需要防止“测试全绿但真实环境失败”的任务。 不适用：纯知识问答、翻译、创作、一次性聊天建议、无需验证的单句结论。 --- # Cybernetic Systems Engineering（CSE） 把软件开发当作一个**闭环控制系统**来做： - **被控对象（Plant）**：代码库 + 运行时环境 + 依赖 + 数据 + 测试系统 - **控制器（Controller）**：你（Agent）+ 你的计划、策略、节奏控制 - **传感器（Sensors）**：测试、构建日志、运行日志、指标、复现脚本、审计文档 - **执行器（Actuators）**：代码修改、配置变更、依赖升级、回滚、文档更新、测试补强 - **参考输入（Reference）**：需求、issue、验收标准、SLO/SLA、业务边界 - **输出（Output）**：实际行为（通过/失败测试、运行结果、性能数据、门禁结果） - **误差（Error）**：参考输入与输出之间的差距 - **扰动（Disturbance）**：环境差异、并发、flake、脏状态、真实 schema 漂移 - **时滞（Delay）**：慢测试、CI 排队、真实环境依赖、发布窗口 本 skill 的目标不是“让你会用控制论术语”，而是让你在复杂工程任务中： 1. 先建立正确的观测与误差定义 2. 再用最小控制输入减少误差 3. 用分层验证防止振荡与假收敛 4. 用总体设计视角控制复杂性 ## 项目级控制拓扑 CSE 不只处理“当前这一条任务怎么修”，还要先回答“这个项目由谁控制、在哪一层控制、跨模块如何协调”。 从控制层级看，CSE 默认同时存在两套闭环： - **任务级闭环** - 面向单个 issue、单次 patch、单轮验证 - 关注最小控制输入、最小 failing case、分层验证 - **项目级控制拓扑** - 面向多模块、多团队、多阶段演化 - 关注总体设计部、控制结构、跨模块协调、边界冻结与升级路径 如果只建立任务级闭环，而没有项目级控制拓扑，常见结果是： - 单个 patch 看起来正确，但把复杂性偷偷转移到别的模块 - 局部验证通过，但共享接口、共享状态或共享基础设施被无意破坏 - 每个人都在改“自己眼前那一层”，但没有人对整体误差负责 因此在以下场景，必须先补项目级控制拓扑，再进入具体修复： - 改动跨 2 个及以上模块、服务或语言边界 - 需要同时改代码、配置、schema、运行流程中的两类以上对象 - 任务会影响共享接口、共享状态、共享基础设施或统一门禁 项目级控制拓扑至少要回答四个问题： 1. **总体设计部在哪里** - 谁负责维护项目级参考输入、关键边界和最终裁决 2. **控制结构是什么** - 本次问题主要落在哪些层，哪些层只能观测，哪些层允许施加控制输入 3. **跨模块协调怎么发生** - 哪些模块存在强耦合，改动会沿什么路径外溢，谁需要被显式通知或升级 4. **哪些边界先冻结** - 哪些接口、schema、门禁口径在本轮不能被顺手改写 ### 控制面 / 数据面 / 状态面 为了避免“看起来只是改一行代码，实际上改穿了整个系统”，复杂任务开始前必须先识别本次改动主要落在哪一面： - **控制面** - 负责决定系统如何调节自己 - 典型对象：限流、重试、熔断、路由、灰度、回滚、门禁、调度策略 - **数据面** - 负责承载真实业务流量和核心处理路径 - 典型对象：请求处理、任务执行、核心计算、主链调用、用户可见结果 - **状态面** - 负责保存共享事实、恢复锚点与跨节点一致性 - 典型对象：数据库、schema、缓存、队列、事件日志、检查点、幂等键 默认要求： 1. 先标出本次改动的**主落点**在哪一面 2. 再标出会被连带影响的次级面 3. 如果一次改动同时触碰两面以上，必须显式写出复杂性是如何转移的 一个典型例子： - 把重试、限流从业务代码下沉到 mesh 或统一网关 - 不是“复杂性消失了” - 而是复杂性从数据面转移到了控制面 - 把会话、任务进度或幂等信息从应用内存迁到 Redis / 数据库 - 不是“状态更简单了” - 而是状态复杂性从节点内部转移到了状态面 如果主落点都判断不清，默认先不要改实现，而是先补控制结构说明。 ### 复杂性转移账本 复杂性不会凭空消失，只会被下沉、上浮或转移到别处。 因此每次声称“系统更简单了”时，都应

⬇0 ❤38 13 days ago · Uploaded Detail →
aliyun aliyun
from GitHub Databases & Storage
数据库 分析数据库 工具

alibabacloud-adb-mysql-copilot

阿里云 AnalyticDB for MySQL 运维诊断助手。支持集群信息查询、性能监控、慢查询诊断、运行中SQL分析、表级优化建议等。当用户提到 ADB MySQL、AnalyticDB、慢查询、SQL 诊断、集群性能、查询RT、BadSQL、数据库优化、表分析、分区诊断、数据倾斜等话题时，应该使用该 Skill。即使用户没有明确说"使用 Skill"，只要涉及 ADB MySQL 相关的运维、诊断或监控问题，也应主动使用。

⬇0 ❤26 12 days ago · Uploaded Detail →
FlameMida FlameMida
from GitHub Development & Coding
自动化 数据库 代码重构和

feat-dev

提供 7 阶段功能开发工作流（需求理解、代码探索、澄清问题、架构设计、实施、质量审查、总结）。适用于新功能实施、API/数据库/服务架构设计、代码重构和多模块开发。当用户提出功能开发、架构设计或代码重构需求时触发。

⬇0 ❤9 2 days ago · Uploaded Detail →
chaofanat chaofanat
from GitHub Databases & Storage
数据库 回忆 视窗

celery-task

Celery 异步任务派发和管理技能，支持跨平台部署（Windows/Linux/macOS）。提供独立的技能环境自动配置、命令执行任务、延时调度、任务监控等功能。适用场景：(1) 异步执行命令或脚本，(2) 延时或定时执行任务，(3) 任务队列管理和监控，(4) 分布式任务处理。内置跨平台兼容处理，自动检测系统类型并适配配置。**三个必要服务**：Redis/Memurai、Celery Worker、Flower 监控都是必要的，任何一个未运行都应该自动启动。**AI 助手自动启动服务**：派发任务前自动检查并启动未运行的任何服务，无需用户手动干预。使用前需先安装 Redis/Memurai（Windows）。

⬇0 ❤9 11 days ago · Uploaded Detail →

Skill File Structure Sample (Reference)

skill-sample/
├─ SKILL.md              ⭐ Required: skill entry doc (purpose / usage / examples / deps)
├─ manifest.sample.json  ⭐ Recommended: machine-readable metadata (index / validation / autofill)
├─ LICENSE.sample        ⭐ Recommended: license & scope (open source / restriction / commercial)
├─ scripts/
│  └─ example-run.py     ✅ Runnable example script for quick verification
├─ assets/
│  ├─ example-formatting-guide.md  🧩 Output conventions: layout / structure / style
│  └─ example-template.tex         🧩 Templates: quickly generate standardized output
└─ references/           🧩 Knowledge base: methods / guides / best practices
   ├─ example-ref-structure.md     🧩 Structure reference
   ├─ example-ref-analysis.md      🧩 Analysis reference
   └─ example-ref-visuals.md       🧩 Visual reference

More Agent Skills specs Anthropic docs: https://agentskills.io/home

SKILL.md Requirements

├─ ⭐ Required: YAML Frontmatter (must be at top)
│  ├─ ⭐ name                 : unique skill name, follow naming convention
│  └─ ⭐ description          : include trigger keywords for matching
│
├─ ✅ Optional: Frontmatter extension fields
│  ├─ ✅ license              : license identifier
│  ├─ ✅ compatibility        : runtime constraints when needed
│  ├─ ✅ metadata             : key-value fields (author/version/source_url...)
│  └─ 🧩 allowed-tools        : tool whitelist (experimental)
│
└─ ✅ Recommended: Markdown body (progressive disclosure)
   ├─ ✅ Overview / Purpose
   ├─ ✅ When to use
   ├─ ✅ Step-by-step
   ├─ ✅ Inputs / Outputs
   ├─ ✅ Examples
   ├─ 🧩 Files & References
   ├─ 🧩 Edge cases
   ├─ 🧩 Troubleshooting
   └─ 🧩 Safety notes

Why SkillWink?

Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.

We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.

Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

Quick Start:

Import/download skills (.zip/.skill), then place locally:

~/.claude/skills/ (Claude Code)

~/.codex/skills/ (Codex CLI)

One SKILL.md can be reused across tools.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.

Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.

2. How do Skills work?

Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.

This keeps agents lightweight while preserving enough context for complex tasks.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared across tools?

Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.

Example: retrieval + writing + automation scripts as one workflow.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up