Skill Radar

Agent Skill Radar

Languages: 简体中文 · English

[](https://github.com/soarsky1991/skill-radar/actions/workflows/daily-radar.yml) [](LICENSE) [](https://github.com/soarsky1991/skill-radar/stargazers)

简体中文

面向 AI agent skills、MCP servers、prompts 和 agent-native developer tools 的每日 GitHub 雷达。

大多数开发者不缺另一个 awesome list，真正缺的是一套可重复的方法：发现需求正在哪里形成，判断什么值得做，并在开新仓库之前先发布证据。

Agent Skill Radar 把公开 GitHub 信号变成每日 build brief。中文用户是这个项目优先服务的人群：开发者、技术创作者、AI 工具训练营、独立开发者和小团队，都可以用它做选题、选型、内容和产品化验证。

它怎么工作

| 步骤 | 做什么 | 输出 | |---|---|---| | 扫描 | 搜索 agent skills、MCP servers、prompts、context tools、agent-native CLIs。 | 原始仓库和 issue 数据 | | 评分 | 按热度、活跃度、issue 需求、扩展性、创作者适配度、新颖度和饱和度排序。 | 机会分数和阶段 | | 简报 | 把高分仓库转成可执行的 companion-tool 角度。 | 带 issue 证据的 build brief | | 发布 | 每天提交一份 Markdown 报告。 | GitHub、博客、图文和短视频都能引用的报告 | | 构建 | 用重复出现的强信号决定下一个小工具。 | 更少随机开坑 |

最新雷达

当前公开报告：[reports/latest.md](reports/latest.md)

首批信号示例：

| 排名 | 机会 | 构建角度 | |---:|---|---| | 1 | addyosmani/agent-skills | 跨 agent 的 skill index、installer 或 quality benchmark | | 2 | getsentry/XcodeBuildMCP | MCP registry、security checker、config generator 或 compatibility layer | | 3 | HKUDS/CLI-Anything | 带 JSON 输出和确定性工作流的 agent-native CLI wrapper |

适合谁

• 想围绕 Codex、Claude Code、Copilot、Cursor、Gemini CLI 或 MCP 做工具的开发者。
• 想先看证据再决定开源项目方向的独立开发者。
• 想把 GitHub 研究变成公众号、小红书、视频、社群选题的技术创作者。
• 想筛选 MCP/Agent 工具、做内部培训或产品化验证的小团队。

快速开始

git clone https://github.com/soarsky1991/skill-radar.git
cd skill-radar
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e .
agent-skill-radar run --limit-per-query 8 --issue-limit 4

提高 GitHub API 限额：

export GITHUB_TOKEN=github_token_here

输出：

data/YYYY-MM-DD.json
data/latest.json
reports/YYYY-MM-DD.md
reports/latest.md

CLI

采集评分 JSON：

agent-skill-radar collect \
  --limit-per-query 12 \
  --issue-limit 6 \
  --days 45 \
  --out data/latest.json

从 JSON 渲染报告：

agent-skill-radar report \
  --input data/latest.json \
  --out reports/latest.md

运行每日流水线：

agent-skill-radar run

添加自定义 GitHub 搜索：

agent-skill-radar collect \
  --query "agent memory coding assistant stars:>100 created:>=2025-01-01" \
  --out data/custom.json

评分模型

这个分数是实用的构建信号，不是投资指标。

• Heat：stars、forks 和已有受众。
• Freshness：近期仓库活跃度。
• Velocity proxy：按创建时间估算的 stars/day。
• Issue demand：comments、reactions 和需求型 issue 标题。
• Extensibility：是否自然适合 plugin、skill、MCP、CLI、prompt 或 template。
• Creator fit：是否适合做教程、benchmark、公开报告或内容系列。
• Novelty：生态是否足够新，还有明显空位。
• Saturation penalty：生态是否已经太成熟、太拥挤。

阶段：

• build-now：可以立刻做 proof of concept。
• probe-this-week：先发 fake-door README、issue reply 或 demo post。
• content-first：先做内容观察反馈，再决定要不要构建。
• archive：保留为参考。

运营节奏

• 每日：提交一份 radar report。
• 每周两次：把一个高分 gap 拆成公开内容。
• 每周：发布趋势笔记，请社区补充遗漏仓库。
• 每两周：判断最强重复信号是否值得孵化成 companion project。

路线图

• Star delta snapshots。
• Repo allowlist / denylist。
• Issue demand 聚类。
• Codex、Claude Code、Copilot、Cursor、Gemini CLI、MCP 兼容性字段。
• GitHub Pages dashboard。
• Companion repo 验证模板。

贡献

欢迎开 issue 提供：

• 应该被追踪的 repo 或生态；
• 需要过滤的噪音结果；
• 更有用的评分信号；
• 能证明真实需求的 issue、discussion 或 release 证据。

小而具体、有证据的建议最有价值。

English

Daily GitHub radar for AI agent skills, MCP servers, prompts, and agent-native developer tools.

Most builders do not need another giant awesome list. They need a repeatable way to notice where developer demand is forming, decide what is worth building, and publish evidence before opening another repo.

Agent Skill Radar turns public GitHub signals into a daily build brief. Chinese-speaking builders are the first audience for monetization and community growth, while the English version stays complete so the project can connect with the global open-source and AI agent ecosystem.

How It Works

| Step | What happens | Output | |---|---|---| | Scan | Search GitHub for agent skills, MCP servers, prompts, context tools, and agent-native CLIs. | Raw repo and issue data | | Score | Rank by heat, freshness, issue demand, extensibility, creator fit, novelty, and saturation. | Opportunity score and stage | | Brief | Convert the top repos into concrete companion-tool angles. | Build briefs with issue evidence | | Publish | Commit a dated Markdown report every day. | Reports for GitHub, blogs, newsletters, and short-form content | | Build | Use repeated strong signals to choose the next small tool. | Less random shipping |

Latest Radar

The current public report lives at [reports/latest.md](reports/latest.md).

Example signal from the first run:

| Rank | Opportunity | Build angle | |---:|---|---| | 1 | addyosmani/agent-skills | Cross-agent skill index, installer, or quality benchmark | | 2 | getsentry/XcodeBuildMCP | MCP registry, security checker, config generator, or compatibility layer | | 3 | HKUDS/CLI-Anything | Agent-native CLI wrapper with JSON output and deterministic workflows |

Who This Is For

• Developers building tools around Codex, Claude Code, Copilot, Cursor, Gemini CLI, or MCP.
• Indie hackers who want evidence before committing to a new open-source idea.
• Technical creators turning GitHub research into articles, videos, newsletters, or community discussions.
• Small teams evaluating MCP tools, agent workflows, and productization paths.

Quick Start

git clone https://github.com/soarsky1991/skill-radar.git
cd skill-radar
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e .
agent-skill-radar run --limit-per-query 8 --issue-limit 4

Set a token for higher GitHub API limits:

export GITHUB_TOKEN=github_token_here

Outputs:

data/YYYY-MM-DD.json
data/latest.json
reports/YYYY-MM-DD.md
reports/latest.md

CLI

Collect scored JSON:

agent-skill-radar collect \
  --limit-per-query 12 \
  --issue-limit 6 \
  --days 45 \
  --out data/latest.json

Render a report:

agent-skill-radar report \
  --input data/latest.json \
  --out reports/latest.md

Run the daily pipeline:

agent-skill-radar run

Add a custom GitHub search query:

agent-skill-radar collect \
  --query "agent memory coding assistant stars:>100 created:>=2025-01-01" \
  --out data/custom.json

Scoring Model

The score is a practical build signal, not an investment metric.

• Heat: stars, forks, and existing audience.
• Freshness: recent repository activity.
• Velocity proxy: stars per day since creation.
• Issue demand: comments, reactions, and demand-shaped titles.
• Extensibility: whether the repo naturally supports plugins, skills, MCP, CLI, prompts, or templates.
• Creator fit: whether it can become a visible workflow, tutorial, benchmark, or content series.
• Novelty: whether the ecosystem is young enough to have gaps.
• Saturation penalty: whether a giant ecosystem is already too mature.

Stages:

• build-now: create a proof of concept immediately.
• probe-this-week: publish a fake-door README, issue reply, or demo post.
• content-first: cover the topic, watch responses, then build.
• archive: keep for reference.

Operating Loop

• Daily: commit one radar report.
• Twice weekly: turn one high-score gap into a public breakdown.
• Weekly: publish a trend note and ask for missing repos.
• Every two weeks: decide whether the strongest repeated signal deserves a companion project.

Roadmap

• Star delta snapshots.
• Repo allowlist and denylist.
• Issue clustering by demand type.
• Compatibility fields for Codex, Claude Code, Copilot, Cursor, Gemini CLI, and MCP.
• GitHub Pages dashboard.
• One-click brief template for validating a companion repo.

Contributing

Open an issue with:

• a repo or ecosystem you think should be tracked;
• a noisy result that should be filtered;
• a scoring signal that would make the radar more useful;
• issue, discussion, or release evidence that shows real demand.

Small, evidence-backed suggestions are more useful than broad category requests.

License

MIT

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: soarsky1991
• Source: soarsky1991/skill-radar
• License: MIT
• Homepage: https://github.com/soarsky1991/skill-radar/blob/main/reports/latest.md

Install and usage instructions live in the source repository linked above.