Token导航 LogoToken导航

Agent Skill · 研究检索

feature-radar-scan(特征雷达扫描)

feature-radar-scan 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

feature-radar-scan 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 63,254。

总安装 318周安装 13GitHub Stars 12下载 102核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:feature-radar-scan(特征雷达扫描)
来源仓库:https://github.com/runkids/feature-radar
仓库路径:skills/feature-radar-scan
安装命令:
npx skills add https://github.com/runkids/feature-radar --skill feature-radar-scan
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/runkids/feature-radar --skill feature-radar-scan

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

扫描潜在功能机会并加入雷达库,过滤弱信号。需强证据支撑才创建机会文件,避免无效消耗。支持 brainstorm 模式与焦点阅读模式两种触发方式。适用于持续发现增量改进与突破性想法。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

feature-radar-scan 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/feature-radar-scan

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/runkids/feature-radar --skill feature-radar-sc…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

feature-radar-scan 怎么安装?

在终端执行 npx skills add https://github.com/runkids/feature-radar --skill feature-radar-scan。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

feature-radar-scan 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

feature-radar-scan 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

feature-radar-scan 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

feature-radar-scan 用的人多吗?

公开数据是累计安装 318 次,近一周安装 13 次,下载 102 次,GitHub 12 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

feature-radar-scan 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
feature-radar-scan

特征雷达扫描 · 本页

31812需要联网未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Scan Opportunities

Discover new feature opportunities and add them to .feature-radar/opportunities/.

Deep Read

Behavioral Directives

Additional directive for this skill:

  • Filter aggressively — Do NOT create opportunity files for weak signals. If you can't cite concrete demand evidence, skip it.

Brainstorm Intake

Enter Brainstorm Intake if ANY of these are true:

  • User says "I have an idea", "what if we...", "I was thinking about...", "brainstorm"
  • User describes a problem without a clear feature shape
  • User's input lacks specific demand signals, impact/effort estimates, or a concrete feature name

Skip Brainstorm Intake if ALL of these are true:

  • User gave a specific directive like "scan opportunities", "scan ecosystem", "find new features"
  • User's input does not contain a personal idea or vague exploration

If skipping, jump directly to ## Workflow.

Phase 1: Core Questions

Ask these one at a time. Prefer multiple-choice when possible.

  1. Problem space — "What problem are you trying to solve, or what experience do you want to improve?"

- Cross-reference: search existing opportunities/ and archive/ for related themes. - If a match is found, surface it: "Is this related to #{nn} {title}, or a completely different direction?"

  1. Target user — "Who would benefit from this feature?"

- Offer choices derived from base.md Project Context if available.

  1. Spark — "What triggered this idea?"

- (A) A pain point from my own usage - (B) Saw a similar feature in another tool/project - (C) New technical possibilities (new API, new library) - (D) Community/user feedback - (E) Pure creative exploration

Phase 2: Adaptive Depth

After Phase 1, assess idea maturity:

Mature (has clear problem + user + demand signal): → Ask 1 closing question to confirm scope, then proceed to Exit.

Emerging (has problem but fuzzy shape): → Ask up to 3 more questions to sharpen:

  • "What does the usage look like in your mind?"
  • "Have you seen an implementation you particularly liked?" (if yes, consider creating a references/ entry)
  • "What's the minimum scope that would feel useful?" (MVP scoping)

Raw (pure exploration, no clear problem yet): → Switch to open-ended dialogue. Ask up to 5 more questions:

  • Explore adjacent possibilities
  • Challenge assumptions: "If we don't build this, what's the biggest loss?"
  • Seek demand signals: "Has anyone (including yourself) run into this problem repeatedly?"
  • Stop when: a clear feature shape emerges, OR user says "enough"

Exit: Output Options

Summarize the refined idea:

"Here's a summary of our discussion:

  • Problem: {problem}
  • Target user: {target user}
  • Direction: {feature shape}
  • Demand signal: {demand evidence or 'creative exploration'}
  • Related items: {related opportunities/archive/specs, or 'none found'}"

Then ask:

"What would you like to do next?"

  • (A) Proceed to scan — use this direction as focused context, search all 6 sources for supporting evidence and related opportunities
  • (B) Save as opportunity draft — write to opportunities/{nn}-{slug}.md with Status: Open, decide later whether to scan

If (A): Pass the summary as context into ## Workflow Step 1, with a narrowed focus on the identified direction. If (B): Create the opportunity file following ../feature-radar/references/SPEC.md § 3.3, populate fields from the intake summary, then run the Annotation Checkpoint per ../feature-radar/references/WORKFLOW-PATTERNS.md. After approval, present Completion Summary and suggest "run a focused scan around this direction" as a next step.

Workflow

  1. Identify sources — where to look for ideas:

- User signals: issues, discussions, forum posts, support requests - Creative exploration: "what if we..." brainstorming, combining existing features in new ways - Ecosystem evolution: adjacent tools, emerging standards, new capabilities in dependencies - Technical possibilities: new APIs, libraries, or techniques that enable things previously impossible - Cross-project research: interesting approaches from related projects (from base.md Inspiration Sources) - Community conversations: Reddit, HN, Discord, blog posts - If Brainstorm Intake was completed: use the intake summary as the primary focus direction. Prioritize sources most relevant to the identified problem space. Still scan all 6 source types, but weight results toward the intake direction.

  1. Scan and collect — for each source, look for:

- Unmet user needs and recurring pain points - Feature ideas with demand signals (upvotes, comments, multiple independent asks) - Creative approaches that could enhance existing functionality - Technical breakthroughs that unlock new possibilities - Patterns emerging across multiple tools

  1. Deduplicate — check against existing opportunities/ and archive/ files
  2. Cross-reference codebase — for each candidate, search the project to check:

- Already partially implemented? → mark as "Partially Done" - Does existing architecture support this? → note in "Design Notes" - Related TODOs or FIXMEs in the code? → cite them

  1. Evaluate each candidate:
CriterionQuestion
Real user demandAre users actually asking for this, or does it solve a latent need?
Value upliftDoes this meaningfully improve the user experience or unlock new possibilities?
Innovation potentialDoes this introduce a creative breakthrough or unique approach?
Effort / impact ratioIs the cost justified by the benefit?
Architectural fitDoes it align with our core philosophy?
Ecosystem timingIs the ecosystem ready?
  1. Create opportunity files — for each viable candidate, write .feature-radar/opportunities/{nn}-{slug}.md
  2. Checkpoint — Review & Annotate per ../feature-radar/references/WORKFLOW-PATTERNS.md

Present scan results using this format:

Scan complete: {n} new opportunities
| # | Opportunity | Demand Signal | Impact | Effort | Source |
|---|------------|---------------|--------|--------|--------|
| {nn} | {title} | {evidence} | H/M/L | H/M/L | {where found} |
  1. Update base.md — increment opportunities count, update Value & Innovation Landscape if needed

Opportunity File Format

Use the format defined in ../feature-radar/references/SPEC.md § 3.3 (opportunities/{nn}-{slug}.md).

Guidelines

  • Don't create opportunities for every idea you find. Filter aggressively — weak signal wastes attention.
  • 1 issue with no comments = weak signal. Multiple independent asks = strong signal.
  • Creative ideas without existing demand can still be valid — evaluate innovation potential separately.
  • Write an honest "Our Position" — it's OK to say "we don't want this" or "not yet."
  • Number sequentially from the highest existing number in opportunities/ and archive/.
  • If scanning reveals problems others have that we've already solved, add to references/ instead.

Example Output

→ Created opportunities/07-streaming-output.md (Impact: High, Effort: Medium)
→ Skipped: "hook system" already exists as opportunities/03-hook-system.md
→ Updated base.md: opportunities 6 → 7

Brainstorm Intake Example

User: "I was thinking... what if we could automatically detect when a feature is getting stale?"

Phase 1:
  Q1 (Problem): "What problem are you trying to solve?" → Feature opportunities sitting unreviewed
  Cross-ref: Found #05 role-assignment — user confirms: different direction
  Q2 (Target): "Who benefits?" → Project maintainers managing backlogs
  Q3 (Spark): (A) Pain point from own usage

Phase 2 (Emerging → 2 follow-ups):
  Q4: "What does usage look like?" → Periodic check, flag items older than N days with no activity
  Q5: "Minimum useful scope?" → Just a reminder in completion summary, no automation needed

Exit summary:
  Problem: Stale opportunities go unnoticed
  Target user: Project maintainers
  Direction: Staleness detection in completion summaries
  Demand signal: Personal pain point (single user)
  Related: None found

User chose: (B) Save as opportunity draft
→ Created opportunities/06-staleness-detection.md (Impact: Low, Effort: Low)
→ Suggested next step: "run a focused scan around staleness detection"

Completion Summary

Follow the template in ../feature-radar/references/DIRECTIVES.md, with skill name "Scan Complete".

平台分布

Codex

35.69%
按下载量换算36

Claude

32.91%
按下载量换算34

Cursor

19.05%
按下载量换算19

Gemini CLI

10.19%
按下载量换算10

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills