Agent skill

Hotspot Article

by ChanningLua in ChanningLua/prax-agent

从近期大事件、真实需求和常青决策中选题,完成多源研究、业务落地、实测、事实核验和精选文章. An agent skill from ChanningLua/prax-agent.

MITAuto-check: notesDevelopment

Install Hotspot Article

skills CLI
$ npx skills add ChanningLua/prax-agent --skill hotspot-article -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ChanningLua/prax-agent hotspot-article --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ChanningLua/prax-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/prax/skills/hotspot-article .claude/skills/hotspot-article && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hotspot-article
GitHub stars
273
Token cost
~2.7k tokens
SKILL.md length
437 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

从近期大事件、真实需求和常青决策中选题,完成多源研究、业务落地、实测、事实核验和精选文章. An agent skill from ChanningLua/prax-agent.

  • Works in 10 steps: :读取项目配置 → :构建三轨机会池 → :先补业务场景和技术桥,再算机会分 → …
  • Development work in your project
  • SKILL.md covers 触发条件, 文件约定, Step 0:读取项目配置 and Step 1:构建三轨机会池, plus 9 more sections
  • Calls python3

What it does

Hotspot Article is an agent skill from ChanningLua/prax-agent. 从近期大事件、真实需求和常青决策中选题,完成多源研究、业务落地、实测、事实核验和精选文章

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The repository describes itself as: Self-improving agent runtime that learns from experience — test-verify-fix loops, correction detection, cross-project memory, multi-model orchestration. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/hotspot-article”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Glob, Grep, WebSearch, WebCrawler

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. :读取项目配置
  2. :构建三轨机会池
  3. :先补业务场景和技术桥,再算机会分
  4. :研究,不先写正文
  5. :多视角提纲
  6. :必须做一次真实验证
  7. :写初稿
  8. :三次独立审校
  9. :运行硬质量门
  10. :交付

What it can do on your machine

Read from SKILL.md and the folder at commit 19d016b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • WebSearch
    • WebCrawler

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hotspot Article loads about 2.7k tokens when it runs. Until then it costs about 15 tokens; SKILL.md has 437 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~15
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit, Glob, Grep, WebSearch, WebCrawler

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ChanningLua/prax-agent at commit 19d016b, republished under its MIT licence (© ChanningLua). 437 words, ~2,731 tokens.

Download SKILL.mdSave it as .claude/skills/hotspot-article/SKILL.md (or your agent's skills folder).
name
hotspot-article
description
从近期大事件、真实需求和常青决策中选题,完成多源研究、业务落地、实测、事实核验和精选文章
allowed-tools
Bash, Read, Write, Edit, Glob, Grep, WebSearch, WebCrawler
triggers
热点文章, 热点追踪, 业务选题, 用户需求, 深度文章, 长文, 对标精选, hotspot article, longform, editorial pipeline
tags
research, writing, hotspot, demand, fact-check, longform
priority
10

热点深度文章 Pipeline

这不是“一次提示词写长文”,而是一条有证据链和退出门槛的编辑流水线:

三轨机会池 → 需求优先评分 → 技术到业务桥接 → 多源研究 → 多视角提纲 → 实测 → 成稿 → 事实/技术/文风审校 → 质量门

对标腾讯云“内容精选”时,先读 docs/recipes/tencent-selected-benchmark.md,再按内容类型选质量档,不能用一个固定篇幅处理所有文章:

  • selected-analysis:重大事件或行业变化,6,000–12,000 有效字符;
  • selected-framework:架构、组织或决策框架,5,500–11,000 有效字符;
  • selected-hands-on:产品实测或项目实战,4,500–9,000 有效字符。

三类都控制在 5–9 个 H2。重量来自攻击链、分类框架、真实输入输出、失败修复、架构图和明确取舍,不来自拆出更多章节。selected-sharp 只用于技术短稿,不计入腾讯云精选批次;selected 保留为兼容旧稿。

机器分只衡量可审计的下限,不能把 8.6/10 直接解释为审美或洞察得分。最终发布仍需人工主编确认。

触发条件

  • “追一下今天的 AI 热点,写成深度文章”
  • “从客户项目和业务难题里找一个值得写的选题”
  • “对标精选文章,做一篇 8–9 分的长文”
  • “运行 hotspot-article”

如果用户没有给主题,同时扫描近期大事件、真实需求和常青决策三条线。默认周内容组合为:时事 30%、需求/解决方案 50%、常青框架 20%。选题先补齐业务场景,再按当前组合缺口选择;热度很高但说不清“谁在什么情况下要做什么决定”的内容,只进简报,不写精选文章。

把选题与评分写入 brief.md,不中断执行等待确认;如果涉及政治、医疗、金融、法律或未成年人等高风险主题,则停在选题报告,等待用户确认。

文件约定

ROOT=.prax/vault/hotspot-articles/<YYYY-MM-DD>/<topic-slug>

$ROOT/
├── brief.md
├── topic-candidates.json
├── opportunity-report.json
├── business-bridge.json
├── demand-brief.md
├── research-notes.md
├── source-index.json
├── outline.md
├── evidence.json
├── evidence/
├── draft.md
├── fact-check.json
├── fact-check.md
├── editorial-review.md
├── quality-report.json
└── publish-ready.md       # 只有全部门槛通过后才创建

新文件使用 Write;修改已有文件使用 Edit。保留中间稿和失败报告,不覆盖研究证据。

Step 0:读取项目配置

可选配置 .prax/article.yaml:

yaml
audience: 中文开发者与技术决策者
topics: [AI Agent, LLM, RAG, 开源工具]
exclude: [纯融资传闻, 无原始出处的爆料]
lookback_hours: 48
candidate_limit: 20
tone: 克制、清晰、证据优先
target_mix:
  event: 0.30
  demand: 0.50
  evergreen: 0.20

没有配置就使用以上默认值。当前日期和时区必须写入 brief.md。

Step 1:构建三轨机会池

每个候选标注一个 track:

A. 时事线 event
  • 当天 .prax/vault/ai-news-hub/<DATE>/;
  • 先运行 python3 -m prax.article_integrations probe,把可用和缺失的适配器写入 brief.md;
  • TRENDRADAR_OUTPUT 存在时,用 python3 -m prax.article_integrations ingest-trendradar 归一化其 Markdown、JSON 或 SQLite 输出;
  • AutoCLI 可用时,通过 collect-autocli 抓登录态平台;扩展或登录态失败只跳过该源;
  • 官方博客、GitHub Trending、Hacker News、行业媒体;
  • 正文优先使用 Crawl4AI;未安装时适配器会遵守 robots.txt 并退回公开 HTTP 抓取。
B. 需求线 demand
  • 客户项目 brief、售前/客服问题和交付复盘(先脱敏);
  • 搜索词、站内搜索无结果、相关问题与持续出现的长尾查询;
  • GitHub Issues/Discussions、技术社区中反复出现的“怎么做/怎么选/为什么失败”;
  • 招聘 JD、岗位能力变化、培训与管理难题;
  • 产品使用数据、试用流失、工单和销售异议;
  • 文章评论中请求方案、补充案例或表达自身痛点的内容。
C. 常青线 evergreen
  • 架构取舍、成本模型、迁移指南、排障手册;
  • 每季度仍会遇到的采购、合规、团队协作和人才培养决策;
  • 旧文章中持续带来搜索、收藏、咨询和内部转发的问题。

每个候选保存标题、来源、时间、平台指标和抓取时间。平台不可比的点赞、排名不得直接相加。单张截图或发布一小时内的高评论率只能触发进一步研究,不能直接证明“热门”或“真实需求”;尽量保存 1h / 6h / 24h 快照,并把评论分成方案询问、真实案例、反驳讨论和普通互动。

Step 2:先补业务场景和技术桥,再算机会分

每个候选必须回答:

字段要回答的问题
actor谁正在遇到问题
job_to_be_done他要完成什么任务
trigger为什么现在必须处理
current_workaround当前怎么凑合解决
decision读完文章要做什么选择
cost_of_inaction不处理有什么损失
evidence客户 brief、工单、搜索、评论、Issue 等需求证据

缺少两个以上字段的候选不能进入精选长文。不要编造客户、预算、工单或业务结果。

再建立一条不能跳步的技术到业务链:

来源信号 → 技术变化 → 受影响流程 → 业务决策 → 验证方法 → 成功指标 → 不适用条件

字段合格标准
source_signal写清事件、客户问题或长期信号,并保留原始出处
technical_delta说明能力、约束或架构相对之前具体变了什么,不写“效率提升”一类空话
affected_workflow落到售前、研发、客服、运营、招聘、审计等具体流程节点
business_decision决策人读完后要采用、延期、自建、采购、替换或停止什么
validation_method给出可重复的对照、试点、回放、压测或失败注入方法
success_metric使用业务方约定的质量、时延、成本、风险或人效指标
no_fit_conditions明确哪些组织、数据或流程条件下不值得采用

七项必须全部存在。事件再热,只要还停留在产品功能介绍、发布会复述或技术名词解释,就把 editorial_route 设为 daily-digest,不能进入精选长文。需求或常青题缺桥时设为 research-brief,先补现场证据。

再过一道“题目重量门”。至少满足以下三项中的两项:

  • 会改变预算、权限、架构、岗位或跨团队流程中的一项;
  • 能用一个真实或明确标注的参考业务流程,从触发走到验收和失败处置;
  • 决策错误会带来可说明的交付、成本、安全或组织后果。

单个协议字段、局部版本变化、某个仓库去掉一层组件、厂商小功能更新,默认只做技术短帖。除非能证明它已经造成大范围迁移、真实客户损失或重大的采购决策,否则不得靠扩写升格为精选文章。

撞题门

在目标社区检查最近 30 天的精选内容。为每个候选记录:

  • 已有文章是否覆盖同一事件或需求;
  • 中心结论和业务落点是否相同;
  • 本文新增的是原创运行证据、未被注意的原始材料、真实客户现场,还是只换了一种说法。

同题已有更完整文章时,候选必须降级或换题。不能因为已经做完研究就继续发布。

把候选写入 topic-candidates.json,信号均按 0–5 分:

json
{
  "portfolio_counts": {"event": 3, "demand": 4, "evergreen": 1},
  "target_mix": {"event": 0.3, "demand": 0.5, "evergreen": 0.2},
  "candidates": [
    {
      "id": "ai-search-build-or-buy",
      "title": "AI 联网搜索应该自建还是采购",
      "track": "demand",
      "signals": {
        "problem_frequency": 4,
        "decision_urgency": 5,
        "scenario_specificity": 5,
        "evidence_strength": 4,
        "audience_fit": 5,
        "attention_velocity": 3,
        "cross_source": 3,
        "depth": 5,
        "content_gap": 4,
        "risk": 1,
        "saturation": 3
      },
      "business_case": {
        "actor": "正在做企业搜索的技术负责人",
        "job_to_be_done": "给客户确定可交付的联网搜索方案",
        "trigger": "项目进入技术选型",
        "current_workaround": "逐个对比搜索 API 和开源框架",
        "decision": "自建、采购或混合",
        "cost_of_inaction": "错过交付期或持续产生错误答案",
        "evidence": ["脱敏客户 brief", "社区重复问题"]
      },
      "business_bridge": {
        "source_signal": "客户项目进入 AI 联网搜索选型",
        "technical_delta": "实时检索增加了新鲜度、引用、延迟和成本约束",
        "affected_workflow": "售前调研、方案架构、上线验收和质量回归",
        "business_decision": "采购搜索 API、开源自建或采用混合架构",
        "validation_method": "用固定查询集比较覆盖率、引用正确性、延迟和成本",
        "success_metric": "达到客户约定的质量、时延和预算阈值",
        "no_fit_conditions": "固定语料且不需要实时信息时不引入联网检索"
      }
    }
  ]
}

运行确定性评分器:

bash
python3 -m prax.topic_opportunity \
  "$ROOT/topic-candidates.json" \
  --json-out "$ROOT/opportunity-report.json" \
  --bridge-out "$ROOT/business-bridge.json"

机会分由 真实需求 45%、注意力 20%、编辑价值 35% 组成,再扣传闻风险和内容饱和度。候选还必须满足:总分至少 65、证据强度至少 3、风险不高于 2、业务场景完整度至少 0.75、技术到业务桥完整度为 1.00。

评分器优先补齐周内容组合缺口。没有历史时先从 demand 线选择;需求线没有合格项才回退到其他轨道。评分结果同时给出 longform、daily-digest、research-brief 或 hold 路由。把入选主题扩写为 demand-brief.md,并让评分器生成 business-bridge.json;后者只复制候选中已经写明的链路,不替作者补造业务事实。纯新闻只能解释“发生了什么”时,进入日报而不是精选长文。

Step 3:研究,不先写正文

先验证需求本身。demand 轨道至少要有两类独立信号,例如“脱敏客户 brief + 重复搜索问题”或“工单 + GitHub Issues”;早期互动数字只能算其中半类。无法补足时,把文章降级为探索稿,不创建 publish-ready.md。

再逐段验证 business-bridge.json:

  • source_signal 用原始材料确认,不拿转载标题代替;
  • technical_delta 用文档、代码、论文或可复现结果说明“变了什么”;
  • affected_workflow 用客户流程、工单、访谈或公开案例确认影响位置;
  • business_decision 至少比较两个真实可选方案,不能预设产品必买;
  • validation_method 和 success_metric 必须能在 Step 5 实际执行或观测;
  • no_fit_conditions 要保留,即使它会缩小文章适用范围。

可选运行 GPT Researcher:

bash
python3 -m prax.article_integrations research \
  "<主题 + 业务决策 + 六个研究问题>" \
  --out "$ROOT/research-leads.md"

它的输出只算研究线索。每个 URL 仍要单独读取并进入 source-index.json,不能把聚合报告自身拆成多个独立来源。STORM 已有输出可通过 STORM_OUTPUT 作为提纲和追问参考导入,仍不得跳过本文的来源、实测和事实门。

来源数量按路线确定:selected-analysis 研究至少 12 条,selected-framework 至少 8 条,selected-hands-on 至少 4 条。实测路线不能用堆来源代替真实操作;分析路线也不能用一次配置检查代替技术纵深。优先顺序:

  1. 官方公告、产品文档、标准、代码仓库;
  2. 原始论文、数据集、基准报告;
  3. 有署名和编辑流程的专业媒体;
  4. 专家分析与社区讨论,仅用于观点或线索。

同一新闻的转载不算独立来源。每条来源必须读到支持主张的具体段落;只看搜索摘要不计入研究数。

source-index.json 格式:

json
{
  "sources": [
    {
      "id": "S01",
      "title": "来源标题",
      "url": "https://example.com/original",
      "source_type": "official",
      "published_at": "2026-07-29",
      "accessed_at": "2026-07-29T16:00:00+08:00",
      "supports": ["关键主张 A"],
      "notes": "原文证据的准确释义"
    }
  ]
}

正文引用统一写作 [S01]。精确数字、日期、性能结论和直接归因必须紧邻引用。无法核验的精确数字应删除或明确标成估计。

Show full SKILL.md (170 more words)Show less

Step 4:多视角提纲

先读 demand-brief.md 和 business-bridge.json。开头 300 字内要让读者看到具体角色、触发场景和待做决策;正文至少给出一张决策表、一个真实工作流或案例,以及不适用条件。时事只占背景所需篇幅,主线按“技术变化如何影响流程—有哪些选择—怎样验证”展开。

在 outline.md 中让四个角色分别提出问题,再删去与核心决策无关的问题,合并为一条叙事主线:

  • 工程师:原理、实现路径、性能和复现条件;
  • 产品负责人:用户场景、收益、采用成本;
  • 安全/合规审稿人:攻击面、隐私、失败条件;
  • 怀疑者:反例、替代解释、营销话术。

所有路线的 H2 控制在 5–9 个。相邻内容能在同一节回答就不拆节,但每个 H2 必须新增事实、机制、案例、产物或决策中的至少一项。

  • selected-analysis:现场 → 完整事实链 → 被忽略的技术细节 → 机制解释 → 企业影响 → 可执行方案 → 边界;
  • selected-framework:真实问题 → 分类轴 → 分类矩阵 → 逐类决策 → 跨类场景 → 反例与边界;
  • selected-hands-on:为什么测试 → 真实输入 → 运行过程 → 失败与修复 → 最终产物 → 瑕疵 → 适用人群。

Step 5:必须做一次真实验证

验证必须对应 business-bridge.json 中的 validation_method 和 success_metric,不能测了一个方便运行但与业务决策无关的指标。selected-hands-on 至少完成两个实际运行或案例;其他路线至少一次。验证可以是:

  • 运行开源项目的最小复现;
  • 调用公开 API 并保存响应;
  • 对公开数据做可重复的计算;
  • 对多个官方版本/参数做结构化对照。

不要编造终端结果。无法运行时,把原因写入 evidence.json 并停止在草稿状态,不能生成 publish-ready.md。

evidence.json 格式:

json
{
  "runs": [
    {
      "name": "最小复现",
      "method": "完整、可重复的方法",
      "command": "实际执行的命令(若适用)",
      "result": "观察到的结果和误差",
      "limitations": "这次验证没有覆盖什么,结论不能外推到哪里",
      "artifact": "evidence/run-01.txt",
      "status": "passed"
    }
  ]
}

Step 6:写初稿

draft.md 必须满足:

  • 使用已经写入 brief.md 的路线和对应篇幅,不靠长引用、代码和参考列表注水;
  • 5–9 个 H2,且每节有明确的信息增量;
  • 开头让读者看见一个现场、冲突、真实测试动机或生产问题,不写背景综述;
  • 只保留一个中心判断,用完整事实链、分类框架或真实项目把它撑住;
  • selected-analysis 和 selected-framework 至少两张有效图表;selected-hands-on 至少四张真实截图、对照、输出或图表;
  • 文中必须看得见作者做过什么:读出的原文细节、真实输入输出、失败修复、自己建立的模型或明确取舍;
  • 明确区分事实、观察、推断和作者判断;
  • 逐段使用 [Sxx] 引用,不使用“据报道”代替来源;
  • 禁止“颠覆性、史诗级、秒杀、彻底改变”等无证据宣传语。
去 AI 腔编辑

初稿完成后单独做一轮“人味编辑”,不增加新事实,只改表达:

  • 删除每节开头的套话和末尾重复总结,段落直接从事实、场景或判断进入;
  • 不机械统计某一种句式;重点删除没有作者观察、对象和后果的模板段落;
  • 不为凑排比强行写三点、四层、五个关键;只有真实顺序或互斥分类才编号;
  • 标题使用自然判断或读者问题,不把所有标题写成“名词:解释”的同一格式;
  • 长短句和段落长度要有变化,连续三个段落不能使用相同句式开头;
  • 把“赋能、闭环、底层逻辑、生态、全面升级”等抽象词换成具体的人、动作、对象和后果;
  • 允许作者做有证据的取舍和判断,不写四平八稳的“两边都有道理”;
  • 大声读一遍;删掉读起来像演讲提纲、咨询报告或产品发布稿的句子。

Step 7:三次独立审校

审校时不要沿用写作者的自我评价。

7.1 事实审校

抽取至少 8 个关键主张写入 fact-check.json:

json
{
  "claims": [
    {
      "id": "C01",
      "claim": "正文中的可核验主张",
      "source_ids": ["S01", "S03"],
      "status": "verified",
      "included_in_article": true,
      "caveat": ""
    }
  ]
}

verified/supported/qualified 为可接受状态。pending/unverified 主张若仍在正文,质量门失败。同步生成便于人工阅读的 fact-check.md。

7.2 技术审校

检查命令、版本、参数、因果关系、基准条件和术语。把“相关”误写成“因果”、把单次结果写成普遍结论,均需退回修改。

7.3 编辑审校

在 editorial-review.md 对 7 项各打 0–5 分并给证据:信息增量、推理深度、结构、清晰度、原创综合、读者价值、作者感与自然度。任何一项低于 4,修改一次;最多两轮,仍不达标就保留草稿并报告原因。

Step 8:运行硬质量门

在安装 Prax 的环境中运行:

bash
python3 -m prax.content_quality \
  "$ROOT/draft.md" \
  --business-bridge "$ROOT/business-bridge.json" \
  --sources "$ROOT/source-index.json" \
  --fact-check "$ROOT/fact-check.json" \
  --evidence "$ROOT/evidence.json" \
  --profile "<selected-analysis|selected-framework|selected-hands-on>" \
  --json-out "$ROOT/quality-report.json"

命令中的 profile 必须与 brief.md 一致。selected-sharp 通过也不能生成腾讯云精选批次的 publish-ready.md。

质量门只按正文实际引用的来源计算域名多样性;只放进研究池、没有进入正文的来源不能抬分。每次证据运行还必须写明 limitations,缺少外推边界时按未完成处理。

只有命令退出码为 0、所有 hard gates 通过、机器分至少 80/100,并且编辑审校七项都至少 4/5,才能用 Read + Write 生成 publish-ready.md。最多修订两轮,不得通过重复段落或虚构来源冲分。

Step 9:交付

向用户报告:

  • 选题、热点分及选择理由;
  • 技术到业务链、内容路由以及仍缺的桥接字段;
  • 研究来源数、正文实际引用数、第一方引用数;
  • 实测方法和证据路径;
  • 机器可审计分、编辑七项分;
  • publish-ready.md 或未通过时的 draft.md 路径;
  • 仍然存在的局限。

安全边界

  • 不自动发布到网站、公众号或社交平台;
  • 不绕过登录、付费墙、robots 或站点限制;
  • 不把社区评论当作事实来源;
  • 不伪造浏览、采访、运行结果或引用;
  • 不为了达到篇幅门槛重复表达;
  • 高风险领域必须有人类主编确认后才能进入发布态。

© ChanningLua, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in src/prax/skills/hotspot-article of ChanningLua/prax-agent.

Open the folder on GitHubat commit 19d016b

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Hotspot Article next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Hotspot Article

What does Hotspot Article do?

从近期大事件、真实需求和常青决策中选题,完成多源研究、业务落地、实测、事实核验和精选文章. An agent skill from ChanningLua/prax-agent. Hotspot Article is an agent skill from ChanningLua/prax-agent.

When should I use Hotspot Article?

Hotspot Article fits situations like: development work in your project.

How do I install Hotspot Article in Claude Code?

Run `npx skills add ChanningLua/prax-agent --skill hotspot-article -a claude-code`. Or copy the skill folder (src/prax/skills/hotspot-article in ChanningLua/prax-agent) into .claude/skills/hotspot-article in your project. Claude Code loads it when a task matches its description.

How do I install Hotspot Article in Codex?

Run `npx skills add ChanningLua/prax-agent --skill hotspot-article -a codex`. Or copy the skill folder (src/prax/skills/hotspot-article in ChanningLua/prax-agent) into .agents/skills/hotspot-article in your project. Codex loads it when a task matches its description.

Can I use Hotspot Article in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ChanningLua/prax-agent --skill hotspot-article -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hotspot-article, .gemini/skills/hotspot-article, .github/skills/hotspot-article and .opencode/skills/hotspot-article in your project.

What does Hotspot Article need to run?

Going by SKILL.md and its folder, Hotspot Article needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Glob, Grep, WebSearch, WebCrawler.

Does Hotspot Article access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Hotspot Article safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Hotspot Article use?

Hotspot Article is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hotspot Article use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Hotspot Article?

Skills that share tags, products or a category with Hotspot Article: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hotspot Article?

ChanningLua (a GitHub user) maintains it in ChanningLua/prax-agent, which has 273 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 11, 2026.

Source: ChanningLua/prax-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.