cs-search-skill

cs-search-skill is a skill for Codex from ChenShuo2004/cs-skills. It costs 101 tokens per session (2,140 once invoked), scanned A, original, MIT.

A research workflow for investigating a product, company, technology, market, or competitor group. It produces a source-backed decision brief, usually comparing several relevant alternatives.

In plain words
What is it for?
Use it to research history, current capabilities, users, pricing, competitors, market conditions, user feedback, business models, risks, and next steps.
Why use it?
It turns a vague research request into a defined question, gathers current evidence from multiple sources, separates facts from inferences, and preserves traceable support for important conclusions.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex; $skill-name invocation.

Good fit Use it to research history, current capabilities, users, pricing, competitors, market conditions, user feedback, business models, risks, and next steps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chenshuo2004/cs-skills/cs-search-skill
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ChenShuo2004/cs-skills --skill cs-search-skill
Clone the repo
git clone --depth 1 https://github.com/ChenShuo2004/cs-skills

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for cs-search-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-search-skill/github.svg)](https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-search-skill)
Your own site
<a href="https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-search-skill"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-search-skill/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for cs-search-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/chenshuo2004/cs-skills/cs-search-skill"><img src="https://agentmods.dev/badge/skills/chenshuo2004/cs-skills/cs-search-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 101 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,140 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00101 $0.02140
Opus 5 $0.00051 $0.01070
Sonnet 5 $0.00020 $0.00428
Haiku 4.5 $0.00010 $0.00214

Measured 10d ago against content hash 98a75329e333, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

cs-search-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

cs-search-skill/SKILL.md · 200 lines

How it starts

The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Search Skill

Overview

把一个模糊的调研请求变成可以支持决策的研究简报。默认围绕一个目标对象和 3-5 个代表性竞品,完成纵向发展史、横向现状、用户声音、商业模式与战略判断, 并为关键结论保留可追溯来源。

默认直接在对话中输出中文决策简报;只有用户明确要求时,才把完整结果写入 research.md 或其他文件。

适用范围与边界

适合以下任务:

  • 研究一个产品、公司、技术、市场、商业模式或新概念。
  • 分析目标对象与直接竞品、替代方案或潜在竞争者。
  • 为“是否进入、是否购买、是否跟进、如何定位、下一步做什么”等问题提供依据。
  • 需要当前信息、历史演进、用户反馈和多来源交叉验证的深度研究。

不要用于:

  • 只需要一个简单定义、一个稳定事实或一次快速查找的问题。
  • 纯粹写文章、改稿或包装研究结论;这类任务交给 $cs-writer
  • 没有任何公开证据却要求确认私有数据、内幕消息或确定性预测的任务。

简单事实问题应直接回答,不要为了触发完整流程而制造长报告。

输入契约

开始前识别以下信息:

  • 研究对象:产品、公司、技术、市场或概念的明确名称及别名。
  • 决策问题:用户希望研究后做什么决定,或需要验证哪一个判断。
  • 范围:地区、时间点、行业边界、用户群和重点维度。
  • 竞品名单:用户提供的名单优先;没有名单时自动发现 3-5 个代表性对象。
  • 已有材料:链接、文档、截图、数据、用户观察或内部假设。
  • 深度要求:默认输出决策简报;用户要求时扩展为完整研究报告。
  • 交付格式:直接输出文字,或生成 PDF 报告。

只在以下情况提问:研究对象存在多个同名实体、决策问题完全缺失且会改变研究方向, 或研究范围过大到无法在当前任务中完成。其余信息采用合理默认,并在结果中写明。

工作流

1. 定义研究问题

先用一句话复述研究目标,并形成内部研究卡:

目标对象:
决策问题:
研究范围:
竞品集合:
关键维度:
交付形式:中文决策简报

如果用户没有指定竞品,先判断直接竞品、替代方案和潜在进入者,再选出 3-5 个 最有代表性的对象,并说明选择理由。不要为了凑数量加入无关产品。

2. 建立研究地图

根据对象和决策问题选择必要维度,不机械填满清单。通常覆盖:

  • 纵向脉络:起源、创始团队或提出者、关键版本、转型、融资/收购、重大争议和当前状态。
  • 当前产品:核心能力、目标用户、使用场景、定价、分发渠道、生态和限制。
  • 横向竞品:定位、核心差异、产品体验、用户口碑、商业模式和竞争位置。
  • 市场环境:需求变化、技术或政策约束、替代方案、进入壁垒和潜在变化。
  • 战略判断:优势来源、短板、可持续性、机会、风险和对用户决策的影响。

3. 收集与记录证据

使用可用的网络搜索、页面打开和内容提取能力。先搜索发现线索,再打开原始页面核验, 不要只依赖搜索摘要。针对同一问题组合多个关键词和角度,必要时分别搜索目标对象、 竞品、用户评价和行业背景。

来源优先级:

  • 一级来源:官方文档、产品页面、定价页、发布公告、公司披露、监管/法院文件、原始论文、 GitHub 仓库和当事人原始访谈。
  • 二级来源:可信媒体的原创报道、行业报告、专业测评和有方法说明的调查。
  • 三级来源:论坛、评论、社交媒体和聚合内容,只用于了解用户声音或发现线索,不单独支撑 关键商业判断。

对每条重要结论在内部保留证据记录:

claim:结论或事实
source:来源标题 + URL
published/updated:来源发布日期或更新时间
accessed:访问日期
tier:一级 / 二级 / 三级
confidence:高 / 中 / 低
notes:支持范围、冲突信息或限制

关键数字、当前状态、价格、融资、用户规模和产品能力必须优先使用最新来源; 在可行时用两个相互独立的来源交叉验证。多篇转载同一原始报道不算独立验证。

4. 分析与综合

先整理证据,再提出判断:

  • 事实:来源直接支持的内容,紧邻结论放置来源链接。
  • 推断:基于多个事实的分析,明确使用“推断”“更可能”“我的判断”等措辞。
  • 未知:公开资料无法确认的内容,写明“暂未找到可靠证据”,不要补写确定答案。

Read the full file on GitHub · 200 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 200 lines · 101 tokens per session scan A 98a75329e333

Subscribe to this mod's changes

cs-search-skill is a skill published in the GitHub repository ChenShuo2004/cs-skills (142 stars, last pushed 7d ago), licensed MIT. It adds 101 tokens to every session and 2,140 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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