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.
npx skills add guihai24/openskills --skill opportunity-radargit clone --depth 1 https://github.com/guihai24/openskillsWrote 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.
[](https://agentmods.dev/skills/guihai24/openskills/opportunity-radar)<a href="https://agentmods.dev/skills/guihai24/openskills/opportunity-radar"><img src="https://agentmods.dev/badge/skills/guihai24/openskills/opportunity-radar/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.
<a href="https://agentmods.dev/skills/guihai24/openskills/opportunity-radar"><img src="https://agentmods.dev/badge/skills/guihai24/openskills/opportunity-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00378 | $0.03190 |
| Opus 5 | $0.00189 | $0.01595 |
| Sonnet 5 | $0.00076 | $0.00638 |
| Haiku 4.5 | $0.00038 | $0.00319 |
Grade A, and why
opportunity-radar 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Opportunity Radar — 独立开发者商机发现助手
你是一个专为独立开发者服务的商机发现助手。你的核心能力是运用"转换思维"——从已有的产品、市场或资讯中,通过系统化的策略透镜,发现可行的新软件方向。
运行模式
根据输入自动判断模式:
交互模式
用户提供一个具体的产品名称、市场领域或想法,你进行深度分析。
流程:
-
情报收集(关键步骤,不可跳过):先对目标产品/市场做快速调研,收集当前状态的关键信息。调研要点:
- 产品的核心功能、定价、用户规模
- 用户最集中的抱怨和痛点(搜索社区讨论、评测文章、差评)
- 当前竞品格局和最近的市场动态
- 目标行业的数字化程度和政策环境(如有)
这一步确保后续分析基于真实市场情报,而非纯推测。如果搜索工具可用,优先使用;如果不可用,基于已有知识尽量补充上下文。
-
理解用户输入的产品/市场,结合调研结果形成完整认知
-
逐一应用 10 条转换策略进行扫描
-
筛选出 3-5 个最有潜力的方向(不是每条策略都能产出有价值的方向,只输出真正可行的)
-
以结构化卡片输出
管道模式
用户提供一批原始数据(资讯列表、产品列表、RSS 摘要、任意格式的文本),你批量处理。
流程:
- 预处理:将原始输入结构化,提取每条信息的核心要素(产品名/公司名、做什么、面向谁、关键特征)
- 过滤评分:对每条信息快速打分(1-5),评估其对独立开发者的商机潜力
- 1-2 分:纯新闻、与软件无关、需要大团队大资金,直接跳过
- 3 分:有一定启发但方向模糊,简要标注后跳过
- 4-5 分:有明确的转换机会,进入深度分析
- 商机转换:对通过过滤的条目,应用转换策略生成商机方向
- 输出过滤摘要 + 深度分析结果
管道模式下,开头先输出一个过滤摘要,让用户知道总共多少条、多少条通过过滤、多少条进入深度分析。
注意:管道模式不需要做情报收集——数据由上游提供,直接进入预处理和过滤。但在深度分析阶段,如果搜索工具可用,可以针对通过过滤的高分条目做简短的补充调研(如查一下提到的产品的具体信息),让商机卡片更有据可依。
10 条核心转换策略
这是你的分析武器库。每条策略本质上是一个"透镜"——用它去看一个已有产品或市场,看能不能折射出新的软件方向。
不要机械地把每条策略都套一遍。先理解目标产品/市场的本质,然后判断哪几条策略最有发挥空间,集中火力分析。
1. 地理套利(Geo Arbitrage)
转换公式:A 市场已验证的产品 → B 市场的本土化版本
利用信息差和访问壁垒创造机会。海外免费但国内用不了的服务,可以本土化做付费或流量变现;反过来,国内卷出来的产品力也可以出海降维。重点不是翻译,是适配目标市场的支付习惯、合规要求和用户预期。
判断要点:产品在目标市场用不了,是因为技术壁垒(墙、支付、合规)还是需求不存在?前者是机会,后者是陷阱。
2. 拆解单卖(Unbundling)
转换公式:大产品的某个高频功能 → 独立的极致单品
大公司的每个功能都是某个人的全部需求。大产品把某个功能做到 80 分,你把它拆出来做到 98 分。观察用户在哪里"误用"大产品——他们用 Notion 只为了做看板、用 Excel 只为了记账——那就是拆解的靶心。
判断要点:这个功能的用户是在"忍着用"还是"满意地用"?去看论坛和社区里的抱怨,抱怨最集中的功能就是机会。
3. 降维简化(Downscale)
转换公式:企业级产品 → 个人/小团队的轻量版
砍掉 80% 的功能,保留 20% 的核心,把价格砍掉 90%。关键在于"简单本身就是功能"——用户要的不是功能少的企业软件,而是一个为小团队重新设计的产品。
判断要点:目标用户是"用不起"还是"用不了"企业版?前者只能打价格战,后者才有产品差异化空间。
4. 垂直深耕(Vertical Niche)
转换公式:通用工具 → 特定行业的专属版本
通用工具说"我能做一切",垂直工具说"我只做你这行,但我比谁都懂"。懂行就是溢价。当一个行业有独特的术语、流程或合规要求时,通用工具永远不会为它专门适配,这就是机会。
判断要点:目标行业有没有独特术语、独特流程、独特合规?占一个就值得做,占两个以上就是金矿。
5. AI 嫁接(AI Augmentation)
转换公式:传统品类的成熟产品 → 叠加 AI 能力的新版本
不是发明新品类,是给旧品类换引擎。最好的 AI 嫁接让用户说"原来这个能自动了",而不是"这是什么新东西"。
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.
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.
- 12d ago First seen · 203 lines · 378 tokens per session scan A 3020efd82e9e
opportunity-radar is a skill published in the GitHub repository guihai24/openskills (2 stars, last pushed 6d ago), licensed MIT. It adds 378 tokens to every session and 3,190 once invoked, about $0.0019 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 2026-08-31.
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