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 PANGKAIFENG/ai-product-manager-skills --skill competitive-analysisgit clone --depth 1 https://github.com/PANGKAIFENG/ai-product-manager-skillsWrote 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/pangkaifeng/ai-product-manager-skills/competitive-analysis)<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/competitive-analysis"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/competitive-analysis/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/pangkaifeng/ai-product-manager-skills/competitive-analysis"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/competitive-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00150 | $0.02225 |
| Opus 5 | $0.00075 | $0.01112 |
| Sonnet 5 | $0.00030 | $0.00445 |
| Haiku 4.5 | $0.00015 | $0.00222 |
Grade A, and why
competitive-analysis 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
竞品决策分析 Skill（competitive-analysis）
中文速查
- 中文名：竞品决策分析 / 竞品决策简报
- 英文稳定名：
competitive-analysis - 分类：决策调研 / 产品研究
- 你可以这样叫我：
帮我做竞品分析、研究下这个新产品对我们有什么启发、打开这个产品看看对产品决策有什么用、分析 Krowork 这类产品、把竞品信息转成 PRD 输入 - 适合：围绕一个产品决策，把竞品、替代方案、市场信号和可选产品走查转成路线图、定位、定价、功能优先级、差异化或 Go/No-Go 输入。
- 不适合：问题还没定义清楚（先用
ai-collaboration-calibration）；只想建立长期主题认知或候选池（用research-topic-compiler）；已经只有 A/B/C 最终选择（用decision-research）；只要 UI 高保真参考（用ui-mockup-desktop-workbench）。
核心原则
竞品分析服务产品决策，不服务信息完整性。
不要把“打开网站、登录、点完功能”当作目标。产品走查只是证据渠道之一。真正要回答的是：
- 我们该学什么？
- 我们该避开什么？
- 我们需要验证什么？
- 这会改变我们的产品路线、定位、定价或优先级吗？
如果用户只给了一个竞品 URL，先把它改写成决策问题，再决定是否需要浏览器、截图、登录态或外部评论渠道。
启动协议
先判断用户的请求处在哪一层：
| 层级 | 用户表述 | 处理方式 |
|---|---|---|
| 取证层 | “打开这个网址点一下所有功能” | 追问或推断它服务什么产品决策；走查只是可选证据渠道。 |
| 产品层 | “这个产品对我们有什么启发” | 进入本 Skill，输出 Product Decision Brief。 |
| 决策层 | “我们要不要做这个方向 / 学它的定价 / 改路线图” | 进入本 Skill；若只剩最终选择，交给 decision-research。 |
| 研究层 | “系统研究这个赛道，先沉淀一批竞品” | 交给 research-topic-compiler 的 Product Candidate Research。 |
最多问 3 个启动问题；能从用户上下文、URL、仓库文档或已有 PRD 推断的，不要打断：
- 这次竞品分析要影响哪个产品决策？
- 我方产品、目标用户、当前阶段是什么？
- 是否允许使用登录态、截图、浏览器自动化或用户账号？如果没有明确授权，只用公开信息。
工作流
-
决策锚定
- 把请求转成一句
decision_question。 - 写清
current_product_context、decision_owner、time_budget和decision_deadline，未知就标为假设。 - 如果没有决策问题，先用
ai-collaboration-calibration校准，不直接开始搜索。
- 把请求转成一句
-
竞品边界定义
- 区分 direct competitor、workflow alternative、status quo、adjacent inspiration。
- 如果用户只给一个产品，补充“它代表哪类替代方案”的推断。
- 如果目标是候选池，转
research-topic-compiler；本 Skill 可消费候选池做决策简报。
-
证据渠道选择
- 读取
references/evidence-channel-guide.md。 - 不要默认所有渠道全开；按决策问题选择 3-6 个高价值渠道。
- 将产品走查、浏览器截图、OAuth 登录和 Computer Use 视为
Product Walkthrough Evidence，按references/browser-walkthrough-boundaries.md执行。
- 读取
-
证据收集与分级
- 官方页面、定价页、文档、changelog、案例、招聘、评论、社区、用户访谈和走查证据分开记录。
- 每条证据标注：source、date/accessed_at、evidence_level、supports、contradicts、decision_implication。
- 对动态信息、价格、当前功能、登录流程和评论，必须实时验证并给来源链接或截图路径。
-
从外部观察转成内部判断
- 不输出“竞品有这些功能”就结束。
- 把观察翻译成我方 taxonomy：用户任务、激活路径、付费触发、协作模型、信息架构、AI 能力边界、信任机制、增长入口、运营负担。
- 对每个可借鉴点写清：照抄会错在哪里、需要适配的我方上下文、最小验证动作。
What ships with it
6 files 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 · 146 lines · 150 tokens per session scan A 0405969ced95
competitive-analysis is a skill published in the GitHub repository PANGKAIFENG/ai-product-manager-skills (11 stars, last pushed 13d ago), licensed MIT. It adds 150 tokens to every session and 2,225 once invoked, about $0.0007 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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