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 konglong87/superPM --skill pm-geogit clone --depth 1 https://github.com/konglong87/superPMWrote 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/konglong87/superpm/pm-geo)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-geo"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-geo.svg" alt="Measured on agentmods" 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 | $0.00084 | $0.02830 |
| Opus 5 | $0.00042 | $0.01415 |
| Sonnet 5 | $0.00017 | $0.00566 |
| Haiku 4.5 | $0.00008 | $0.00283 |
Grade A, and why
pm-geo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (run first)
bash "$(dirname "${BASH_SOURCE[0]}")/../../check-update.sh" 2>/dev/null || true
# 创建增长迭代目录
mkdir -p docs/03-增长迭代
echo "📡 GEO / AI 搜索优化工具"
# 检查前置文档
if [ -f "docs/02-方案设计/产品定位方案.md" ]; then
echo "✅ 产品定位方案 - 已找到"
else
echo "⏳ 产品定位方案 - 未找到(可选,缺失时将由本技能快速采集)"
fi
if [ -f "docs/01-需求调研/市场调研报告.md" ]; then
echo "✅ 市场调研报告 - 已找到"
else
echo "⏳ 市场调研报告 - 未找到(可选)"
fi
跨 Agent 交互规则
当流程要求与用户交互时:
- 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
- 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
- 一次只问一个问题。
- 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
- 不得在用户回答前生成文档、写入 docs。
- 已有 docs 文件不能替代本轮用户回答。
适用场景
- 用户说"让 AI 推荐我们的产品""GEO 优化""AI 搜索里搜不到我们""生成式引擎优化""提升在 ChatGPT/Perplexity 的曝光"
- 产品已上线或已有官网/内容资产,希望被 AI 搜索引擎在回答中准确提及与推荐
- 与
pm-growth(转化增长)区分:geo 管"被 AI 发现与推荐",growth 管"看到之后的转化"
执行流程
步骤 1: 明确 GEO 目标查询(主 agent - 用户交互)
使用 AskUserQuestion 询问:
🎯 GEO 优化目标
你希望用户在 AI 搜索里用哪类问题能搜到/被推荐你们?
A) 品类/场景问题(如"有什么好用的XX工具") B) 对比/选型问题(如"XX 和 YY 哪个好") C) 解决方案问题(如"怎么解决 XX 痛点") D) 品牌/官网直接召回(搜品牌名能出现官网与简介) E) 全部覆盖(推荐,但耗时较长)
💡 提示:先聚焦 1-2 个高价值查询场景,比铺开更有效。
记录到变量 GEO_TARGET。
步骤 2: 现状可见性诊断(主 agent + subagent)
2.1 采集产品基础信息
如果有产品定位方案 / 市场调研报告,读取并提取:产品名称、核心品类、目标用户、差异化卖点、官网 URL。
如果缺失,使用 AskUserQuestion 快速采集以上 5 项(一次一题)。
2.2 模拟 AI 搜索召回测试
使用 Agent 工具派发 subagent,模拟在 ChatGPT / Perplexity / AI Overviews 中查询目标问题,记录当前是否提及本产品、提及时的描述是否准确:
Tool: Task
Parameters:
subagent_type: "general-purpose"
description: "GEO现状召回诊断"
prompt: |
你是一个 GEO(生成式引擎优化)审计专家。请基于公开信息,模拟用户在主流 AI 搜索引擎中提问时的召回情况。
**产品信息**:{产品名称} / {核心品类} / {官网URL} / {差异化卖点}
**目标查询场景**:{GEO_TARGET}
**任务**:
1. 针对每个目标查询,判断当前主流 AI 引擎(ChatGPT、Perplexity、Google AI Overviews、豆包、文心一言)在回答中是否会提及该产品。
2. 若提及,描述是否准确、是否给出官网/正确链接。
3. 若未提及,分析最可能的原因(实体未被收录、内容信号弱、缺乏权威引用、品类归因错误等)。
4. 给出 0-100 的可见性评分与 3 条最关键的缺口。
返回结构化 JSON:
```json
{
"visibility_score": 0-100,
"per_query": [{"query": "...", "mentioned": true/false, "accuracy": "准确/偏差/错误", "reason": "..."}],
"top_gaps": ["缺口1", "缺口2", "缺口3"]
}
```
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.
- 8d ago First seen · 273 lines · 84 tokens per session scan A c5df977f4c83
pm-geo is a skill published in the GitHub repository konglong87/superPM (62 stars, last pushed 5d ago), licensed MIT. It adds 84 tokens to every session and 2,830 once invoked, about $0.0004 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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