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 yaojingang/yao-geo-skills --skill yao-geo-intent-minergit clone --depth 1 https://github.com/yaojingang/yao-geo-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/yaojingang/yao-geo-skills/yao-geo-intent-miner)<a href="https://agentmods.dev/skills/yaojingang/yao-geo-skills/yao-geo-intent-miner"><img src="https://agentmods.dev/badge/skills/yaojingang/yao-geo-skills/yao-geo-intent-miner/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/yaojingang/yao-geo-skills/yao-geo-intent-miner"><img src="https://agentmods.dev/badge/skills/yaojingang/yao-geo-skills/yao-geo-intent-miner.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 | $0.00071 | $0.01075 |
| Opus 5 | $0.00036 | $0.00537 |
| Sonnet 5 | $0.00014 | $0.00215 |
| Haiku 4.5 | $0.00007 | $0.00108 |
Grade A, and why
yao-geo-intent-miner 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.
What it actually says
Yao GEO Intent Miner
使用场景
- 把种子词、品牌、产品、竞品、区域、人群和业务材料扩展成 AI 搜索问题集。
- 先于内容生产建立问题底座,输出意图簇、追问链路、查询重写、内容选题、FAQ、知识库和监测 Prompt。
- 面向 DeepSeek、豆包、千问、Kimi、元宝适配国内 AI 平台的口语、多轮、场景和复杂决策问法。
必读资料
references/research-foundation.mdreferences/intent-mining-method.mdreferences/cn-platform-adaptation.mdreferences/scoring-and-mapping.mdreferences/real-data-ingestion.mdreferences/report-module-contract.mdreferences/four-format-output.mdreferences/quality-gates.md
执行流程
- 归一化输入对象:主对象、品牌、产品线、行业、竞品、区域、人群、预算、场景、痛点、资质、规模、时效和材料来源。
- 判断真实数据模式:
未接入 / 用户提供 / 工具或连接器导入 / 已采样校准。没有真实数据时,必须输出数据缺口和采样计划,不能伪造真实平台回答、搜索量或转化数据。 - 做事实与证据校准:品牌、产品、价格、合规、竞品和行业事实必须标注来源状态,未校准信息只能作为假设或待确认项。
- 建立双层意图:先映射到信息、导航/验证、交易/行动任务层,再扩展为九类 GEO 操作意图。
- 按角色、场景、决策阶段、约束条件、证据需求和内容资产用途生成完整自然语言问题,不能只堆短关键词。
- 输出五段式重写:口语问法、独立重写、检索短语、证据查询、标题输入。
- 保留多轮追问链路:
root_question_id / parent_question_id / standalone_rewrite / context_dependency / platform_fit。 - 聚类去重,按问题目标、用户角色、约束条件、资产用途和合规等级合并同义问题。
- 十维评分:商业价值、AI 答案触发概率、内容缺口、品牌植入空间、证据可得性、竞争难度、对话延展价值、决策阶段价值、平台覆盖度、合规风险。若有真实数据,按搜索量、AI 采样、客服/销售频次和转化信号校准分数。
- 映射资产:文章、页面模块、FAQ、知识库条目、监测 Prompt、标题生成输入包、证据补采任务和 30/60/90 天落地路线。
- 报告正文必须覆盖
references/report-module-contract.md的系统模块;允许按项目删减,但删减原因要写入质检说明。 - 输出 Word/PDF/HTML/Markdown 时必须遵守
references/four-format-output.md。HTML 使用 kami 长文档排版语言:暖纸底、油墨蓝、暖灰边框、serif 标题、sticky 菜单;Word 宽表必须横向 A4、固定表格布局、表格总宽不超过页面可用宽度;PDF 默认横向 A4,宽表自动换行且不向右溢出。 - 自 review:检查四个报告是否存在、格式是否有效、内容是否一致、HTML/Word/PDF 是否符合 kami 版式且表格不溢出。
输出契约
- AI 搜索问题集与意图地图。
- 问题聚类、追问链路和查询重写清单。
- 评分矩阵与优先级排序。
- 内容选题、FAQ 题库、知识库条目建议。
- 监测 Prompt 库。
- 证据缺口、合规边界和落地路线。
- 真实数据接入状态、AI 平台采样计划、数据校准动作。
- 默认四件套:Markdown、HTML、Word、PDF。
What ships with it
29 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.
- agents/interface.yaml 1005 B
- evals/expected_artifacts.json 495 B
- evals/failure_cases.md 696 B
- evals/quality_cases.json 674 B
- evals/rubric.md 597 B
- evals/trigger_cases.json 334 B
- examples/hubspot-cn-demo/expected-output/hubspot-cn-ai-intent-miner-demo.docx 24 KB
- examples/hubspot-cn-demo/expected-output/hubspot-cn-ai-intent-miner-demo.html 63 KB
- examples/hubspot-cn-demo/expected-output/hubspot-cn-ai-intent-miner-demo.md 44 KB
- examples/hubspot-cn-demo/expected-output/hubspot-cn-ai-intent-miner-demo.pdf 388 KB
- examples/hubspot-cn-demo/expected-output/index.html 63 KB
- examples/hubspot-cn-demo/expected-output/quality-report.json 2.2 KB
- examples/hubspot-cn-demo/input/report_input.json 70 KB
- examples/README.md 896 B
- manifest.json 726 B
- references/cn-platform-adaptation.md 1.1 KB
- references/four-format-output.md 1.6 KB
- references/intent-mining-method.md 2.1 KB
- references/quality-gates.md 1.6 KB
- references/real-data-ingestion.md 2.5 KB
- references/report-module-contract.md 3.6 KB
- references/research-foundation.md 2.4 KB
- references/scoring-and-mapping.md 1.6 KB
- reports/data-capability-ui-upgrade-2026-05-21.md 1.9 KB
- reports/layout-fix-2026-05-19.md 228 B
- reports/method-upgrade-2026-05-21.md 2.5 KB
- scripts/check_report_layout.py 2.9 KB runs code
- scripts/render_intent_miner_report.py 30 KB runs code
- templates/brief-template.md 1.0 KB
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 · 59 lines · 71 tokens per session scan A e8e4370ad045
yao-geo-intent-miner is a skill published in the GitHub repository yaojingang/yao-geo-skills (742 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,075 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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