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 AgenticAIPlan/AgenticAISkills --skill ecopartner-match-recommendgit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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/agenticaiplan/agenticaiskills/ecopartner-match-recommend)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/ecopartner-match-recommend"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ecopartner-match-recommend/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/agenticaiplan/agenticaiskills/ecopartner-match-recommend"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ecopartner-match-recommend.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 | $0.00108 | $0.01430 |
| Opus 5 | $0.00054 | $0.00715 |
| Sonnet 5 | $0.00022 | $0.00286 |
| Haiku 4.5 | $0.00011 | $0.00143 |
Grade A, and why
ecopartner-match-recommend 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 11d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ecopartner-match-recommend
适用场景
当用户需要将一个模糊的 AI 落地需求快速匹配至中南片区的生态伙伴时，使用本 Skill。
典型触发场景：
- 客户提出 AI 项目需求，但未明确指定供应商
- 需要快速了解某类 AI 能力有哪些合作伙伴可承接
- 需要生成一份可汇报的伙伴匹配方案
- 区域生态运营中的供需对接
关键约束：本 Skill 仅覆盖中南片区生态伙伴，包括华南（广东、广西、海南、福建、香港、澳门）、华中（湖南、湖北）、西南（重庆、四川）。
输入要求
- 业务目标：客户想解决的 AI 落地问题（描述性文本）
- 背景上下文：客户的行业、规模等基本信息（如有）
- 关键约束：是否指定技术路线（飞桨/文心）、预算范围等
- 期望输出：推荐伙伴列表 + 可视化报告
执行步骤
Step 1：读取伙伴数据
读取 references/partner-data.json，获取完整伙伴列表。
Step 2：智能需求解析
仔细理解客户需求，拆解出所需 AI 能力组合：
- 需要 OCR 文字识别吗？
- 需要计算机视觉（图像检测/分类/分割）吗？
- 需要自然语言处理（文本分析/抽取）吗？
- 需要大模型/智能体吗？
- 需要语音识别吗？
- 还需要什么其他能力？
Step 3：技术选型建议
根据场景推荐飞桨或文心技术路线：
| 场景 | 推荐技术 | 原因 |
|---|---|---|
| 软硬一体/边缘部署 | 飞桨 | PaddleX、PP-OCR 原生支持端侧 |
| OCR + 视觉组合 | 飞桨 | PP-OCRv5 + PaddleX 视觉生态完整 |
| 工业视觉/质检 | 飞桨 | 工业相机 + 边缘推理生态成熟 |
| 快速接入/大模型对话 | 文心 | ERNIE API 调用简单，上线快 |
| 知识库 + RAG | 文心 | ERNIE + 千帆平台集成更好 |
| 数字人/创意生成 | 文心 | ERNIE 数字人能力更强 |
Step 4：多标签组合匹配
带着 Step 2 分析出的 AI 能力，逐一审视伙伴数据：
| 优先级 | 规则 |
|---|---|
| 第一优先 | ai_tags 同时包含多个需求能力 |
| 第二优先 | ai_tags 包含核心能力 + industry 匹配 |
| 第三优先 | ai_tags 包含核心能力（备选） |
Step 5：分级推荐输出
按匹配度输出推荐结果：
| 等级 | 标准 |
|---|---|
| ⭐⭐⭐⭐ 强烈推荐 | AI能力全匹配 + 行业/场景匹配 |
| ⭐⭐⭐ 推荐 | AI能力匹配，或能力+行业部分匹配 |
| ⭐⭐ 备选 | 仅AI能力匹配 |
Step 6：智能追问
每次选最相关的 1-2 个维度追问，每次 ≤ 3 个问题：
| 维度 | 作用 | 适用场景 |
|---|---|---|
| 项目阶段 | 判断 POC 还是已立项 | 几乎所有场景 |
| 数据情况 | 影响方案设计 | 需要模型训练的 |
| 预算/时间 | 筛选供应商档位 | 有档位差异时 |
Step 7：生成 HTML 可视化报告
完成匹配后，生成独立 HTML 报告，包含：
- 需求分析（客户场景、所需 AI 能力、推荐技术）
- 伙伴推荐（按分级展示）
- 匹配理由（每家伙伴的核心优势）
- 注意事项（选型和实施关键提示）
- 追问清单（引导深入了解项目）
输出要求
- 结构清晰：按 ⭐⭐⭐⭐强烈推荐 / ⭐⭐⭐推荐 / ⭐⭐备选 分级展示
- 结论与过程一致：匹配理由需对应 Step 2 分析出的 AI 能力
- 明确风险与假设：
- 定价信息不完整，需单独询价
- 星级评分基于 ai_tags 匹配度，实际效果以 POC 为准
- 区域限制：仅中南片区
参考资料
| 文件 | 用途 |
|---|---|
references/partner-data.json |
核心数据：240+ 生态伙伴信息 |
references/partner-data-schema.md |
数据字段说明 |
references/report-template.html |
HTML 报告模板 |
references/match-report-example.html |
报告示例 |
references/screenshot-*.png |
报告效果截图 |
What ships with it
11 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.
- .gitignore 35 B
- README.md 7.4 KB
- references/design-guide.md 4.5 KB
- references/match-report-example.html 19 KB
- references/partner-data-schema.md 1.9 KB
- references/partner-data.json 199 KB
- references/report-template.html 13 KB
- references/screenshot-followup.png 333 KB
- references/screenshot-overview.png 227 KB
- references/screenshot-partners.png 271 KB
- scripts/match.py 13 KB runs code
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.
- 11d ago First seen · 134 lines · 108 tokens per session scan A 517d544014e8
ecopartner-match-recommend is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 108 tokens to every session and 1,430 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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