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 Gingiris-1031/gingiris-skills --skill gingiris-ugc-matrixgit clone --depth 1 https://github.com/Gingiris-1031/gingiris-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/gingiris-1031/gingiris-skills/gingiris-ugc-matrix)<a href="https://agentmods.dev/skills/gingiris-1031/gingiris-skills/gingiris-ugc-matrix"><img src="https://agentmods.dev/badge/skills/gingiris-1031/gingiris-skills/gingiris-ugc-matrix.svg" alt="Measured on agentmods" 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 | $0.00432 | $0.03297 |
| Opus 5 | $0.00216 | $0.01648 |
| Sonnet 5 | $0.00086 | $0.00659 |
| Haiku 4.5 | $0.00043 | $0.00330 |
Grade A, and why
gingiris-ugc-matrix 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 7d 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ 2C 产品的渠道调整
本 skill 默认 dev / B2B 渠道。2C 消费品 / 教育 / 应用：获客主战场是 垂类社区 + 短视频 + 垂直 KOL，按地区公开数据选第一平台（如印尼/泰国短视频已反超 Facebook）。KOL 优先 nano / micro 垂类——粉丝越多互动率越低，micro > mega 性价比更高。完整 2C 渠道数据库 + 公开来源见 → gingiris-seo-geo/references/2c-adaptation.md。
UGC 矩阵号增长实操手册
🌍 Language / 语言: 中文 | English
核心方法论
UGC增长飞轮（IP号 + 矩阵号）
IP号（立人设、建信任）
→ 吸引高质量Creator主动加入
→ UGC矩阵号（50-500个账号批量产出）
→ 规模化曝光放大IP声量
→ 更多人加入 → 飞轮加速
关键数据（客户案例验证，某 AI 知识工作产品·团队自报）
| 指标 | 数据 |
|---|---|
| 团队规模 | 2人核心 |
| 时间周期 | 60天 |
| ARR | ~$10M |
| 全球社媒曝光 | 7000万 |
| CPM | $0.5 |
| 真人:AI UGC比例 | 6:4 |
| 融资/广告费/销售团队 | 零 |
执行SOP概要
1. 创作者招募与分层
- 战略家（Strategists）：10人，创造新概念、测试新方向
- 执行者（Executors）：70人，精准复刻爆款脚本
- 招募渠道优先级：核心圈层 > 微型创作者私信 > 社群招募 > Cold Email
2. 激励机制设计
- 阶梯式播放量Bonus（1万→+$20，5万→+$30，10万→+$50）
- 只付最高流量平台费用，其余平台白嫖
- 全员奖金池机制（达标后按贡献比例分配）
3. AI矩阵号运营
- AI UGC三段式结构：Hook（5秒AI数字人）→ Use Case Demo → CTA
- 单AI账号每条帖子：十几万~20万播放
- 发布频率：一天2-3条（避免被封）
- 可灵(Kling)换脸批量生成不同人物版本
4. 多平台策略
| 平台 | 受众 | 转化率 | 关键 |
|---|---|---|---|
| TikTok | 13-18岁 | 最低 | 极度抽象、Hook强 |
| 18-25岁 | 中等 | 重分享、视觉质量 | |
| YouTube | 25-35岁 | 最高 | 前3秒留存 |
| Threads | 全年龄 | 高 | 算法红利期 |
5. 内容策略两步走
- AI干货增粉：工具列表、技巧分享 → 涨粉
- Use Case转化：Before & After帖、手机实拍 → 转化
6. 市场进入顺序
🇺🇸 美国（先） → 🇯🇵 日本 → 🇹🇼 港台+🇰🇷 韩国 → 🌏 东南亚
完整SOP
上线前安全门
- 确认人物肖像、声音、音乐、素材和品牌标识的授权范围，并保存可追溯记录。
- AI 生成或付费合作按平台和当地法规清晰披露；不做未授权换脸、内容洗稿或多账号规避处罚。
- 先用 3–5 个账号验证“发布后 24 小时存活 + 有效互动 + 激活”，再扩矩阵；账号数量不是成功指标。
- 每周记录素材假设、平台、版本、发布时间、24h/7d 数据和复用决定，淘汰高曝光低激活内容。
详细操作手册见 references/full-sop.md，包含：
- 品牌大使思维与创作者筛选标准
- 完整招募渠道与管理SOP
- 激励机制设计（固定费用+阶梯Bonus+奖金池）
- AI-Assisted Multi-Platform Content Strategy
- Launch Week发布计划（每天20条×60创作者）
- 平台冷启动路径（Reddit/YouTube/小红书）
- 社区体系建设（品牌挚友计划）
- 内容制作方法论（拆竞品+AI批量生产）
- 客户案例完整复盘（已脱敏）
What ships with it
3 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.
- 7d ago First seen · 236 lines · 432 tokens per session scan A de2c9c0a166d
gingiris-ugc-matrix is a skill published in the GitHub repository Gingiris-1031/gingiris-skills (77 stars, last pushed 2d ago), licensed MIT. It adds 432 tokens to every session and 3,297 once invoked, about $0.0022 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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