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 kezd088/100x-skill-tiktok --skill 100x-search-querygit clone --depth 1 https://github.com/kezd088/100x-skill-tiktokWrote 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/kezd088/100x-skill-tiktok/100x-search-query)<a href="https://agentmods.dev/skills/kezd088/100x-skill-tiktok/100x-search-query"><img src="https://agentmods.dev/badge/skills/kezd088/100x-skill-tiktok/100x-search-query/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/kezd088/100x-skill-tiktok/100x-search-query"><img src="https://agentmods.dev/badge/skills/kezd088/100x-skill-tiktok/100x-search-query.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 | $0.00069 | $0.02301 |
| Opus 5 | $0.00034 | $0.01151 |
| Sonnet 5 | $0.00014 | $0.00460 |
| Haiku 4.5 | $0.00007 | $0.00230 |
Grade A, and why
100x-search-query 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
100x-search-query
一句话定位
输入产品/人设卡片，输出 Pinterest / TikTok / Reddit 三平台各 15 条英文搜索短语，用于找 视觉参考素材、灵感图和对标内容。属于 100x 体系 L2 创意生成层，对应"搜索关键词"这一步。
何时触发
用户说：
- "帮我找参考图" / "找灵感图" / "去哪找参考图" / "给我搜索关键词"
- "what should I search on Pinterest" / "find reference images"
- "need TikTok/Reddit search terms for this product"
- 或直接给一个产品卡片 / persona 卡片，要求出一套搜索词
输入
最小输入（类别 A，硬性必填）：category（品类，自由文本，例如"保健品""家居""美妆个护"；
参考未来 taxonomies/，禁止套用任何客户专属品类词典）。product_name 不是用户
必须提供的输入——拿不到具体品牌/产品名时用 "[品类锚点] <category>" 占位继续跑
（schema.json 要求输出里的 product_name 必须非空字符串，那是"输出契约的必填"，
不是"用户输入的必填"，两者不是一回事）。详见 workflow.md Phase 1 类别 A 校验。
软性补充（类别 B，缺失走三级降级，见 workflow.md Phase 1）：core_benefit、
target_audience、brand_tone、topic_angle（default/aesthetic/pain-driven/
sales-push 之一）。
上游可选产出（类别 C，缺失静默跳过，不阻塞，也绝不要求用户先跑别的 skill）：persona
卡片、insight 结果、爆点结果。若提供，则必须真正影响生成——2C 阶段至少 6 条
要体现 persona 的具体受众/身份角度，不能只是收下卡片但产出跟没给一样，详见
workflow.md Phase 1 步骤 4 / Phase 2 相应段落。
输出
结构见 schema.json：queries.pinterest/tiktok/reddit 各 15 条 {q, intent_cn, stage}，
meta.based_on_5a 声明本次 5A 覆盖。拿到类别 C（persona/insight）输入时，
meta.persona_informed 须为 true 且 meta.persona_descriptor_terms 须列出从卡片摘取
的具体受众/身份用词，否则不算合规产出（公理 5）；没拿到类别 C 输入时两个字段都可以
省略。可选再渲染 3 张 Markdown 表（每平台一张，列：# / query / search_intent_cn /
5a_stage）。
核心约束（5 条公理，详见 axioms.md）
- 每平台正好 15 条，全英文 ASCII，少 1 条或混中文 = 作废
- 平台差异化锁死：Pinterest 美学词 / TikTok hashtag+梗词 / Reddit 痛点问句，串台作废
- 基于 5A 意图分层生成，
meta.based_on_5a必填非空，45 条覆盖 ≥3 个阶段 - 每条必带 ≤20 字中文搜索意图注释，格式必须是"闭集类别标签+纯中文说明"（已知限制：
这是格式校验，能挡住"整句照抄英文翻译"，但挡不住"贴合法标签壳、内容仍是逐字
直译"这种更隐蔽的情况，例如给直译内容随手配一个"质疑:"前缀就能通过——这是已知
语义缺口，不是"保证零直译"，见
axioms.md公理 4 TODO） - 拿到 persona/insight 输入就必须真的体现在产出里：
persona_informed=true时，45 条里至少 6 条要命中persona_descriptor_terms声明的具体受众/身份用词，否则作废（已知限制：词表判据挡不住"敷衍地摘几个过宽泛的 词凑数"，只能挡"完全没用"，见axioms.md公理 5）
边界（无例外）：无论产品文案源语言是什么（支持英文与西语等多语种输入），
输出搜索词始终是英文——这是公理 1 的直接推论，不是额外限制。没有非英文版本这个
选项：输出短语严格受 schema.json 的 ASCII 强校验约束，无非英文产出分支。
What ships with it
10 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 · 116 lines · 69 tokens per session scan A 0265045b22f3
100x-search-query is a skill published in the GitHub repository kezd088/100x-skill-tiktok (8 stars, last pushed 16d ago), licensed MIT. It adds 69 tokens to every session and 2,301 once invoked, about $0.0003 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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