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 ilexlyu/ad-proposal-skills --skill ad-proposal-narrativegit clone --depth 1 https://github.com/ilexlyu/ad-proposal-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/ilexlyu/ad-proposal-skills/ad-proposal-narrative)<a href="https://agentmods.dev/skills/ilexlyu/ad-proposal-skills/ad-proposal-narrative"><img src="https://agentmods.dev/badge/skills/ilexlyu/ad-proposal-skills/ad-proposal-narrative/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/ilexlyu/ad-proposal-skills/ad-proposal-narrative"><img src="https://agentmods.dev/badge/skills/ilexlyu/ad-proposal-skills/ad-proposal-narrative.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 | $0.00083 | $0.00583 |
| Opus 5 | $0.00042 | $0.00292 |
| Sonnet 5 | $0.00017 | $0.00117 |
| Haiku 4.5 | $0.00008 | $0.00058 |
Grade A, and why
ad-proposal-narrative 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.
What it actually says
提案结构与客户叙事
目标：把策略和创意整理成客户听得懂、愿意买单的 PPT 逻辑。核心不是堆资料，而是建立“客户为什么要相信”的说服链。
Inputs
- 洞察 Top 3、创意方向、客户背景、决策人类型、交付页数、已有资料。
- 若只有资料没有洞察，先建议调用
$ad-insight-reviewer。
Workflow
-
确定主线
- 用一句话说明提案要证明什么。
- 判断客户当前最关心的是增长、品牌、转化、认知、招商、内部对齐还是风险规避。
-
拆章节
- 背景：为什么现在要做。
- 问题：客户真正要解决什么。
- 洞察：我们如何重新解释市场/用户。
- 策略：品牌应该怎么行动。
- 创意：用什么传播方向承接策略。
- 执行：怎么落地到内容、渠道、节奏。
- 价值：为什么客户应该买单。
-
生成逐页蓝图
- 每页只保留一个主判断。
- 为每页补证据、画面建议、讲述意图和转场逻辑。
-
写客户版摘要
- 用客户语言复述，而不是代理商内部术语。
- 将复杂判断压缩成 3-5 个关键结论。
Output Format
## 提案主线
## 章节结构
| 章节 | 目的 | 客户要被说服的点 |
## 逐页蓝图
| 页码 | 标题 | 主判断 | 证据 | 画面建议 | 转场逻辑 |
## 客户版摘要
## 讲稿要点
Quality Bar
- 每页只能有一个主判断。
- 不能只是“行业趋势、竞品分析、人群洞察”平铺；必须形成因果链。
- 必须回答：客户为什么要相信、为什么现在要做、为什么这条策略适合他。
- 说明每个章节承担的说服功能，以及它如何推动客户进入下一项判断。
What ships with it
1 file 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.
- 11d ago First seen · 60 lines · 83 tokens per session scan A 3792fbdf82ad
ad-proposal-narrative is a skill published in the GitHub repository ilexlyu/ad-proposal-skills (10 stars, last pushed 1mo ago), licensed MIT. It adds 83 tokens to every session and 583 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 2026-08-31.
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