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 yugef3h/leo-skills --skill role-cardgit clone --depth 1 https://github.com/yugef3h/leo-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/yugef3h/leo-skills/role-card)<a href="https://agentmods.dev/skills/yugef3h/leo-skills/role-card"><img src="https://agentmods.dev/badge/skills/yugef3h/leo-skills/role-card/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/yugef3h/leo-skills/role-card"><img src="https://agentmods.dev/badge/skills/yugef3h/leo-skills/role-card.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 | $0.00087 | $0.00888 |
| Opus 5 | $0.00044 | $0.00444 |
| Sonnet 5 | $0.00017 | $0.00178 |
| Haiku 4.5 | $0.00009 | $0.00089 |
Grade A, and why
role-card 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
role-card：角色魅力蒸馏
把一个你喜欢的角色，蒸成「为什么打动观众 + 怎么设计出来的」的精简档案，并把能复用的讨喜公式入库。
先读：
- 抽取方法 →
references/extraction-core.md - 输出模板 →
references/templates.md - 查证与防幻觉 →
references/evidence.md - 完整实例 →
references/examples.md
主流程 7 步
1. 输入收集
问用户（若未提供）：作品名 + 角色名。用户可补充：自己对这角色的印象、喜欢点、粘贴片段。用户口述是一级依据，别浪费。
2. 联网查证
按 references/evidence.md：先查剧情事实（百科/官方），再查真实口碑（书评/豆瓣/知乎/弹幕）。网文查不到权威来源是常态，走依据分级兜底。
3. 客观抽取
按 references/extraction-core.md 出基础档案：关键节点、性格、能力、语言习惯、人际、内核。影视靠台词/表演/镜头，网文靠心理独白/金句/读者反馈，两套方法分开用。
4. 魅力分析（核心）
按 references/templates.md 六维度：高光节点 / 讨喜点（映射情绪机制）/ 反转弧光 / 人格魅力源（原型标签）/ 观众口碑 / 案例故事。每条判断必须带依据标注。
5. 配方提炼
把能复用的讨喜公式、反转套路抽成可填空公式，按 references/templates.md 配方卡写法，标注验证角色 + 适用场景。
6. 存档入库
- 角色卡 →
vault/characters/<角色名>.md（同名角色追加作品名） - 配方 → 追加进
vault/formulas/<机制>.md表格 - 更新
vault/README.md索引（角色表加一行、配方计数 +1）
7. 存疑确认
把「存疑待确认」清单给用户拍板。用户确认的补入、否定的删掉、未答的留在清单里，不写进正式档案。
输出要求（精简）
- 角色卡目标是能扫读、不冗长：关键节点 3-5 个、讨喜点 3-5 条、每条一句。
- 没有标志性台词就删掉那一行，不硬凑。
- 先给用户看成品卡片，确认后再入库，别直接写库。
红线
- 据实，不脑补：无依据的判断进「存疑待确认」，不落笔。
- 依据分级：用户口述 > 真实评论/官方 > 剧情事实 > AI推断（推断必须标注）。
- 区分一时冲动与稳定性格；区分演员/作者本人与角色；续作/改编人设变动分开。
- 不套 AI 空洞套词（眼底掠过一丝之类）。
- 依据标注符号统一用：
【用户口述】【真实评论】【官方】【剧情】【推断】。
What ships with it
4 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 · 54 lines · 87 tokens per session scan A f3dcca1cf884
role-card is a skill published in the GitHub repository yugef3h/leo-skills (11 stars, last pushed 5d ago), licensed MIT. It adds 87 tokens to every session and 888 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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