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 zhuanggenhua/BoardGame --skill card-effect-authoringgit clone --depth 1 https://github.com/zhuanggenhua/BoardGameWrote 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/zhuanggenhua/boardgame/card-effect-authoring)<a href="https://agentmods.dev/skills/zhuanggenhua/boardgame/card-effect-authoring"><img src="https://agentmods.dev/badge/skills/zhuanggenhua/boardgame/card-effect-authoring/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/zhuanggenhua/boardgame/card-effect-authoring"><img src="https://agentmods.dev/badge/skills/zhuanggenhua/boardgame/card-effect-authoring.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 | $0.00049 | $0.01674 |
| Opus 5.5 | $0.00020 | $0.00670 |
| Sonnet 5.5 | $0.00010 | $0.00335 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
card-effect-authoring 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 21d 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Card Effect Authoring
本 skill 吸收 ygopro-scripting-workflow 中可迁移的工作流做法，但只作为 BoardGame 的项目级适配层。规则真相、录入合同、审计判断和 AI 合法动作仍分别由现有 canonical 文档负责。
何时使用
- 用 AI 新增一张卡、一个能力或一组卡牌效果。
- 根据规则书、卡图、Wiki 或用户指定来源把卡牌效果落到运行时。
- 需要同时生成卡牌数据、效果实现、AI 支持、测试和 evidence。
- 需要把一批相似卡归入同一个共享效果流程。
纯规则审计走 game-audit-workflow；只做 AI 策略调参走 game-ai-strategy-design；只做资源录入走 data-entry-workflow。
先读
data-entry.md：主真相源、locked 合同和零猜测门禁。description-to-implementation-audit.md：语义、消费、真实入口、最终状态和证据链。rule-driven-interaction-design.md：Choice Request、权限、响应窗口和 AI 合法动作。testing-audit.md：测试范围和证据边界。game-ai-adaptation：AI 合法动作、交互收口和 watchdog 边界。- 目标游戏的卡牌 / 能力专项 spec、rule 文档和已有 evidence。
吸收的工作流原则
1. 结构化效果拆解
先把卡牌描述拆成可验证的原子语义：
- 时机与触发。
- 执行主体。
- 条件与使用门槛。
- 实际成本。
target、material、reference的对象角色。- 玩家或 AI 的选择。
- 数值、资源、区域和状态变化。
- 持续时间、次数限制、重置、失败路径和清理。
可以借鉴外部仓库的“条件 / 费用 / 对象 / 处理”拆解习惯，但不能把 YGOPro 的 target 或 operation 直接当成 BoardGame 的语义模型。
2. 参考同类流程再生成
生成实现前必须检索仓库内相似卡、共享 helper、Effect DSL primitive、semantic selector / gateway 和已有 sharedFlowId。输出要区分：
- 可直接复用的共享流程。
- 允许的配置差异，例如数值、数量上限、文案或目标集合。
- 新增职责，例如新的资源、状态、交互、AI 动作或最终权威状态。
- 只能保留为 audited exception 的 legacy 路径。
“看起来同类”不能替代逐项一致性核对。
3. 先抽取共享流程，再绑定卡牌差异
一批相似卡先建立或复用一个 sharedFlowId 和共享配置 schema。共享流程承载运行时实现、主领域测试、负向路径和共用的 AI / 交互收口；不要为每张卡复制一套实现和完整测试。
每张卡或每个能力只需绑定：
- locked 录入合同或明确的
partial / blocked / disputed状态。 - 卡牌定义 / i18n / 资源消费映射。
sharedFlowId与该卡的差异字段，例如数值、数量上限、文案或目标集合。- 共享流程覆盖不到的最小边界测试，以及该卡特有的 AI 合法动作、真实交互或 evidence。
- 若不存在可复用流程，先实现一次共享流程，再把卡牌差异写入配置；只有新增触发时机、资源、状态生命周期、交互、AI 合法动作或最终权威状态时，才新增独立实现和测试。
测试覆盖率只能帮助发现共享流程或配置边界的漏分支，不能单独证明卡牌完成。
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.
- 21d ago First seen · 120 lines · 49 tokens per session scan A 3ea48a9e8231
card-effect-authoring is a skill published in the GitHub repository zhuanggenhua/BoardGame (24 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 1,674 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-04.
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