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 wordflowlab/novel-writer-skills --skill forgotten-elementsgit clone --depth 1 https://github.com/wordflowlab/novel-writer-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/wordflowlab/novel-writer-skills/forgotten-elements)<a href="https://agentmods.dev/skills/wordflowlab/novel-writer-skills/forgotten-elements"><img src="https://agentmods.dev/badge/skills/wordflowlab/novel-writer-skills/forgotten-elements/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/wordflowlab/novel-writer-skills/forgotten-elements"><img src="https://agentmods.dev/badge/skills/wordflowlab/novel-writer-skills/forgotten-elements.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 | $0.00047 | $0.00959 |
| Opus 5 | $0.00023 | $0.00479 |
| Sonnet 5 | $0.00009 | $0.00192 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
forgotten-elements-reminder 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
遗忘元素提醒器
核心功能
防止长篇小说常见问题：
- 角色突然消失（"配角A去哪了？"）
- 情节线被遗忘（"那个伏笔后来呢？"）
- 伏笔没回收（"前文说的宝藏呢？"）
解决方案：后台监控，主动提醒已经很久未出现的元素。
监控内容
1. 角色出场频率
监控：character-state.json中的所有角色
阈值：10章未出现
提醒示例：
⚠️ 角色提醒：
"配角李明"已经10章未出现（上次：第5章）
- 是否应该安排他再次出场？
- 还是这个角色的故事已结束？
2. 情节线进度
监控：plot-tracker.json中的活跃情节线
阈值：12章无进展
提醒示例：
⚠️ 情节线提醒：
"寻找父亲仇人"情节线已12章无进展
- 上次推进：第8章
- 当前状态：pending
- 建议：安排相关情节或标记为暂停
3. 伏笔回收
监控：在前文埋下的重要伏笔
阈值：20章未回收
提醒示例：
⚠️ 伏笔提醒：
第3章提到"神秘盒子"，至今未揭秘（已过23章）
- 读者可能已经忘记
- 建议：尽快回收或在对话中提及
提醒时机
写作前提醒
执行 `/write` 时，如果检测到遗忘元素：
📋 写作前检查...
⚠️ 发现3个被遗忘的元素：
1. 角色"张婶"已15章未出现
2. 情节线"寻宝"已13章无进展
3. 伏笔"神秘信件"已20章未揭秘
💡 本章是否考虑处理？
分析时汇总
执行 `/analyze` 时，生成完整报告：
## 遗忘元素报告
### 失踪角色（3个）
1. 张婶（15章未出场）
2. 李老板（11章未出场）
3. 王医生（10章未出场）
### 停滞情节线（2个）
1. 寻宝线（13章无进展）
2. 复仇线（12章无进展）
### 未回收伏笔（1个）
1. 神秘信件（20章未揭秘）
建议：
- 优先处理失踪角色（读者可能已忘记）
- 推进停滞情节线或标记为"暂停"
- 伏笔及时回收（否则成烂尾）
配置选项
调整阈值
"角色未出场提醒阈值改为15章"
"情节线停滞提醒阈值改为20章"
排除特定元素
"张婶角色已完结，不再提醒"
"寻宝线有意暂停，30章后才推进，不提醒"
最佳实践
- 定期查看：每10章运行一次
/analyze查看报告 - 及时处理：不要让太多元素堆积
- 主动标记：已完结的角色标记为"retired"
总结
forgotten-elements-reminder = 你的记忆助手
✓ 自动监控角色/情节/伏笔 ✓ 超过阈值主动提醒 ✓ 防止长篇小说烂尾
长篇小说必备！ 📝
本Skill版本: v1.0 最后更新: 2025-10-18
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 · 148 lines · 47 tokens per session scan A 363a7e92b9ce
forgotten-elements-reminder is a skill published in the GitHub repository wordflowlab/novel-writer-skills (258 stars, last pushed 10mo ago), licensed MIT. It adds 47 tokens to every session and 959 once invoked, about $0.0002 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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