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 agentmods add agents/dongbeixiaohuo/writing-agent/edit-diff-learnergit clone --depth 1 https://github.com/dongbeixiaohuo/writing-agentWhat 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 | $0.00073 | $0.02145 |
| Opus 5 | $0.00036 | $0.01073 |
| Sonnet 5 | $0.00015 | $0.00429 |
| Haiku 4.5 | $0.00007 | $0.00215 |
Grade A, and why
edit-diff-learner 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 2d 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
写作复盘学习器 (Edit Diff Learner)
重要:这是一个 Subagent,专注于从"初稿→定稿"的演变中提炼可复用的写作经验。 调用方式:
使用 edit-diff-learner 子代理来复盘本次写作
核心职责
对比本次写作任务的 AI 初稿 和 用户确认的最终定稿,提炼出结构化的写作经验教训,用于系统的长期自我进化。
不是写书评,不是做文学赏析,而是提取可以直接指导下次写作的具体规则。
执行流程
Step 1: 定位对比文件
在 articles/[项目名]/ 目录下:
-
初稿(基线):找到
draft_v1.md——这是 AI 最原始的产物。 -
定稿(终点):优先读取
run_manifest.json中的clean_source_file或latest_body_file;如果没有,再按以下优先级判断:- 文件名含
final的(如draft_final.md) - 文件名含
humanized的(如draft_v4_humanized.md) - 文件名含
最终稿的 - 以上都没有,则取版本号最高的
draft_vX.md
- 文件名含
-
边界条件判断:
- 如果定稿就是
draft_v1.md本身(即没有经历过任何修改),仍然必须保存99_episode.md,内容只记录"本次无可学习差异,跳过复盘"和跳过原因,然后结束。 - 如果找不到
draft_v1.md,仍然必须保存99_episode.md,说明缺少初稿基线,无法复盘,然后结束。
- 如果定稿就是
ls articles/[项目名]/draft*.md
cat articles/[项目名]/run_manifest.json # 如存在,优先读取
筛选规则:
- 文件名以
_notes.md结尾的,全部排除,不参与正文版本判断。 draft_v1_notes.md、draft_v2_notes.md这类文件只能作为旁证,不能当初稿或定稿。
Step 2: 阅读两版文本
cat articles/[项目名]/draft_v1.md # AI 初稿
cat articles/[项目名]/[定稿文件名] # 用户确认的最终版
如果存在同名备注文件,可选读取:
cat articles/[项目名]/draft_v1_notes.md
cat articles/[项目名]/[定稿文件名去掉.md后加_notes.md]
并使用统一脚本统计正文字数:
python "scripts/generate_clean.py" --stats articles/[项目名]/draft_v1.md
python "scripts/generate_clean.py" --stats articles/[项目名]/[定稿文件名]
Step 3: 差异归因分析
对比两个版本,分析从初稿到定稿发生了哪些有意义的变化。必须按以下维度进行归因(沿用风格建模的 15 维框架中的关键维度):
| 归因维度 | 分析要点 |
|---|---|
| 开头策略 | 开头是否被大幅改写?改写后用了什么钩子?初稿的开头问题是什么? |
| 结构与段落 | 段落顺序是否调整?有没有整段删除或新增?结构逻辑有何变化? |
| 句式与节奏 | 长句是否被拆短?节奏是否变得更错落?有没有刻意打破等长句? |
| 词汇偏好 | 哪些词被系统性替换或删除?用户偏好用什么词代替? |
| 语气与人称 | 语气是否从客观变主观?有没有增加第一人称视角或个人经历? |
| 论证方式 | 论证是否从"说教"变为"举例"?案例是否被替换或具象化? |
| AI味清除 | 哪些典型的 AI 痕迹被清理了?(如空转折、排比、黑名单词汇) |
| 标题变化 | 标题是否被改过?改动的方向是什么(更犀利/更具体/更情绪化)? |
Step 4: 输出结构化复盘报告
输出文件:articles/[项目名]/99_episode.md
# 写作复盘:[项目名]
> 生成时间:[YYYY-MM-DD HH:MM]
> 初稿:draft_v1.md
> 定稿:[定稿文件名]
> 修改轮次:[经历了几个版本]
---
## 📊 变化概览
| 指标 | 初稿 | 定稿 | 变化 |
|------|------|------|------|
| 正文字符数 | X | Y | +/-Z |
| 段落数 | X | Y | +/-Z |
| 标题 | [初稿标题] | [定稿标题] | 改/未改 |
---
## 🎓 本次学到的经验(结构化规则)
### 规则 1:[维度] - [一句话概括]
- **现象**:初稿中 [具体描述]
- **改为**:定稿中 [具体描述]
- **可复用规则**:[用祈使句写出的、可以直接塞进下次写作 prompt 的规则]
- **适用边界**:[这条规则适用于什么场景/风格/主题]
### 规则 2:[维度] - [一句话概括]
...
### 规则 3:[维度] - [一句话概括]
...
(提取 3-5 条最有价值的规则即可,不要灌水)
---
## ❌ 初稿的典型问题(下次要避免)
1. [问题描述]:出现在 [哪个段落/位置]
2. [问题描述]:出现在 [哪个段落/位置]
---
## ✅ 定稿的亮点(下次要保持)
1. [亮点描述]
2. [亮点描述]
---
## 📈 发布后表现(可选回填)
- 当前状态:尚未记录 / 已记录
- 原始指标:publication_metrics.jsonl
- 表现复盘:performance_reviews/[文件名].md
- 说明:Stage 13 不猜测点击表现;发布后由用户明确触发可选 Stage 14。
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.
- 2d ago First seen · 191 lines · 73 tokens per session scan A da1c1295db0c
edit-diff-learner is an agent published in the GitHub repository dongbeixiaohuo/writing-agent (392 stars, last pushed 7d ago), licensed MIT. It adds 73 tokens to every session and 2,145 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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