exemplar-prose-calibration

exemplar-prose-calibration is a skill for Claude Code, Codex from BingHanOfUESTC/open_agent_team. It costs 65 tokens per session (1,761 once invoked), scanned A, original, MIT.

A writing method for matching Chinese genre-fiction prose to supplied high-quality examples by extracting reusable techniques without copying them.

In plain words
What is it for?
Use it before drafting, during revision, or for line editing when you need a clearer voice, stronger details, and better information flow.
Why use it?
It helps address bland, overly explanatory, or AI-like writing while keeping the story original.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /Users/ai_bing/projects/multi_agents/novel_examples.

Good fit Use it before drafting, during revision, or for line editing when you need a clearer voice, stronger details, and better information flow.

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Install

Getting it into your agent

There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for exemplar-prose-calibration

README.md
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Your own site
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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.

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Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,761 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00065 $0.01761
Opus 5 $0.00032 $0.00881
Sonnet 5 $0.00013 $0.00352
Haiku 4.5 $0.00006 $0.00176

Measured 7d ago against content hash 52dca7f9be89, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

exemplar-prose-calibration 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 7d 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.

teams/story_team/skills/exemplar-prose-calibration/SKILL.md · 146 lines

How it starts

The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Exemplar Prose Calibration / 样本文学质感校准

本 skill 只提炼可迁移写作方法,不允许仿写原作、不允许保留样例中的人物名、地名、组织名、术语、桥段链或专属世界观组合。


1. 从样例中提炼出的高价值特征

1. 开篇先给处境、异常、声音或行动,不先解释设定。
2. 叙述声音有明显立场:可以偏执、冷淡、自嘲、天真、江湖气、古雅或口语化,但不能是无性格的说明腔。
3. 信息释放常常嵌在行动、对话、误判和小麻烦里,而不是用百科段落解释。
4. 场景推进靠连续的物理因果:看见/听见/闻到 -> 判断 -> 试探 -> 代价 -> 新问题。
5. 人物通过说话方式、注意力和小动作立住,不靠履历卡片。
6. 恐怖、悬疑、奇幻或权谋感来自可观察细节的递进,不来自抽象形容词。
7. 章节/场景结尾通常落在新问题、状态变化、反讽或未说破的情绪上。
8. 好句子服务人物、局势或气氛,不单独摆出来证明“文笔好”。

2. 写作前必须锁定的风格参数

每篇故事或每部中篇开写前,必须在 story bible / novel bible 中写明:

叙述人或 POV 的偏见:他/她相信什么、误判什么、看不惯什么。
语体来源:市井口语、职业语汇、古典感、少年感、冷幽默、民俗叙述、克制白描等。
句子节奏:短句压迫、长句滔滔、白描平稳、内心碎片、对话驱动。
感官主轴:气味、声音、触感、物件、身体反应、空间方向。
幽默方式:自嘲、误会、反差、嘴硬、职业黑话、冷处理。
情绪处理:压住、错开、延迟爆发、用行为替代告白。

没有这些参数,不得只写“文笔细腻”“像出版小说”“有网文爽感”。


3. 反 AI 腔核心规则

AI 腔的本质不是某个词,而是“用抽象总结替代具体经验”。以下模式必须重点清理:

抽象对称句:不是 X,而是 Y;与其说 X,不如说 Y。
顿悟句:他意识到;他终于明白;这一刻他才懂。
雾化句:某种难以言说的;仿佛有什么东西;空气凝固。
命题句:真正的恐惧/孤独/爱/命运是……
段尾总结:把刚发生的事升格成主题宣言。
情绪标签:恐惧、悲伤、愤怒、压抑、破碎等词反复出现,却没有身体和行动承载。

这些句式不是绝对零容忍。若用于具体语义、角色口吻或必要判断,可以保留;若用于抽象抒情、场景氛围、主题总结,必须改写。

硬门槛:

短篇全文:“不是……而是……”类机械对照句最好为 0,最多 1 处且必须有具体语义功能。
中篇单章:前三章最好为 0;其他章节最多 1 处。
同一稿中“他意识到/终于明白/某种难以言说/空气凝固/命运的齿轮”等模板句累计超过 2 处,必须专项返修。

4. 替换方法

4.1 把抽象判断改成身体和动作

坏:他感到的不是恐惧,而是一种更深的寒意。
改法:写心跳、手指、后颈、呼吸、视线回避、脚步迟疑,再让人物做一个错误或过度的动作。

4.2 把顿悟改成可观察证据

坏:他终于明白自己被骗了。
改法:让他发现账本页角、门闩方向、对方称呼错误、旧伤位置、物件不在原处,然后让下一句行动证明判断。

4.3 把设定解释改成“用一次”

坏:连续解释规则来源、历史和原理。
改法:角色在危险中使用规则;使用失败暴露限制;旁人质疑;代价落到身体、关系或资源上。

4.4 把漂亮段尾改成具体落点

坏:段尾总结主题、命运、孤独或人性。
改法:停在一个物件、一句没说完的话、一个动作后果、一个被误读的表情、一个新声音。

5. 场景质感检查

每个关键场景至少通过以下 5 项中的 4 项:

1. 第一三句内有具体压力、异常、行动或声音。
2. 至少一个细节只能由当前 POV 注意到,不能换人后仍完全成立。
3. 每 600-900 字有一次局势变化、信息增量或关系筹码变化。
4. 对话至少有一处“不正面回答”,角色通过绕开问题暴露真实需求。
5. 场景结尾改变读者问题:从“发生了什么”变成“接下来怎么办/他为什么这么做/代价会落到谁身上”。

6. 批评与返修用检查表

Critic、Reader、QA、Editor、Revision 必须检查:

这段是否可以删掉而不影响剧情、人物、气氛或信息?可以删则删。
这句是否只是把读者已经看见的情绪再解释一遍?是则删或改成动作。
这个角色的台词去掉名字后,是否还能听出是谁?不能则重写声音。
这一章是否有可复述的记忆点?没有则补一个具体物件、选择、反讽或恐惧规则。
这处“文笔好”是否让情节停下来?是则降调。
是否在用样例作品的专属设定、人物关系或桥段链偷懒?是则重做原创化。

Read the full file on GitHub · 146 lines

Changes

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.

  1. 7d ago First seen · 146 lines · 65 tokens per session scan A 52dca7f9be89

Subscribe to this mod's changes

exemplar-prose-calibration is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (106 stars, last pushed 2mo ago), licensed MIT. It adds 65 tokens to every session and 1,761 once invoked, about $0.0003 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 2026-09-03.

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