content-style-calibration-loop

name	content-style-calibration-loop
description	Use when a user edits or criticizes generated copy and you need to extract durable writing-style rules instead of merely remembering surface words.
allowed-tools	["Read","Write","Edit","AskUserQuestion"]
metadata	{"trigger":"用户手改文案、说不像我、太正经、太编辑、没网感、太 AI、需要把风格反馈沉淀成 skill/reference","source":"local"}

Content Style Calibration Loop

Purpose

This skill turns user edits into reusable style rules. It prevents the agent from doing the shallow version of learning:

用户用了几个词 → 下次多塞这几个词

The correct goal is:

用户改了什么关系、视角、节奏、风险边界或人群钉子 → 下次按这个写作判断工作

Use this skill after the user edits generated text, rejects a tone, or says a draft does not sound like them.

When To Use

Use it when the user:

• edits a model-written paragraph and asks you to learn the style;
• says 不像我, 太编辑, 太 AI, 太正经, 太抽象, 没网感, 像 README, or 像发布会;
• distinguishes between a surface feature and the real rule, e.g. 不是多用这些词，是要有网感;
• wants the lesson added to a skill, reference, README, or workflow;
• is calibrating a recurring content voice for Xiaohongshu, Douyin, WeChat, scripts, captions, or other public copy.

Do not use it to preserve raw private conversations. Extract rules, not transcripts.

Calibration Workflow

1. Capture the before/after pair

Get the model draft and the user-edited version. If the original is not available, still analyze the user edit, but mark the missing comparison.

Completion criterion: you can point to at least one changed phrase, deleted structure, or added line.

2. Diff meaning, not just wording

Classify each important change:

Change type	Question
Voice	Did the user make it rougher, sharper, calmer, more local, more personal?
Viewpoint	Did the user move from expert explanation to participant experience?
Relationship	Did the user show what the tool/project does to a person?
Specificity	Did the user replace generic terms with an incident, object, role, or scene?
Rhythm	Did the user shorten, fragment, add breath, or keep a deliberate rough edge?
Risk boundary	Did the user remove over-cautious disclaimers or add factual anchors?
Platform fit	Did the user make it more suitable for XHS/Douyin/WeChat/etc.?

Completion criterion: every accepted user edit is mapped to at least one reason.

3. Extract rules, not word lists

Bad extraction:

User likes 疑似、绷不住、抽象.

Better extraction:

User wants high-intensity internet-native judgment: identify the absurd point in a real situation, then say it with half-joke/half-accurate technical self-mockery. Internet words may appear, but should not be forced.

Completion criterion: the rule would still work if none of the original words were reused.

4. Keep the strongest examples

Store 1-3 compact before/after examples or pattern examples. Do not store entire chat logs, platform metrics, credentials, personal directories, or private context.

Completion criterion: each example demonstrates a rule and is safe to publish or share.

5. Decide the destination

Use the smallest durable destination that will affect future behavior:

Lesson type	Destination
User preference that should follow all sessions	memory / user profile
Account-specific writing style	account style guide or content repo reference
Reusable agent procedure	skill
One-off task state	session notes or current repo files, not memory
Raw operational data	operations repo only, not public skills

Completion criterion: the lesson is stored where the next relevant agent/tool can read it.

6. Verify the next prompt can use it

Write a tiny future-use prompt fragment, for example:

Use the calibrated style rule: websense comes from identifying the absurd point, not stuffing meme words. Keep technical anchors real, but open with the human friction.

Completion criterion: the prompt fragment is specific enough to change a future draft.

Output Format

When reporting a calibration result, use:

# 风格校准结论

## 1. 用户改动

## 2. 改动背后的规则

## 3. 下次写作要做

## 4. 下次写作不要做

## 5. 可沉淀位置

## 6. 已沉淀 / 待用户确认

Keep it short. The goal is not literary analysis; the goal is future behavior change.

Example

User changes:

我就是那个有无数想法、但被上班榨到一行代码都不想写的技术中登。

into:

轻松干翻我这种有无数想法、但被上班榨到一行代码都不想写的技术中登。

Do not extract:

Use the phrase 技术中登 more.

Extract:

Do not merely introduce the persona. Show the power relationship: the tool/AI/project has so much energy that it embarrasses, replaces, overwhelms, or exposes the person who was supposed to control it.

Public-Skill Hygiene

When turning a private calibration into a public skill:

• Keep general rules and safe examples.
• Remove platform backend data, engagement metrics, account IDs, session IDs, local paths, cron IDs, cookies, screenshots, and raw comments.
• Do not quote large private conversation chunks.
• Do not publish a user's personal biography unless they explicitly made it part of the public account voice.
• Prefer user-edited sample / confirmed style sample wording over private chat provenance.

Common Pitfalls

1. Learning the vocabulary instead of the judgment. If the user says 不是这些词，是网感, immediately rewrite the rule around judgment.
2. Overfitting one post. A high-performing post gives a hypothesis, not universal law. Record it as a style pattern to test.
3. Saving too much. Skills should contain compact rules and examples, not raw drafts, raw chats, or platform data dumps.
4. Skipping verification. A calibration is not complete until it produces a prompt fragment, checklist item, or skill/reference update that future agents can actually use.
5. Letting the editor override the user. The agent may explain why an edit works, but the user's taste is the final style signal for their own account.