# Create Agent

> Distill a user's idea, viewpoint, or method into a reusable social-agent skill package that acts as an expression agent on their behalf.

- Skill: `foryourhealth111-pixel/create-agent` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add foryourhealth111-pixel/create-agent`
- Raw SKILL.md: https://api.skillmd.com/api/skills/foryourhealth111-pixel/create-agent/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: foryourhealth111-pixel (https://skillmd.com/u/foryourhealth111-pixel)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/foryourhealth111-pixel/create-agent

---


> Language policy:
> Detect the user's language from the first message and keep that language for the whole run.
> 本技能根据用户首条消息自动选择中文或英文，并全程保持同一种语言。

# 赛博嘴替.skills / create-agent（V2）

## 产品定义

- 本技能用于生成 `social-agent skill package`，不是角色扮演或人物复刻工具。
- 品牌入口是 `赛博嘴替.skills`，命令入口是 `/create-agent`。
- 产物是一个可复用的 `expression agent`：代表用户解释其想法、观点或方法。
- 输入模式固定为 `interview-first, attachments-assisted`：
  - 访谈是主输入。
  - 附件用于证据、例子、语气校准，不是启动前置条件。
- 主场景是 `解释想法`：讲清楚、换受众表达、纠正误解、在对方没理解时继续解释。

## 触发条件（中文）

当用户说以下任一内容时启动：

- `/create-agent`
- `帮我做一个表达代理`
- `我想蒸馏一个想法代理`
- `帮我做一个能替我解释想法的 skill`

当用户在已创建代理上要求增量更新时：

- `我有新材料` / `追加`
- `这不对` / `应该改成`
- `/update-agent {slug}`

当用户说 `/list-agents` 时，列出所有代理。
当用户说 `/delete-agent {slug}` 时，执行删除流程（需确认）。

## 操作契约（中文）

### 1) 创建流程（必须遵守）

1. 将 `prompts/intake.md` 作为唯一权威 intake 脚本执行（按脚本中的三轮 batched 提问与处理规则）。
2. 每轮都执行“整轮提问 -> 用户长回答 -> 本轮摘要 -> 轻确认后再进入下一轮”。
3. 三轮结束后，使用 `prompts/preview.md` 作为唯一权威 preview 脚本输出 4 类预览并请求修正。
4. 附件可在 Round 1/2 提前接收；但默认在三轮完成前不做深度消费，仅在“需要立即澄清 seed”时提前使用。
5. 预览确认后，继续补充使用附件中的例子、反例、术语与语气证据；若无附件仍可继续生成。
6. 生成代理包并写入目录。
7. 返回调用方式与可管理命令。

### 2) 三轮访谈摘要（仅作参考，操作以 `prompts/intake.md` 为准）

Round 1: 表达目标

- 你希望代理替你表达什么 seed？
- 这件事最希望对方带走的 1-3 个结论是什么？
- 常见使用场景是什么？

Round 2: 误解与纠偏

- 对方通常在哪些点误解你？
- 你通常如何重讲？
- 哪个例子/反例最有效？

Round 3: 互动风格

- 代理默认应当多直接、多耐心、多结构化？
- 面对不同理解水平如何切换表达方式？
- 哪些表达方式必须避免？

### 3) 目标产物形态

生成完成后，默认写入 `./agents/{slug}/`，产物包含：

- `SKILL.md`：可直接调用的代理入口
- `intent.md`：表达目标、边界、非目标
- `core_view.md`：核心观点与推理主线
- `explanation_routes.md`：多条稳定解释路径
- `audience_translation.md`：面向不同受众的改写策略
- `interaction_contract.md`：互动行为与提问策略
- `style_overrides.md`：语气/深度/结构等可调参数
- `misunderstandings.md`：高频误解与纠偏动作
- `expression_assets.md`：可复用比喻、例子、反例、短句模板
- `meta.json`：版本、时间戳、来源摘要

### 3.1) V2 Builder 构建顺序（操作性约束）

在 intake 三轮完成、preview 修正完成（confirmed preview）、并完成附件补充使用后，必须按以下顺序构建 V2 包：

1. `intent_builder`
2. `core_view_builder`
3. `explanation_routes_builder`
4. `audience_translation_builder`
5. `interaction_contract_builder`
6. `style_overrides_builder`
7. `misunderstandings_builder`
8. `expression_assets_builder`

一致性锚点（强约束）：
- 所有 builders 必须对齐已确认的 intake summaries + confirmed preview。
- Builders `do not invent` new conclusions beyond confirmed material.
- 若证据不足，使用各 builder 的 empty-state fallback，而不是补造结论。

### 4) 进化流程

对 `/update-agent {slug}`：

1. 读取现有代理包。
2. 对“新增材料/补充信息”，将 `prompts/merger.md` 作为权威 merge 路由脚本执行。
3. 对“这不对/应该改成/这个说法不像我”类显式纠正，将 `prompts/correction_handler.md` 作为权威 correction 路由脚本执行。
4. 只做增量更新，不丢失已确认结论。
5. 更新 `meta.json` 的 `version` 与 `updated_at`。
6. 返回变更摘要（新增了什么、替换了什么、为什么）。

### 5) 管理命令

默认代理目录：

- `./agents/{slug}/`
- `list` / `update` 在默认 `./agents/` 视图下会自动识别同级 `./colleagues/` 旧产物，并按兼容对象处理。

`/list-agents`：

```bash
python3 ${CLAUDE_SKILL_DIR}/tools/skill_writer.py --action list --base-dir ./agents
```

`/update-agent {slug}`：

- 走“进化流程”，对指定代理做增量更新。

`/delete-agent {slug}`：

```bash
rm -rf agents/{slug}
```

执行删除前必须口头确认。

## Tool Rules（共享）

- 文本/Markdown/JSON/图片/PDF 读取：`Read`
- 附件解析或采集脚本：`Bash` 调用 `tools/` 下脚本
- 文档写入：`Write` / `Edit`
- 版本变更：更新 `meta.json`

若采集脚本失败：

- 先报告错误原因和可行替代方案
- 可退化为“仅访谈 + 手工附件读取”模式
- 不得因为缺少附件而拒绝创建

---

# English Version

## Product Contract

- This skill creates a `social-agent skill package`, not a roleplay clone.
- Front-door brand: `赛博嘴替.skills`; command surface: `/create-agent`.
- The generated artifact is an `expression agent`.
- Input model is `interview-first, attachments-assisted`.
- Primary scenario is `explain ideas` with audience adaptation and misunderstanding correction.

## Trigger Conditions

Start when the user says any of:

- `/create-agent`
- `Help me create an expression agent`
- `I want to distill an idea into an agent`
- `Build a skill that explains my idea for me`
- `Create an agent for this concept`

Evolution mode:

- `I have new material` / `append`
- `This is wrong` / `change this`
- `/update-agent {slug}`

List mode:

- `/list-agents`

Delete mode:

- `/delete-agent {slug}` (confirmation required)

## Operational Flow

1. Treat `prompts/intake.md` as the authoritative intake script and execute it round-by-round.
2. For each round, follow: batched prompts -> long-form user answer -> round summary -> light confirmation before proceeding.
3. After Round 3, treat `prompts/preview.md` as the authoritative preview script and request corrections across 4 categories.
4. Attachments may be offered early (Round 1/2), but defer deep attachment use until after the three rounds unless needed to clarify the seed immediately.
5. After preview confirmation, use attachments as supporting evidence/examples (or continue without attachments).
6. Generate the reusable agent package.
7. Return invocation and management commands.

### Interview Rounds (Reference Only; `prompts/intake.md` is operative)

Round 1: What should this agent express?

- What seed should be expressed?
- What are the top 1-3 takeaways?
- In which scenarios is this explanation needed?

Round 2: Why do people misunderstand it?

- Where do misunderstandings happen?
- How do you usually re-explain?
- Which examples/counterexamples work best?

Round 3: How should it interact?

- Preferred directness, patience, and structure?
- How to adapt for weaker/stronger listeners?
- What expression styles should be avoided?

### Target Package Shape

Generated agents are written to `./agents/{slug}/` by default.

- `SKILL.md`
- `intent.md`
- `core_view.md`
- `explanation_routes.md`
- `audience_translation.md`
- `interaction_contract.md`
- `style_overrides.md`
- `misunderstandings.md`
- `expression_assets.md`
- `meta.json`

### Operative V2 Builder Sequence

After intake rounds are complete, preview corrections are confirmed (`confirmed preview`), and attachment evidence is incorporated, build the V2 package in this exact order:

1. `intent_builder`
2. `core_view_builder`
3. `explanation_routes_builder`
4. `audience_translation_builder`
5. `interaction_contract_builder`
6. `style_overrides_builder`
7. `misunderstandings_builder`
8. `expression_assets_builder`

Consistency anchor (hard rule):
- Keep every builder aligned to confirmed intake summaries and confirmed preview.
- Builders `do not invent` conclusions that are not supported by confirmed material.
- Use empty-state fallback text when evidence is missing.

### Management Commands

`/list-agents`:

```bash
python3 ${CLAUDE_SKILL_DIR}/tools/skill_writer.py --action list --base-dir ./agents
```

`/update-agent {slug}`:

- Run incremental evolution update for the target agent.

`/delete-agent {slug}`:

```bash
rm -rf agents/{slug}
```

Require explicit confirmation before delete.

## Compatibility Notes

- Compatibility posture for V1: `soft migration`.
- Primary package path and command surface use `./agents/{slug}/` and `/create-agent`.
- Legacy `./colleagues/{slug}/` packages are compatibility inputs only, not the active output target.
- Expression-agent build orchestration now uses:
  - `prompts/intent_builder.md`
  - `prompts/core_view_builder.md`
  - `prompts/explanation_routes_builder.md`
  - `prompts/audience_translation_builder.md`
  - `prompts/interaction_contract_builder.md`
  - `prompts/style_overrides_builder.md`
  - `prompts/misunderstandings_builder.md`
  - `prompts/expression_assets_builder.md`
- Legacy persona/work prompt files are kept in place only as deprecated migration stubs.
- User-facing invocation target is `/{slug}`.
- Default package path is `./agents/{slug}/`.
- Default `list` and `update` flows also auto-detect same-level `./colleagues/{slug}/` packages and mark them as compatibility targets.