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/install expression-layer
Description
[何时使用]当需要内容生成、格式转换或多形式输出时触发。支持直接输入问题/素材/链接,无需前置思考层。统一路由至 ljg-skills 及发布工具。
README (SKILL.md)
expression-layer: 表达层 🎨
📋 功能描述
帮助用户统一调度内容生成与可视化输出。不依赖前置思考层,直接接收问题/素材/链接,按意图路由至对应 skill,支持单步/串联/并行/发布编排。
适用场景:
- 直接问概念/问题 → 口语化解释/深度文章
- 提供素材/草稿 → 写作 + 人性化 + 多形式输出
- 论文/单词/城市 → 解读 + 可视化卡片
- 完整文章 → 公众号发布
边界条件:
- 不负责深度分析/数据查询(由上游工作流或思考层提供)
- 输出格式依赖上游指定或意图自动识别
- 新增 skill 需更新路由矩阵
🔄 路由编排矩阵
| 输入类型 | 触发意图 | 编排路径 | 输出形式 | 典型场景 |
|---|---|---|---|---|
| 专业概念/问题 | plain |
ljg-plain |
大白话文本(≤200字) | “什么是PE?说人话” |
| 观点/素材/草稿 | writes |
ljg-writes → humanizer-zh |
深度文章(1000-1500字) | “把这段写成公众号文章” |
| 任何文本/数据 | card |
ljg-card(-l/-i/-c/-w/-b) |
PNG 卡片 | “做个信息图/漫画/大字” |
| 文本/大纲 | present |
ljg-present |
HTML 高桥流 | “做成演讲PPT” |
| 论文链接/PDF | paper_flow |
ljg-paper → ljg-card |
解读Markdown + PNG | “读论文并做漫画卡片” |
| 英文单词 | word_flow |
ljg-word → ljg-card |
解析Markdown + PNG | “解词并做信息图” |
| 城市/主题 | travel |
ljg-travel |
研究报告 + PNG卡片 | “做西安旅行功课” |
| 完整文章/解读 | wechat |
wechat-publisher |
公众号推文(HTML+封面) | “发到公众号” |
编排模式:
单步:直接路由到 1 个 skill串联:A 输出 → B 输入(如ljg-paper → ljg-card)并行:同时生成多个形式(如 大白话 + 卡片 + 文章)发布:内容 →wechat-publisher→ 公众号
⚠️ 常见错误
错误 1:强行附加思考层
问题:
• 用户只问“什么是定投”,却先跑降秩/追本分析
• 输出冗长,偏离直接表达需求
解决:
✓ 表达层是独立入口,不依赖思考层
✓ 直接按意图路由到 ljg-plain / ljg-writes
✓ 如需深度分析,由上游工作流调用,非表达层职责
错误 2:忽略输出格式指定
问题:
• 只给内容,不指定输出形式
• 路由歧义(该出文章还是卡片?)
解决:
✓ 优先按关键词自动识别意图(如“说人话”→plain,“做卡片”→card)
✓ 歧义时主动询问:“需要大白话解释、深度文章,还是可视化卡片?”
✓ 支持并行输出:plain + card + writes
错误 3:新增 skill 未更新路由
问题:
• 安装了新 skill,但路由矩阵未更新
• 表达层无法调度新能力
解决:
✓ 新增 skill 后,同步更新 SKILL.md 路由矩阵
✓ 在 references/orchestration-matrix.md 维护完整映射
✓ 提交版本升级(patch version)
🧪 使用示例
输入:
什么是定投?说人话,顺便做个卡片。
预期输出:
- 识别意图:
multi(plain + card) - 编排:并行执行
ljg-plain+ljg-card -i - 输出:大白话文本 + PNG 信息图
输入:
读这篇论文 https://arxiv.org/abs/xxx,做成漫画卡片。
预期输出:
- 识别意图:
paper_flow - 编排:串联执行
ljg-paper→ljg-card -c - 输出:论文解读 Markdown + 漫画风格 PNG
输入:
把刚才的消费分析写成公众号文章发出去。
预期输出:
- 识别意图:
wechat - 编排:
ljg-writes→humanizer-zh→wechat-publisher - 输出:公众号草稿(已排版+封面+合规检查)
🔧 故障排查
| 问题 | 检查项 |
|---|---|
| 不触发 | description 是否包含触发词?用户输入是否匹配意图? |
| 路由错误 | 意图识别是否准确?是否歧义未询问? |
| 输出为空 | 上游 skill 是否安装?(ljg-plain/ljg-card/wechat-publisher) |
| 新增 skill 不调用 | 路由矩阵是否更新?references 是否同步? |
| 格式错误 | 输出格式是否指定?并行输出是否超出上下文限制? |
🔗 相关资源
- 路由编排矩阵:
references/orchestration-matrix.md - 投教工作流编排分析:
references/ie-orchestration.md - 输出模板:
templates/output-template.md - 标准参考:
docs/SKILL-STANDARD-v3.md
Usage Guidance
This skill is essentially a routing/orchestration document and is internally coherent. Before installing, review and trust the downstream skills it will call (ljg-plain, ljg-writes, ljg-card, humanizer-zh, wechat-publisher, etc.), because those components are the ones that will access networks, APIs, or credentials (for example, publishing to a WeChat account requires publisher credentials held by the wechat-publisher skill). Also note the SKILL.md allows tools like Bash/Exec — the routing doc does not itself run shell commands, but ensure your agent's tool permissions are appropriately scoped. If you need higher assurance, test the skill in a restricted sandbox and inspect the actual ljg-* and wechat-publisher skills (their SKILL.md, any install specs, and required env vars) before granting access to production credentials. If additional files or instructions are added that read environment variables, files in ~/, or contact non-official endpoints, re-evaluate as those would be suspicious.
Capability Analysis
Type: OpenClaw Skill
Name: expression-layer
Version: 1.0.0
The 'expression-layer' skill acts as a functional orchestration and routing component designed to coordinate content generation and visualization tasks. While it requests high-privilege tools such as 'Bash' and 'Exec' in SKILL.md, these are aligned with its stated purpose of executing sub-skills and publishing tools. The bundle contains no evidence of malicious intent, data exfiltration, or harmful prompt injection; it focuses entirely on mapping user intents to specific workflow paths.
Capability Assessment
Purpose & Capability
The name/description (expression-layer / 表达层) match the manifest and SKILL.md: the skill's job is to route intents to ljg-plain, ljg-writes, ljg-card, wechat-publisher, etc. The clawhub.yaml lists those dependencies. There are no unrelated environment variables, binaries, or install steps requested that would be disproportionate to an orchestration/router role.
Instruction Scope
SKILL.md contains only routing, intent-mapping, examples, and troubleshooting instructions; it does not instruct reading local secrets or contacting arbitrary external endpoints. However, runtime behavior depends entirely on the downstream skills it invokes (ljg-*, humanizer-zh, wechat-publisher). Those child skills may require credentials or perform network interactions; expression-layer’s instructions assume those skills exist and are trusted.
Install Mechanism
There is no install spec and no code files that would be written/executed during install. This is the lowest-risk pattern (instruction-only). The package metadata references other skills but does not pull external archives or run installers itself.
Credentials
The skill does not declare or require any environment variables, credentials, or config paths. That is proportionate to its documented role as a router. Be aware that downstream skills (e.g., wechat-publisher) are likely to need credentials; those are not requested by this skill itself.
Persistence & Privilege
always is false and model invocation is allowed (platform default). The skill does not request permanent system-wide changes or modifications to other skills' configs. Autonomous invocation is permitted by default but not an extra privilege in this manifest.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install expression-layer - After installation, invoke the skill by name or use
/expression-layer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
v1.0.0: 统一的内容生成与可视化输出协调器,意图驱动路由,支持单步/串联/并行/发布编排
Metadata
Frequently Asked Questions
What is 表达层?
[何时使用]当需要内容生成、格式转换或多形式输出时触发。支持直接输入问题/素材/链接,无需前置思考层。统一路由至 ljg-skills 及发布工具。 It is an AI Agent Skill for Claude Code / OpenClaw, with 73 downloads so far.
How do I install 表达层?
Run "/install expression-layer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 表达层 free?
Yes, 表达层 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 表达层 support?
表达层 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 表达层?
It is built and maintained by lj22503 (@lj22503); the current version is v1.0.0.
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