Agent skill · AI & Agents

knowledge-builder

内容工厂专用知识整理 Skill,把项目资料、素材和运营经验整理成场景生成与内容生产可用的事实输入。

limecloud1,464★ · 1 repos on radarProfile →
claude-code
Install
npx skills add limecloud/lime --skill knowledge-builder --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Path: lime-rs/resources/default-skills/knowledge-builder/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,464
Language: TypeScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 内容工厂知识整理 Skill 你是内容工厂的知识整理 Skill。你的职责是把项目资料变成可生产的事实底座,不是替 App 写普通总结。 ## 工作步骤 1. 识别输入中的三层知识:IP / 品牌人设、项目产品资料、内容运营素材。 2. 标记已确认事实、合理假设、缺失信息和生产风险。 3. 提炼目标人群、痛点、核心卖点、使用场景、决策阶段和内容角度。 4. 如任务要求场景地图,按平台和决策阶段扩展场景,并给出图片需求。 5. 输出可被内容工厂 App 回写的 workspace patch。 ## 输出要求 最终必须输出 JSON,顶层包含 `contentFactoryWorkspacePatch` 或 `workspacePatch`。patch 中至少包含: - `kind`: 固定为 `content_factory.workspace_patch` - `projectId`: 当前项目 ID - `projectKnowledge`: 结构化事实、假设、缺失项 - `sceneTable`: 如任务要求场景地图,包含场景行、维度、决策阶段和图片 brief - `evidence`: 资料来源、假设和待确认问题 - `skillEvidence`: 至少记录 `knowledge-builder` 的执行摘要 不要把知识写入 Skill 自身;Skill 只描述生产方法,知识事实应进入 App 的项目资产。

What's inside
Steps it walks through
  1. 工作步骤
  2. 输出要求
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About this skill
What does the knowledge-builder skill do?

内容工厂专用知识整理 Skill,把项目资料、素材和运营经验整理成场景生成与内容生产可用的事实输入。

How do I install it?

Run `npx skills add limecloud/lime --skill knowledge-builder --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From limecloud/lime, a repository with 1,464 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going