Token导航 LogoToken导航

Agent Skill · AI 工具

compound-learnings(复合学习)

compound-learnings 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 Codex、Claude、Cursor、Gemini CLI 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

compound-learnings 是AI 工具类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证为 MIT,第三方扫描有未通过项,按安装量在AI 工具类的 2,536 个 Skill 里排第 295。

总安装 7,660周安装 316GitHub Stars 3,705下载 2,503核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:compound-learnings(复合学习)
来源仓库:https://github.com/parcadei/continuous-claude-v3
仓库路径:skills/compound-learnings
安装命令:
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings

这个 Skill 是做什么的

AI 工具类 Skill 用来扩展助手本身的能力,例如接模型、接检索、接外部服务。

将近期会话中的错误、纠正和经验转化为永久能力提升机制。自动分析 learnings 文件,提炼可复用的规则与最佳实践。适用于希望 AI Agent 持续进化、减少同类错误的长期使用者。可结合缓存机制防止上下文重置导致知识丢失。安装前建议确认 .claude/cache/learnings/ 路径可写且无敏感信息。

来源仓库内路径:skills/compound-learnings

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

compound-learnings 怎么安装?

在终端执行 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

compound-learnings 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

compound-learnings 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings;npx skills add parcadei/continuous-claude-v3 --skill "compound-learnings" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

compound-learnings 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

compound-learnings 用的人多吗?

公开数据是累计安装 7,660 次,近一周安装 316 次,下载 2,503 次,GitHub 3,705 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

compound-learnings 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 来源仓库有 3,705 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理AI 工具类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

AI 工具类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
executing-plans

执行计划 · Codex、Claude

1,515,336173,110只读MIT通过
verification-before-completion

完成前的验证 · Codex、Claude

1,319,472173,121权限未标注MIT通过
subagent-driven-development

子 Agent 驱动开发 · Codex、Claude

1,312,416173,138只读MIT通过
copy-editing

复制编辑 · Codex、Claude

1,209,57625,632只读MIT通过
paywall-upgrade-cro

付费专区升级 cro · Codex、Claude

1,030,17625,617只读MIT通过
compound-learnings

复合学习 · 本页

7,6603,705执行命令MIT未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Compound Learnings

Transform ephemeral session learnings into permanent, compounding capabilities.

When to Use

  • "What should I learn from recent sessions?"
  • "Improve my setup based on recent work"
  • "Turn learnings into skills/rules"
  • "What patterns should become permanent?"
  • "Compound my learnings"

Process

Step 1: Gather Learnings

# List learnings (most recent first)
ls -t $CLAUDE_PROJECT_DIR/.claude/cache/learnings/*.md | head -20

# Count total
ls $CLAUDE_PROJECT_DIR/.claude/cache/learnings/*.md | wc -l

Read the most recent 5-10 files (or specify a date range).

Step 2: Extract Patterns (Structured)

For each learnings file, extract entries from these specific sections:

Section HeaderWhat to Extract
## Patterns or Reusable techniquesDirect candidates for rules
**Takeaway:** or **Actionable takeaway:**Decision heuristics
## What WorkedSuccess patterns
## What FailedAnti-patterns (invert to rules)
## Key DecisionsDesign principles

Build a frequency table as you go:

| Pattern | Sessions | Category |
|---------|----------|----------|
| "Check artifacts before editing" | abc, def, ghi | debugging |
| "Pass IDs explicitly" | abc, def, ghi, jkl | reliability |

Step 2b: Consolidate Similar Patterns

Before counting, merge patterns that express the same principle:

Example consolidation:

  • "Artifact-first debugging"
  • "Verify hook output by inspecting files"
  • "Filesystem-first debugging" → All express: "Observe outputs before editing code"

Use the most general formulation. Update the frequency table.

Step 3: Detect Meta-Patterns

Critical step: Look at what the learnings cluster around.

If >50% of patterns relate to one topic (e.g., "hooks", "tracing", "async"): → That topic may need a dedicated skill rather than multiple rules → One skill compounds better than five rules

Ask yourself: *"Is there a skill that would make all these rules unnecessary?"*

Step 4: Categorize (Decision Tree)

For each pattern, determine artifact type:

Is it a sequence of commands/steps?
  → YES → SKILL (executable > declarative)
  → NO ↓

Should it run automatically on an event (SessionEnd, PostToolUse, etc.)?
  → YES → HOOK (automatic > manual)
  → NO ↓

Is it "when X, do Y" or "never do X"?
  → YES → RULE
  → NO ↓

Does it enhance an existing agent workflow?
  → YES → AGENT UPDATE
  → NO → Skip (not worth capturing)

Artifact Type Examples:

PatternTypeWhy
"Run linting before commit"Hook (PreToolUse)Automatic gate
"Extract learnings on session end"Hook (SessionEnd)Automatic trigger
"Debug hooks step by step"SkillManual sequence
"Always pass IDs explicitly"RuleHeuristic

Step 5: Apply Signal Thresholds

OccurrencesAction
1Note but skip (unless critical failure)
2Consider - present to user
3+Strong signal - recommend creation
4+Definitely create

Step 6: Propose Artifacts

Present each proposal in this format:

---

## Pattern: [Generalized Name]

**Signal:** [N] sessions ([list session IDs])

**Category:** [debugging / reliability / workflow / etc.]

**Artifact Type:** Rule / Skill / Agent Update

**Rationale:** [Why this artifact type, why worth creating]

**Draft Content:**
\`\`\`markdown
[Actual content that would be written to file]
\`\`\`

**File:** `.claude/rules/[name].md` or `.claude/skills/[name]/SKILL.md`

---

Use AskUserQuestion to get approval for each artifact (or batch approval).

Step 7: Create Approved Artifacts

For Rules:
# Write to rules directory
cat > $CLAUDE_PROJECT_DIR/.claude/rules/<name>.md << 'EOF'
# Rule Name

[Context: why this rule exists, based on N sessions]

## Pattern
[The reusable principle]

## DO
- [Concrete action]

## DON'T
- [Anti-pattern]

## Source Sessions
- [session-id-1]: [what happened]
- [session-id-2]: [what happened]
EOF
For Skills:

Create .claude/skills/<name>/SKILL.md with:

  • Frontmatter (name, description, allowed-tools)
  • When to Use
  • Step-by-step instructions (executable)
  • Examples from the learnings

Add triggers to skill-rules.json if appropriate.

For Hooks:

Create shell wrapper + TypeScript handler:

# Shell wrapper
cat > $CLAUDE_PROJECT_DIR/.claude/hooks/<name>.sh << 'EOF'
#!/bin/bash
set -e
cd "$CLAUDE_PROJECT_DIR/.claude/hooks"
cat | node dist/<name>.mjs
EOF
chmod +x $CLAUDE_PROJECT_DIR/.claude/hooks/<name>.sh

Then create src/<name>.ts, build with esbuild, and register in settings.json:

{
  "hooks": {
    "EventName": [{
      "hooks": [{
        "type": "command",
        "command": "$CLAUDE_PROJECT_DIR/.claude/hooks/<name>.sh"
      }]
    }]
  }
}
For Agent Updates:

Edit existing agent in .claude/agents/<name>.md to add the learned capability.

Step 8: Summary Report

## Compounding Complete

**Learnings Analyzed:** [N] sessions
**Patterns Found:** [M]
**Artifacts Created:** [K]

### Created:
- Rule: `explicit-identity.md` - Pass IDs explicitly across boundaries
- Skill: `debug-hooks` - Hook debugging workflow

### Skipped (insufficient signal):
- "Pattern X" (1 occurrence)

**Your setup is now permanently improved.**

Quality Checks

Before creating any artifact:

  1. Is it general enough? Would it apply in other projects?
  2. Is it specific enough? Does it give concrete guidance?
  3. Does it already exist? Check .claude/rules/ and .claude/skills/ first
  4. Is it the right type? Sequences → skills, heuristics → rules

Files Reference

  • Learnings: .claude/cache/learnings/*.md
  • Skills: .claude/skills/<name>/SKILL.md
  • Rules: .claude/rules/<name>.md
  • Hooks: .claude/hooks/<name>.sh + src/<name>.ts + dist/<name>.mjs
  • Agents: .claude/agents/<name>.md
  • Skill triggers: .claude/skills/skill-rules.json
  • Hook registration: .claude/settings.json → hooks section

平台分布

Claude Code

29.55%
按下载量换算740

OpenCode

22.84%
按下载量换算572

Gemini CLI

15.97%
按下载量换算400

Codex

12.04%
按下载量换算301

Antigravity

7.97%
按下载量换算199

Cursor

3.3%
按下载量换算83

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/parcadei/continuous-claude-v3 --skill compound-learnings;npx skills add parcadei/continuous-claude-v3 --skill "compound-learnings" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

继续浏览同类 Skills