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SKILL verified MIT Self-run

L2 Capture

skill-cloud99277-kitclaw-l2-capture · by cloud99277

Capture concise L2 Whiteboard memory entries on top of memory-manager. Use when the user says "记到L2", "写入白板", "提炼成 L2 记忆", "记下这个决策", or wants 1-3 shared decision/action/learning entries extracted from the current task or conversation. Prefer this over calling memory-update.py directly for normal L2 writes.

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Install

$ agentstack add skill-cloud99277-kitclaw-l2-capture

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

l2-capture

为共享 L2 Whiteboard 提供一个薄封装写入入口。

它做四件事：

  1. 接收原始文本或已经标注好的候选条目
  2. 规范成 decision / action / learning
  3. 先做去重和质量检查
  4. 串行调用 memory-manager 完成最终写入

不要用它写 L3 文档；稳定文档仍然进入 Obsidian 知识库。

什么时候用

  • 用户明确说“记到 L2 / 写入白板 / 记下这个决策 / 记下这个待办”
  • 要从当前任务或对话中提炼 1-3 条共享记忆
  • 需要避免直接调用 memory-update.py 时的并发与重复问题

快速开始

先 dry-run 看候选条目：

python3 ~/.ai-skills/l2-capture/scripts/l2_capture.py \
  --project agent-toolchain \
  --from-text "[decision] 共享稳定知识统一落到 20_Knowledge_Base"

第二版也支持从原始总结里自动提炼：

python3 ~/.ai-skills/l2-capture/scripts/l2_capture.py \
  --project agent-toolchain \
  --from-text "我们决定把共享稳定知识统一放到 20_Knowledge_Base。后续需要单独评估 Git 化方案。实测下来 L3 更适合目录监听自动入库。"

确认后写入：

python3 ~/.ai-skills/l2-capture/scripts/l2_capture.py \
  --project agent-toolchain \
  --from-text "[decision] 共享稳定知识统一落到 20_Knowledge_Base" \
  --apply

支持多条：

python3 ~/.ai-skills/l2-capture/scripts/l2_capture.py \
  --project agent-toolchain \
  --from-text "
[decision] OpenClaw 私有记忆不覆盖共享事实
[action] 单独评估 20_Knowledge_Base 的 Git 化方案
[learning] L3 适合目录监听自动入库
" \
  --apply

如果原始文本只对应一条，给定类型即可：

python3 ~/.ai-skills/l2-capture/scripts/l2_capture.py \
  --project agent-toolchain \
  --type learning \
  --from-text "L3 适合目录监听自动入库；L2 更适合结构化短条目写入。" \
  --apply

输入规则

  • 优先使用显式标记：
  • [decision] ...
  • [action] ...
  • [learning] ...
  • 也支持：
  • decision: ...
  • action: ...
  • learning: ...
  • 如果只有一条内容，可以配合 --type
  • 如果没有显式标记，也会尝试自动提炼 1-3 条候选

自动提炼规则

  • 优先从一句话里识别 decision / action / learning
  • 会按关键词和句首信号做启发式分类
  • 返回 source_mode=auto 和命中的 signals
  • 如果内容太泛、太长，仍然会被过滤或要求你显式标注

默认行为

  • 默认是 dry-run
  • 只有加 --apply 才真正写入 ~/.ai-memory/whiteboard.json
  • 写入前会读取现有 whiteboard 做重复检查
  • 真正落盘时会串行写入，避免多条并发写 whiteboard 失败

和 memory-manager 的分工

  • memory-manager：底层读写与检索统一入口
  • l2-capture：更方便的 L2 写入入口

需要了解 L2 规则时，读取：

  • ~/.ai-skills/memory-manager/references/whiteboard-template.md

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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