Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/cloud99277/kitclaw/l3-syncnpx skills add cloud99277/KitClaw --skill l3-syncgit clone --depth 1 https://github.com/cloud99277/KitClawWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00090 | $0.00627 |
| Opus 5 | $0.00045 | $0.00313 |
| Sonnet 5 | $0.00018 | $0.00125 |
| Haiku 4.5 | $0.00009 | $0.00063 |
Grade A, and why
l3-sync scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
l3-sync — L3 知识库自动索引
监控 Markdown 知识库目录的文件变更，自动触发增量 RAG 索引更新。
适用场景
- Obsidian 编辑后自动更新搜索索引
- 知识库目录有变更时自动增量索引
- 后台守护进程持续监听
工作原理
文件系统变更（inotify / 轮询）
→ 防抖（5s 窗口批量合并变更）
→ knowledge_index.py --update <目录>
→ 观测日志写入 skill-observability
使用方法
单次增量索引（手动触发）
# 读取配置，对所有 l3_paths 做一次增量索引
python3 ~/.ai-skills/l3-sync/scripts/index_watcher.py --once
启动后台监听（推荐）
# 前台运行（调试用）
python3 ~/.ai-skills/l3-sync/scripts/index_watcher.py --watch
# 后台运行
nohup python3 ~/.ai-skills/l3-sync/scripts/index_watcher.py --watch &
用 systemd 管理（生产环境）
# 安装 systemd user service
bash ~/.ai-skills/l3-sync/scripts/install_service.sh
# 管理
systemctl --user start l3-sync
systemctl --user status l3-sync
systemctl --user stop l3-sync
配置
监听路径从 ~/.ai-memory/config.json 的 l3_paths 字段读取：
{
"l3_paths": [
"/mnt/e/Cloud927/.../20_Knowledge_Base"
]
}
与其他 Skill 的关系
- knowledge-search — 搜索已索引的 L3。l3-sync 确保索引是最新的。
- knowledge_index.py — 索引引擎。l3-sync 是它的触发器。
- conversation-distiller — 对话 → L3 文档。写入后 l3-sync 自动增量索引。
依赖
- RAG 引擎已安装（
bash install.sh --with-rag） - Linux inotify 支持（WSL 有；纯轮询模式也支持，但延迟更高）
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 84 lines · 90 tokens per session scan A 8a1703b5256a
l3-sync is a skill published in the GitHub repository cloud99277/KitClaw (5 stars, last pushed 4mo ago), licensed MIT. It adds 90 tokens to every session and 627 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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