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 skills add cafe3310/public-agent-skills --skill long-audio-to-obsidiangit clone --depth 1 https://github.com/cafe3310/public-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cafe3310/public-agent-skills/long-audio-to-obsidian)<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/long-audio-to-obsidian"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/long-audio-to-obsidian/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/long-audio-to-obsidian"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/long-audio-to-obsidian.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00035 | $0.01493 |
| Opus 5 | $0.00017 | $0.00746 |
| Sonnet 5 | $0.00007 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
long-audio-to-obsidian 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Long Audio to Obsidian Archiver
此技能将散乱的语音转写项目文件整理成结构清晰的 Markdown 文档,便于在 Obsidian 等知识库中归档。
它采用 "Agent 规划 + 脚本执行" 的模式:
- Agent (你):负责理解目录结构、识别文件类型、对分段文件进行逻辑排序(例如按行号 1-200, 201-400),并生成合并计划。
- Script:负责根据你的计划,机械地读取文件并生成 Markdown。
使用场景
当用户要求将一个包含多个原始音频、数十个转写分段、以及各种说明文档的目录“整理归档”时使用。
工作流 (Workflow)
1. 调查与分析 (Survey & Analyze)
首先,使用 list_directory (必要时配合 glob) 扫描目标项目目录。
你需要回答以下问题:
- 哪些是原始录音文本?(通常在
1-原始文件或类似目录) - 哪些是最终输出的分段脚本?(通常在
5-最终输出,文件名包含行号如_1-200)- 关键任务:你需要根据文件名中的数字对它们进行正确排序。
- 哪些是项目元数据?(日志、术语表、背景要求等)
- 哪些是衍生知识/问答?(
5-最终输出中非分段的 Markdown 文件)
2. 生成合并配置 (Generate Configuration)
基于你的分析,构建一个 JSON 配置文件。不要直接输出给用户,而是准备写入临时文件。
JSON 结构定义:
{
"output_dir": "目标项目路径/obsidian_archive",
"tasks": [
{
"filename": "1-原始录音汇总.md",
"title": "原始录音汇总",
"sections": [
{
"title": "文件1.txt",
"path": "绝对路径或相对路径/文件1.txt",
"syntax": "text"
}
]
},
{
"filename": "2-最终输出脚本汇总.md",
"title": "最终输出脚本汇总",
"sections": [
{
"title": "Part 1 (1-200)",
"path": "路径/output_1-200.txt",
"syntax": "text"
},
{
"title": "Part 2 (201-400)",
"path": "路径/output_201-400.txt",
"syntax": "text"
}
// Agent 必须确保这里的顺序是正确的!
]
}
// ... 继续为“整理要求汇总”和“知识整理汇总”创建任务
]
}
3. 执行合并 (Execute)
-
使用
write_file将 JSON 配置保存为临时文件,例如merge_plan.json。 -
调用脚本执行合并:
python3 .gemini/skills/long-audio-to-obsidian/scripts/doc_merger.py merge_plan.json(注意:请根据实际安装位置调整脚本路径)
-
检查脚本输出,确认生成成功。
-
删除临时文件
merge_plan.json。
4. 优化命名与生成索引 (Rename & Index)
脚本生成的只是标准文件名的中间产物。为了方便 Obsidian 使用,你需要进行最后一步的人工智能优化:
- 确定项目标题:根据原始目录名或核心文件名,提取一个清晰的项目标题(例如
2024-01-01 会议转写 - 项目 A)。 - 重命名文件:使用
run_shell_command的mv命令,将obsidian_archive中的标准文件重命名为带前缀的格式。1-原始录音汇总.md->{标题} 1 原始录音汇总.md2-最终输出脚本汇总.md->{标题} 2 最终输出脚本汇总.md- ...以此类推。
- 生成索引页:
- 创建一个名为
{标题}.md的新文件。 - 内容应包含指向上述所有文件的 Obsidian WikiLinks (
[[文件名]])。 - 如果有关联的外部产品文档(未合并进来的),也可以在此处添加链接。
- 创建一个名为
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.
- 13d ago First seen · 134 lines · 35 tokens per session scan A 234a5b7f4bd3
long-audio-to-obsidian is a skill published in the GitHub repository cafe3310/public-agent-skills (253 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 35 tokens to every session and 1,493 once invoked, about $0.0002 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 2026-08-30.
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