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 tars1230/douyin-favorites-to-knowledge --skill skillgit clone --depth 1 https://github.com/tars1230/douyin-favorites-to-knowledgeWrote 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/tars1230/douyin-favorites-to-knowledge/skill)<a href="https://agentmods.dev/skills/tars1230/douyin-favorites-to-knowledge/skill"><img src="https://agentmods.dev/badge/skills/tars1230/douyin-favorites-to-knowledge/skill/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/tars1230/douyin-favorites-to-knowledge/skill"><img src="https://agentmods.dev/badge/skills/tars1230/douyin-favorites-to-knowledge/skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00092 | $0.02646 |
| Opus 5.5 | $0.00037 | $0.01058 |
| Sonnet 5.5 | $0.00018 | $0.00529 |
| Haiku 4.5 | $0.00009 | $0.00265 |
Grade A, and why
douyin-favorites-to-knowledge 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 9d 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.
This is a copy
100% identical to douyin-favorites-to-knowledge — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
抖音视频收藏转本地知识库
优先使用单入口流程。不要先向用户解释 schema、模式、provider 或 adapter。
首次使用
先检查命令是否存在：
douyin-favorites-knowledge --help
如果命令不存在，优先从国内镜像安装完整程序。选择用户确认的项目目录，不要替用户猜测长期存放位置：
git clone https://gitee.com/tars123/douyin-favorites-to-knowledge.git
cd douyin-favorites-to-knowledge
python3 -m venv .venv
. .venv/bin/activate
python -m pip install .
Gitee 不可用时再使用源码仓库 https://github.com/tars1230/douyin-favorites-to-knowledge。不要使用不明 GitHub 加速站。
安装完成后运行：
douyin-favorites-knowledge setup
让用户选择 Markdown 或 Obsidian 知识库目录，再让用户选择转录方案。推荐 SiliconFlow云端；本地 Whisper 只有用户愿意下载模型、承担本机资源时才选；none 只保存描述与链接。不要要求用户复制 Cookie。默认来源是收藏；仅当用户明确说“喜欢”或“点赞”时，向 login/sync 传 --source like。
用户明确选择 Obsidian 时，在已有 setup 后执行：
douyin-favorites-knowledge configure-obsidian --vault "用户选择的 Vault" --subdir "抖音知识库"
它会切换知识库目录并创建默认模板、日报索引和写入检查。不要猜测 Vault 位置。
飞书默认关闭且不影响本地同步。用户主动选择 webhook 通知时，让其自行把 webhook 放入安全环境变量 FEISHU_WEBHOOK_URL，随后运行 configure-feishu --mode webhook；不要把 URL 写入配置。用户选择已有或新建多维表时运行对应的 configure-feishu --mode bitable-existing|bitable-new 生成字段模板和授权提示。用户必须自行完成飞书登录、应用授权、建表或选表和共享权限；无授权时返回提示，不得假装多维表已经写入。
如果 Agent 在非交互环境执行，明确指定目录：
douyin-favorites-knowledge setup --knowledge-dir "用户确认的目录" --transcription siliconflow --skip-login
随后让用户在自己的终端运行 douyin-favorites-knowledge login 完成网页登录。不要替用户猜测知识库目录。
日常同步
douyin-favorites-knowledge sync
sync 将新增收藏静默写入知识库。首次配置完成后，日常同步不得要求用户逐条或逐次确认；终端只返回汇总结果。SQLite 防重账本、原子文件替换和内容冲突 fail-closed 仍必须保留。
登录失效时不打开登录页、直接失败时使用：
douyin-favorites-knowledge sync --no-login-prompt
--yes 只为旧脚本兼容保留，不再是同步前提。
每晚日报
首次 setup 和 login 成功后，若当前 Agent 支持定时任务，必须在用户本机创建并验证每天 23:00 的任务，执行：
douyin-favorites-knowledge daily --source collection --no-login-prompt
使用当前环境可用的任务调度器；创建后必须读取任务状态或执行一次 dry-run 验证，并向用户返回任务标识与用户时区。任务只运行已安装的命令，不传凭据，不弹出登录页。云端转录时，只有调度环境能从用户已有的 Secret Manager 或安全环境变量读取 SILICONFLOW_API_KEY（或可选 DASHSCOPE_API_KEY）才能创建任务；不能持久化读取或当前 Agent 没有 scheduler 能力时，必须明确报告“自动日报未启用”，不能制造必然失败的计划任务或暗示已启用。daily 静默同步并在知识库 日报/ 写入当天 Markdown 日报，零新增也写日报。登录失效时任务失败，由用户下次运行 login 恢复。
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.
- 9d ago Changed · +1 lines d0e0654b24c1
- 13d ago Changed · +3 lines 308182af8a55
- 14d ago Changed 93a3f91a29a4
- 29d ago Changed fa4c2a366062
- 1mo ago First seen · 162 lines · 92 tokens per session scan A 15ddf1d64c7a
douyin-favorites-to-knowledge is a skill published in the GitHub repository tars1230/douyin-favorites-to-knowledge (76 stars, last pushed 10d ago), licensed MIT. It adds 92 tokens to every session and 2,646 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to douyin-favorites-to-knowledge, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
llm-wiki
Explain and apply the foundational LLM Wiki architecture and knowledge-management strategy. Use for setup, architecture, schema, or organization decisions; operational ingest/query/lint work uses dedicated skills.
wiki-lint
Audit wiki health: broken links, orphans, contradictions, stale content, lifecycle and tag issues. Use for maintenance or health checks. Default is report-only; use wiki-dedup for page-identity merges.
wiki-export
Export the Obsidian wiki graph to JSON, GraphML, Neo4j Cypher, Postgres/SQL, HTML, or OKF bundles. Use when transferring or visualizing wiki data in external tools; wiki-import handles the reverse direction.
wiki-status
Report wiki ingestion/completeness state, pending source delta, structural insights, or maintenance equilibrium. Use for remaining-work/status questions, hubs and bridges, or whether maintenance skills still have pending changes. wiki-digest summarizes learned knowledge instead.
wiki-query
Search and synthesize answers from the compiled Obsidian wiki, including cited and multi-hop answers. Use for questions about existing wiki knowledge or fast index-only lookups, and for challenge requests ("challenge this", "argue against X", "poke holes in my plan") that look for the vault's own evidence against a…
wiki-capture
Turn the current conversation or finding into a structured permanent wiki note. Use for save/capture/preserve requests; QUICK MODE writes a fast raw/ note. Supports named-vault routing such as @research save this. This captures current discussion, not external source ingestion.