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 lovstudio/skills --skill list-videosgit clone --depth 1 https://github.com/lovstudio/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/lovstudio/skills/list-videos)<a href="https://agentmods.dev/skills/lovstudio/skills/list-videos"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/list-videos/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/lovstudio/skills/list-videos"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/list-videos.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 | $0.00077 | $0.02217 |
| Opus 5.5 | $0.00031 | $0.00887 |
| Sonnet 5.5 | $0.00015 | $0.00443 |
| Haiku 4.5 | $0.00008 | $0.00222 |
Grade A, and why
lov-list-videos 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 26d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
视频清单 · Video Inventory
把一个目录或整台电脑里的视频文件列成一份可排序、可过滤的清单。核心是一个共享的
增量目录缓存：第一次扫描完整遍历，之后每次只对目录做 stat，未变化的目录直接复
用缓存，所以重复运行的成本接近“目录数 × 一次 stat”，与文件总数无关。
Triggers
Activate when
- “列出这个文件夹里所有的视频文件。”
- “全盘找一下我电脑上有哪些视频，按大小排。”
- “最近一周新增了哪些 mp4 / mov？”
- “哪些视频是 HEVC 编码，哪些是 H.264？”
- “List all video files under this project.”
- “Find every video on my Mac and show the biggest ones.”
Do not activate when
- 找的是“上次 AI 对话里生成的那个文件”，需要会话证据 →
lov-search-file - 要从一个视频里抽帧、挑瞬间或做照片 →
lov-video-moments - 要转码、剪辑、加字幕或压缩视频 →
lov-media-creator/lov-media-preprocessor - 要把相机卡素材完整迁移到 SSD 并校验 →
lov-migrate-camera-media - 只是问某个视频格式或编码的知识，不需要扫描磁盘 → 直接回答
User Profile (cross-session)
Every generated Skill is connected to the shared user-profile/v1 contract in
skill.yaml. Read the shared user, brand, workspace, preferences, and this
Skill's skills.<skill_id> namespace at the start of every run. Keep the source
portable: resolved personal values belong in the shared profile, never here.
When the user directly states a durable preference or brand fact, persist it
through scripts/profile_store.py and report the saved profile path. Put
Skill-specific values under records.<field>; use brand.<field> or
user.<field> for shared values. Do not persist inferred secrets or credentials.
See references/user-profile.md for the complete contract.
本 Skill 读取的长期记录（都可选）：
| 记录 | 含义 |
|---|---|
records.default_scope |
不带参数时的默认范围：current（默认）或 global |
records.global_roots |
--global 使用的根目录列表；缺省为主目录加 /Volumes 下的外接卷 |
records.extra_excludes |
追加跳过的目录名或路径后缀 |
records.extra_extensions |
追加识别的视频扩展名 |
records.default_format |
默认输出：table、json、paths、csv |
records.default_sort |
默认排序：mtime、size、path、name、duration |
缓存文件与 Profile 同住：<profile 目录>/lov-list-videos/cache.json，默认即
~/.lovstudio/skills/lov-list-videos/cache.json。LOV_LIST_VIDEOS_CACHE 或
--cache 可以覆盖。缓存是可再生的派生数据，删掉只会让下一次变成全量扫描。
Skill Group Composition
Read references/skill-composition.md before deciding whether to invoke or
extend any adjacent capability. The record distinguishes optional upstream and
downstream handoffs from embedded Kit modules. Do not silently depend on a
sibling Skill that is not shipped with this source.
What ships with it
17 files 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.
- .gitignore 84 B
- cases/cases.json 3.1 KB
- CHANGELOG.md 1.1 KB
- LICENSE 1.0 KB
- pricing-card.yaml 779 B
- README.md 4.5 KB
- references/cache-design.md 3.4 KB
- references/skill-card-standard.md 660 B
- references/skill-composition.md 3.0 KB
- references/user-profile.md 2.7 KB
- scripts/list_videos.py 28 KB runs code
- scripts/profile_store.py 8.3 KB runs code
- scripts/validate_skill.py 26 KB runs code
- skill-card.md 3.1 KB
- skill-card.yaml 4.9 KB
- skill.yaml 2.3 KB
- tests/test_list_videos.py 4.5 KB runs code
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.
- 26d ago First seen · 184 lines · 77 tokens per session scan A 5a6cf6f09cf4
lov-list-videos is a skill published in the GitHub repository lovstudio/skills (72 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 2,217 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-26.
Other skills, from other repositories
mongodb-connection
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling connect()), configuring connection pools, troubleshooting connection errors…
frontend-testing-debugging
Use when testing, debugging, or making targeted improvements to rendered frontend apps through the Frontend Design Skills or web dev plugin: local dev servers, UI regressions, interaction bugs, console errors, responsive layout, and visual QA. Check whether the Browser plugin is available and use it first when it is…
mongodb-search-and-ai
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG…
workers-best-practices
Cloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.
web-performance-optimization
Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.
grill-design
Decide a UI design by building several genuinely different working prototypes and comparing them side by side, in rounds, until the choice is locked. Use when asked to "grill the design", "design this screen", "show me options", "prototype some variants", "what should this look like", "I can't decide on a layout".