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 compress-videogit 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/compress-video)<a href="https://agentmods.dev/skills/lovstudio/skills/compress-video"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/compress-video/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/compress-video"><img src="https://agentmods.dev/badge/skills/lovstudio/skills/compress-video.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 | $0.00089 | $0.02514 |
| Opus 5.5 | $0.00036 | $0.01006 |
| Sonnet 5.5 | $0.00018 | $0.00503 |
| Haiku 4.5 | $0.00009 | $0.00251 |
Grade A, and why
lov-compress-video 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
视频瘦身 · Video Shrink
把一个或多个视频文件压缩成体积尽可能小、肉眼看不出明显损失的 MP4。默认策略是
libx265 CRF 28、保留分辨率与帧率、AAC 96k，输出 <原名>-compressed.mp4 放在原
文件旁边，原文件不动。质量档、目标体积、VMAF 门禁和替换模式都是可选开关。
Triggers
Activate when
- “把这个视频压小一点，发微信用。”
- “这条 4K 素材压到 1080p，尽量小。”
- “压缩到 50M 以内。”
- “压完直接替换原文件。”
- “Compress this video as small as possible without visible quality loss.”
- “Shrink these MP4s to fit in an email.”
Do not activate when
- 只是问 HEVC、H.264、ProRes 的区别或该选哪个 → 直接回答，不跑编码
- 要先找出电脑上有哪些视频 →
lov-list-videos - 要剪辑、加字幕、拼接、调色 →
lov-media-creator - 要对课程实录做增强与分段 →
lov-media-preprocessor - 要压缩图片 →
baoyu-compress-image
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_quality |
smallest / small（默认）/ balanced / high |
records.default_codec |
hevc（默认）/ h264 / av1 |
records.default_preset |
x264/x265 preset，默认按质量档选 |
records.audio_bitrate |
AAC 码率 kbit/s，默认按质量档选 |
records.max_edge |
长边像素上限，默认不缩放 |
records.min_vmaf |
画质门禁分数，设置后每次都会测 VMAF 并按需重编 |
records.output_dir |
固定输出目录，默认放原文件旁边 |
records.replace_original |
true 时默认替换原文件；只有用户明确说过才可写入 |
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 2.6 KB
- CHANGELOG.md 1.1 KB
- LICENSE 1.0 KB
- pricing-card.yaml 731 B
- README.md 4.8 KB
- references/encoding-guide.md 3.3 KB
- references/skill-card-standard.md 660 B
- references/skill-composition.md 2.6 KB
- references/user-profile.md 2.7 KB
- scripts/compress_video.py 31 KB runs code
- scripts/profile_store.py 8.3 KB runs code
- scripts/validate_skill.py 26 KB runs code
- skill-card.md 2.8 KB
- skill-card.yaml 4.4 KB
- skill.yaml 2.5 KB
- tests/test_compress_video.py 6.0 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 · 196 lines · 89 tokens per session scan A b5b64e2e6629
lov-compress-video is a skill published in the GitHub repository lovstudio/skills (72 stars, last pushed today), licensed MIT. It adds 89 tokens to every session and 2,514 once invoked, about $0.0004 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.
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