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/firstdebug/avatar-platform/avatar-config-authoringnpx skills add firstdebug/avatar-platform --skill avatar-config-authoringgit clone --depth 1 https://github.com/firstdebug/avatar-platformWrote 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/firstdebug/avatar-platform/avatar-config-authoring)<a href="https://agentmods.dev/skills/firstdebug/avatar-platform/avatar-config-authoring"><img src="https://agentmods.dev/badge/skills/firstdebug/avatar-platform/avatar-config-authoring.svg" alt="Measured on agentmods" 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 | $0.00058 | $0.01540 |
| Opus 5 | $0.00029 | $0.00770 |
| Sonnet 5 | $0.00012 | $0.00308 |
| Haiku 4.5 | $0.00006 | $0.00154 |
Grade A, and why
avatar-config-authoring 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 3d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
2 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.
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.
- 3d ago First seen · 137 lines · 58 tokens per session scan A ec8258c9acd9
avatar-config-authoring is a skill published in the GitHub repository firstdebug/avatar-platform (5 stars, last pushed 8d ago), with no licence file. It adds 58 tokens to every session and 1,540 once invoked, about $0.0003 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.
Other skills, from other repositories
api-contrib
Contributing to the Kokoro-FastAPI Python API: module layout, endpoint gating pattern, test expectations. Use when adding or changing endpoints, services, or inference code.
video-recap
从输入视频端到端生成中文解说成片。用户提供 .mp4 / .mov / .mkv / .webm，并要求添加旁白、 配音、总结、短剧/电视剧/电影/纪录片/科普解说时使用。负责编排 video- 技能链：视频理解 → Agent 制定故事与视听方案 → 剪辑 → 配音 → 合成。触发词：视频解说、视频旁白、生成解说、 视频 recap、video recap、voiceover、narration、auto-dub、recap。.
video-script
对已完成分析的视频进行导演与剪辑策划，再写带时间戳的中文解说并校验。workdir 已包含 agentnarrationbrief.md 与 vlmanalysis.json 时使用。适用于故事方向、片段选择、画面/原声/旁白分工、 解说写作与复核。输入 workdir 中的理解索引；输出 recapstoryplan.json、visualaudioboard.json、 可选 stylecard.json、cut 模式需要的 clipplan.json，以及通过校验的 narration.json。触发词：解说词、写解说、视频旁白、 narration script、写稿、解说文案、剪辑思路、导演思路。.
video-assemble
合成视频解说最终成片：把旁白音频铺到源视频上，按旁白窗口压低原声，生成 SRT / ASS 字幕并可烧录， 最后做响度标准化。作为最终合成阶段使用。输入源视频、ttsmeta.json 与旁白位置； 输出 recap 成片和字幕。触发词：视频合成、混音、字幕、压字幕、assemble video、mux、ducking、subtitles、成片。.
video-cut
把长视频按 Agent 选择的原片区间剪成短片。作为两阶段创作流程中的剪辑环节，读取 clipplan.json 与源视频， 输出 editedsource.mp4；随后 Agent 按输出时间线写 narration.json。单独调用且未传 --no-narration-map 时， 仍支持旧版单阶段路径，把原片时间的 narration.json 映射为 narrationmapped.json。 触发词：视频剪辑、剪辑式解说、video cut、clip plan、拼剪。.
video-understanding
把视频分析为结构化理解索引：场景检测、ASR 转写、逐场景 VLM 观察、静音窗口、融合时间线和写作 brief。 用于理解、索引或总结视频，也作为后续创作前的分析阶段。输入视频文件；输出 scenes.json、 asrresult.json、vlmanalysis.json、silenceperiods.json、timelinefusion.json、agentnarrationbrief.md。 触发词：视频理解、视频分析、视频索引、video understanding、analyze video、看懂视频。.