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 donvito/skillsbento --skill stream-studiogit clone --depth 1 https://github.com/donvito/skillsbentoWrote 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/donvito/skillsbento/stream-studio)<a href="https://agentmods.dev/skills/donvito/skillsbento/stream-studio"><img src="https://agentmods.dev/badge/skills/donvito/skillsbento/stream-studio/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/donvito/skillsbento/stream-studio"><img src="https://agentmods.dev/badge/skills/donvito/skillsbento/stream-studio.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 | $0.00174 | $0.01193 |
| Opus 5.5 | $0.00070 | $0.00477 |
| Sonnet 5.5 | $0.00035 | $0.00239 |
| Haiku 4.5 | $0.00017 | $0.00119 |
Grade A, and why
stream-studio 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 10d 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stream Studio
One skill for the whole pipeline: raw stream → transcript + chapters + clips → summary reel / shorts → YouTube copy. All files follow one folder layout and naming scheme so a clip, its transcript, subtitles, chapters and thumbnail always link together, and every video is rebuildable from its version folder.
SKILL=<this skill's base directory>
$SKILL/scripts/stream.py doctor · init · transcribe · clips · reel · short-new · short-build · migrate
$SKILL/scripts/short.py shorts engine front end: doctor · brand · transcribe · frame · cut · scaffold · balance
$SKILL/engine/ assets/ motion-reel renderer, kit.js, fonts
$SKILL/references/ folders.md · process-stream.md · make-shorts.md · style.md · kit-api.md · youtube.md
Which workflow?
| the user says | do | read |
|---|---|---|
/process-stream video.mp4, "clip my stream", "transcript", "chapters", "subtitles" |
process the stream | references/folders.md, then references/process-stream.md |
| "summary video of all features", recap, highlight reel | summary reel (longform) | references/process-stream.md → Summary reel |
/make-shorts <topic or file>, "make a short", "vertical", "reel" |
make shorts | references/folders.md, then references/make-shorts.md (+ style.md, kit-api.md, youtube.md) |
| "chapter timings I can paste in X/YouTube" | chapters | references/process-stream.md → Chapters |
| "title and description" | YouTube copy | references/youtube.md |
A request often chains them ("process this stream and make a short on dots"): do /process-stream first, then shorts from the clips.
Works in Claude Code and Codex
- The skill is the same in both.
/process-streamand/make-shortsare Claude Code commands only; in Codex ask in plain words or mention the skill ($stream-studio process my stream video.mp4,$stream-studio make a 30s short about <topic>). - Tool names in these docs are generic: "run in the background" = Claude Code's Bash
run_in_background, or in any agentnohup <cmd> > .work/job.log 2>&1 &and poll the log; "ask the user" = a question tool if you have one, otherwise a short numbered list in chat; "look at a frame" = open the PNG with your image-viewing tool. - Python scripts locate themselves (
$SKILL= the folder holding this file), so the skill works from any install location.
What ships with it
27 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.
- assets/fonts/Anton-Regular.ttf 167 KB
- assets/fonts/JetBrainsMono.ttf 183 KB
- assets/fonts/OFL-Anton.txt 4.4 KB
- assets/fonts/OFL-JetBrainsMono.txt 4.3 KB
- assets/fonts/OFL-SpaceGrotesk.txt 4.4 KB
- assets/fonts/SpaceGrotesk.ttf 133 KB
- assets/kit.js 12 KB runs code
- assets/template.html 4.0 KB
- engine/assets/engine.js 24 KB runs code
- engine/assets/reel.html 17 KB
- engine/README.md 5.9 KB
- engine/references/engine-api.md 4.8 KB
- engine/references/motion-design.md 2.6 KB
- engine/scripts/build.py 2.8 KB runs code
- engine/scripts/check_env.sh 1.0 KB runs code
- engine/scripts/ingest.py 9.0 KB runs code
- engine/scripts/new_project.py 2.3 KB runs code
- engine/scripts/render.py 6.7 KB runs code
- engine/scripts/score.py 15 KB runs code
- references/folders.md 3.6 KB
- references/kit-api.md 3.3 KB
- references/make-shorts.md 10 KB
- references/process-stream.md 4.6 KB
- references/style.md 5.1 KB
- references/youtube.md 1.1 KB
- scripts/short.py 23 KB runs code
- scripts/stream.py 21 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.
- 10d ago First seen · 47 lines · 174 tokens per session scan A 0a7a7967027f
stream-studio is a skill published in the GitHub repository donvito/skillsbento (9 stars, last pushed 10d ago), licensed Apache-2.0. It adds 174 tokens to every session and 1,193 once invoked, about $0.0007 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 2026-10-01.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
component-fixtures
Use when creating or updating component fixtures for screenshot testing or their shared infrastructure, or when designing UI components to be fixture-friendly. Covers fixture file structure, theming, service setup, CSS scoping, async rendering, validation, and common pitfalls.