Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hassancs91/claude-faceless-shorts-creatornpx agentmods add skills/hassancs91/claude-faceless-shorts-creator/make-shortWrote 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/hassancs91/claude-faceless-shorts-creator/make-short)<a href="https://agentmods.dev/skills/hassancs91/claude-faceless-shorts-creator/make-short"><img src="https://agentmods.dev/badge/skills/hassancs91/claude-faceless-shorts-creator/make-short/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/hassancs91/claude-faceless-shorts-creator/make-short"><img src="https://agentmods.dev/badge/skills/hassancs91/claude-faceless-shorts-creator/make-short.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00183 | $0.02161 |
| Opus 5 | $0.00092 | $0.01081 |
| Sonnet 5 | $0.00037 | $0.00432 |
| Haiku 4.5 | $0.00018 | $0.00216 |
Grade A, and why
make-short 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 12d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
make-short — fully-TSX vertical shorts, end to end
Everything is code: no footage, no stock. One short = one Remotion composition fed by a
beats contract, with voice and SFX layered on after render. Proven across the 12 example
shorts in shorts/. The idea bank + niche ranking lives in IDEAS.md — read it when
picking a topic, and grow it when brainstorming.
Run everything from the repo root. Tools are stdlib-only Python 3.10+; ffmpeg/node
must be on PATH; API keys live in .env (see .env.example).
Artifact contract
shorts/short-N-<niche>/
script.md — hook, beat sheet table (time | on screen | VO), production notes
beats.json — machine contract: format, vo[] (text+start/end+words), beats[], niche timeline
voice/ — per-line TTS clips + voice.wav (gen_voice.py) [gitignored]
sfx-plan.json — cue sheet on the global timeline (mix_sfx.py)
output/ — short-N-sfx.mp4 (+ music auditions) [gitignored]
remotion/src/shots/short-N/
ShortN<Name>.tsx — THE composition (registered by npm run gen)
vo.gen.ts — AUTO-GENERATED VO with exact word times (never hand-edit)
Shared kit: remotion/src/lib/shorts.tsx (Captions, Kicker, BigTitle, Stamp, PauseCard,
StatChip, ProgressBar, ShortsBackdrop, SAFE areas, prog helper). Niche libs so far: lib/chess.tsx,
lib/math.tsx, lib/algo.tsx, lib/sheet.tsx, lib/piano.tsx, lib/prob.tsx, lib/map.tsx,
lib/orbit.tsx, lib/chart.tsx, lib/story.tsx… Add at most ONE new niche lib per short,
generic enough for a series.
Stage 1 — script + beats
Beat grammar (~38–42s): HOOK (0–3.4s, frame 0 FULLY composed — the payoff already visible, no fade from black) → SETUP → optional QUIZ (PauseCard, ~2.5s — drives comments) → REVEAL in 2–4 steps, each synced to a VO word → TWIST → LOOP (last frame == frame 0, dissolving the payoff back into the intro so it replays seamlessly).
Outros — no fluff. NEVER end on an old-school engagement-CTA — no "what should I do next?", no "comment below", no "which one should I break down". They read as dated. End on the PAYOFF line, and let the visual LOOP blend back into the intro — the loop-into-intro is the ending. Prefer a clean visual dissolve/reset that lands the last frame exactly on frame 0. If a seamless loop genuinely isn't possible for a topic, just end on the payoff with no filler. Don't pad the tail: trim the composition so it ends shortly after the payoff, not seconds of dead air.
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.
- 12d ago First seen · 128 lines · 183 tokens per session scan A 2c612c6a9e34
make-short is a skill published in the GitHub repository hassancs91/claude-faceless-shorts-creator (230 stars, last pushed 24d ago), licensed MIT. It adds 183 tokens to every session and 2,161 once invoked, about $0.0009 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 2026-08-30.
Other skills, from other repositories
verticals
AI-native vertical video engine with niche intelligence. Takes a one-line topic and a niche profile, and outputs a finished YouTube Short/Reel/TikTok with AI-generated b-roll, voiceover, burned-in captions, background music, and thumbnail. Supports multiple LLM providers (Claude, Gemini, GPT, Ollama), TTS providers…
creative-theme-generator
Converts any user input into structured creative themes for AI video generation. Handles: natural language descriptions, keywords, long-form text extraction, partial fields, random generation, and batch test tasks. Outputs standardized 12-field JSON (taskid, type, theme, description, durationsec, creativestyle, tone…
ai-film-studio
AI film production studio with pre-built scripts: analyze inspiration/reference videos (Gemini), write script + storyboard, generate stills (GPT Image 2; Seedream 5 / Nano Banana as fallbacks), video (Gemini Omni 1.1 Flash, FLUX 3 Video, Seedance 2.5/2.0, LTX 2.5/2.3; Veo 3.1 fallback), audio (ElevenLabs), and…
seedance-director
A workflow coordinator that connects several skills for producing a complete set of Seedance video clips from a short-film request.
seedance-post-production
A video post-production workflow for turning multiple clips into a finished short film. It supports joining clips, color adjustment, transitions, subtitles, and combining sound with video.
seedance-micromotion
A final prompt-editing step that adds small, natural movements to Seedance video instructions before they are sent to the video generator.