Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add maddexritter-rgb/vibe-editing/plugin install vibe-editingWrote 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/maddexritter-rgb/vibe-editing/listicle-short)<a href="https://agentmods.dev/skills/maddexritter-rgb/vibe-editing/listicle-short"><img src="https://agentmods.dev/badge/skills/maddexritter-rgb/vibe-editing/listicle-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/maddexritter-rgb/vibe-editing/listicle-short"><img src="https://agentmods.dev/badge/skills/maddexritter-rgb/vibe-editing/listicle-short.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 | $0.00082 | $0.06078 |
| Opus 5 | $0.00041 | $0.03039 |
| Sonnet 5 | $0.00016 | $0.01216 |
| Haiku 4.5 | $0.00008 | $0.00608 |
Grade A, and why
listicle-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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
listicle-short — long-form → 60s numbered listicle V1, in one shot
What it makes: the speaker's best verbatim one-liners, cut from a long-form, assembled into a ~60s rapid-fire list, reframed 9:16, captioned in the SPICE style with a persistent glass category pill (1. OFFER, 2. MARKETING, …) above each tactic, audio leveled to −6 dB. This is the Creator-13-years format, templatized.
Defaults (override per clip)
- Flow = script + V1 together. Produce the numbered script (for sign-off) AND render a V1 in one pass.
--stop-after-scriptfor script-only. - Framing = auto 9:16 (face-tracked).
--stop-after-assembleoutputs the horizontal cut for the user's own reframe/bg (hero clips); they re-run with--no-reframeto caption it after. - Music = off.
--music <track>to add (calm/cinematic from the Speaker TikTok set, never hardcore — see caption-clips SOP). - Length ≈ 60s, ~10–14 tactics. Content-driven, not capped.
The run (agent steps)
1. Ingest
python3 ${CLAUDE_PLUGIN_ROOT}/skills/listicle-short/scripts/ingest.py "<youtube-url-or-file>" --out ~/Downloads/<slug>/
→ source.mp4 (1080p), transcript_ts.txt (read this), transcript_words.json (precise word timestamps), meta.json. YouTube uses free json3 captions; local files fall back to Groq lv3.
2. Mine + write the script (THIS is the editorial judgment — do it well)
Read transcript_ts.txt. For coverage on long transcripts, fan out parallel readers (Agent tool) over thirds, then curate. Pick the punchiest, self-contained, verbatim one-liners — prefer the speaker's OWN section/headline lines if the source is already enumerated ("number one… number two…"). Rules baked from the 13-years build:
- Each line must be a clean section/idea opener that stands alone (no dangling "this/that", no mid-story fragment). The #1 failure is pulling a fragment from the middle of a section — it reads random. Verify lead-in.
- Lead with a credibility/curiosity hook, end on a mic-drop.
- Keep each ~2–6 s; aim ~10–14 lines for ~60s. Renumber 1..N (your own count).
- VERIFY every quote against the source (exact wording + precise in/out from
transcript_words.json). The sub-agent mining timestamps drift — always confirm against the word stream.
What ships with it
3 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.
- 12d ago First seen · 246 lines · 82 tokens per session scan A 1a36616d0e5b
listicle-short is a skill published in the GitHub repository maddexritter-rgb/vibe-editing (7 stars, last pushed 2mo ago), licensed MIT. It adds 82 tokens to every session and 6,078 once invoked, about $0.0004 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.
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