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 crealwork/ai-marketing-kit --skill longform-to-contentgit clone --depth 1 https://github.com/crealwork/ai-marketing-kitWrote 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/crealwork/ai-marketing-kit/longform-to-content)<a href="https://agentmods.dev/skills/crealwork/ai-marketing-kit/longform-to-content"><img src="https://agentmods.dev/badge/skills/crealwork/ai-marketing-kit/longform-to-content/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/crealwork/ai-marketing-kit/longform-to-content"><img src="https://agentmods.dev/badge/skills/crealwork/ai-marketing-kit/longform-to-content.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 | $0.00115 | $0.01372 |
| Opus 5 | $0.00057 | $0.00686 |
| Sonnet 5 | $0.00023 | $0.00274 |
| Haiku 4.5 | $0.00012 | $0.00137 |
Grade A, and why
longform-to-content 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Longform → Content
One raw recording in → a published content system out: relayouted full edit + burned captions + cold open, 4–8 vertical shorts, CTR thumbnails, and scheduled posts.
Every gate below is MANDATORY, in order. Skipping a gate or a self-test is a failure, not a shortcut. Violating the letter of these rules is violating their spirit. Each rule exists because an agent already broke it in production (see Red Flags).
Gates (run in order)
digraph gates {
"G0 Setup check" -> "G1 Brand check" -> "G2 Edit pipeline" -> "G3 Thumbnails" -> "G4 Publish";
"G0 Setup check" -> "STOP: give user references/SETUP.md installs, wait" [label="tool missing"];
"G1 Brand check" -> "Run brand intake (references/BRAND.md)" [label="no DESIGN.md"];
"G4 Publish" -> "STOP: user has no Zernio → onboarding in references/PUBLISHING.md" [label="no account"];
}
G0 — Setup. Verify EVERY tool in references/SETUP.md with its verify command. Any missing → do not improvise; hand the user that file's install links + instructions and wait. Never substitute a "similar" tool.
G1 — Brand. A DESIGN.md with measurable tokens (2 colors as hex, fonts as file
paths, wordmark rule) MUST exist before any pixel is rendered. None? Run the intake in
references/BRAND.md — derive from the user's site/deck, or interview, or apply the
neutral default. Never invent brand values silently.
G2 — Edit. Follow references/PIPELINE.md exactly: transcribe once (word-level, cached) → measure layout geometry per recording → EDL (silence + tight-pacing + disfluency/stumble cuts — tight is the retention default, PIPELINE.md §3–4) → relayout render → cold open + cards → corrected burned captions LAST → SRT + chapters → frame-by-frame self-review before showing the user. Caption timing/text correctness has its own bible: references/CAPTIONS.md — read it before writing any cue.
G3 — Thumbnails. references/THUMBNAILS.md. The main thumbnail's claim MUST be the first spoken line of the cold open, and the user's real face photo MUST be used.
What ships with it
26 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.
- BOOTSTRAP.md 2.6 KB
- llms.txt 2.5 KB
- README.md 6.9 KB
- references/BRAND.md 3.0 KB
- references/CAPTIONS.md 6.7 KB
- references/PIPELINE.md 9.2 KB
- references/PUBLISHING.md 4.1 KB
- references/SETUP.md 4.8 KB
- references/SHORTS.md 6.7 KB
- references/THUMBNAILS.md 5.2 KB
- references/YOUTUBE.md 5.9 KB
- scripts/build_assets.py 2.0 KB runs code
- scripts/compose_thumbs.py 2.9 KB runs code
- scripts/corrections.py 4.4 KB runs code
- scripts/gen_edl.py 3.7 KB runs code
- scripts/gen_final_v4.py 6.3 KB runs code
- scripts/gen_real_baked.py 5.2 KB runs code
- scripts/gen_shorts.py 12 KB runs code
- scripts/gen_srt.py 2.7 KB runs code
- scripts/gen_thumb_baked.py 3.1 KB runs code
- scripts/measure_layout.py 3.3 KB runs code
- scripts/publish.py 6.1 KB runs code
- scripts/README.md 2.1 KB
- scripts/render_full.py 3.8 KB runs code
- scripts/scan_fillers.py 2.9 KB runs code
- scripts/scan_slides.py 1.5 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.
- 12d ago First seen · 90 lines · 115 tokens per session scan A f83867c10243
longform-to-content is a skill published in the GitHub repository crealwork/ai-marketing-kit (18 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,372 once invoked, about $0.0006 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.
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