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 yaxeen/storytelling-skills --skill channel-formulagit clone --depth 1 https://github.com/yaxeen/storytelling-skillsWrote 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/yaxeen/storytelling-skills/channel-formula)<a href="https://agentmods.dev/skills/yaxeen/storytelling-skills/channel-formula"><img src="https://agentmods.dev/badge/skills/yaxeen/storytelling-skills/channel-formula/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/yaxeen/storytelling-skills/channel-formula"><img src="https://agentmods.dev/badge/skills/yaxeen/storytelling-skills/channel-formula.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 | $0.00076 | $0.01332 |
| Opus 5 | $0.00038 | $0.00666 |
| Sonnet 5 | $0.00015 | $0.00266 |
| Haiku 4.5 | $0.00008 | $0.00133 |
Grade A, and why
channel-formula 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Channel Formula
- Every channel that works has an unwritten contract with its audience: a specific promise the winners keep and the flops break. This skill makes the contract written.
- Generic storytelling craft is necessary but not sufficient — a technically perfect story that breaks the channel's promise still dies. Audience expectation beats craft.
- The output is a reusable channel profile: formula, title pattern, packaging rules, and retention benchmarks, extracted from the channel's own data — never from assumptions about the niche.
- Built on the six levers — see storytelling-hooks. Recap: 1. Curiosity gap · 2. Emotional mirror · 3. Conflict engine · 4. Relatability · 5. Pattern + surprise · 6. Three-act.
When to Use
- A channel has some winners and some flops and nobody can articulate why.
- Before scripting for an established channel — check the premise against the formula first.
- After a flop, alongside retention-audit (audit finds where it died; this skill finds what promise was broken).
- Any platform, any niche: crime stories, cricket, tech reviews, cozy content, finance, faith, vlogs.
Intake (the evidence)
- 3–5 top videos: title, thumbnail (screenshot fine), views/velocity, and if available retention at 0:30.
- 1–3 flops from the same format — flops are as informative as winners; the formula lives in the difference.
- Channel's audience in one sentence, and what viewers do with the content (background listening? active watching? saving?).
- Never derive a formula from one video or from niche stereotypes — real data only. Too few videos? Say so and mark the profile "provisional".
Extraction Workflow
- Line up winners' titles side by side. Find the shared skeleton: what kind of threat/promise/subject repeats? What resolution do they all point to? Write the pattern as a fill-in:
[recurring setup]... [recurring promise clause]. - Line up the thumbnails. What is the focal subject in every winner? What emotion? What's never there?
- Name the core promise in one sentence: what feeling does the viewer come to have? (Crime channel: "I saw the twist coming before the detective." Tech channel: "I avoided an expensive mistake." Fitness channel: "I can restart after falling off.")
- Autopsy the flops against it. Which element of the promise did each flop break — premise, resolution, packaging tone, pacing? The broken element becomes a "never" rule.
- Set benchmarks from the channel's own winners (retention at 0:30 / 2:00, typical velocity) — not from industry averages.
- Calibrate the levers. Same six levers, channel-specific volume: how hot should tension run? What kind of conflict does this audience accept? How long should resolutions linger?
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 · 86 lines · 76 tokens per session scan A 09481f40157f
channel-formula is a skill published in the GitHub repository yaxeen/storytelling-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 76 tokens to every session and 1,332 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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self-media-content-analytics
A review process for measuring and learning from social-media content using screenshots, exported files, tables, links, or other supplied data.