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 Avyayalaya/agent-prime --skill multi-channel-publishinggit clone --depth 1 https://github.com/Avyayalaya/agent-primeWrote 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/avyayalaya/agent-prime/multi-channel-publishing)<a href="https://agentmods.dev/skills/avyayalaya/agent-prime/multi-channel-publishing"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-prime/multi-channel-publishing/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/avyayalaya/agent-prime/multi-channel-publishing"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-prime/multi-channel-publishing.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 | $0.00064 | $0.19355 |
| Opus 5 | $0.00032 | $0.09677 |
| Sonnet 5 | $0.00013 | $0.03871 |
| Haiku 4.5 | $0.00006 | $0.01936 |
Grade A, and why
multi-channel-publishing 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- multi-channel-publishing — 86% identical, 77 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,073 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Produce a channel-ready content derivative from a source document — not a summary, but a structurally adapted compression that preserves the source's thesis, maintains evidence fidelity, and conforms to the target channel's format constraints, audience expectations, and hook conventions. The output is a publishable artifact with a compression log proving nothing was lost by accident.
When to Use / When NOT to Use
Use this skill when:
- Repurposing a long-form article (Substack, blog, thesis) into a LinkedIn post, conference abstract, podcast brief, newsletter, tweet thread, spoken script, or executive briefing
- Deriving a compressed format from a full argument (compression direction: long → short)
- Adapting content for a new audience or channel while preserving the core claim
- Producing a spoken script from written beats or a conference proposal
- Creating a distribution package (multiple derivatives from one source)
Do NOT use this skill when:
- You need to write the original long-form piece (use the source-appropriate writing methodology first — P2: Substack first, derivatives second)
- You need to combine multiple sources into one synthesis (use Narrative Building or Problem Framing)
- You need to translate between languages (this is structural adaptation, not linguistic translation)
- The source document is internal/confidential and the target channel is public (Context Gate will catch this, but flag it early)
- You need a generic summary for no specific audience or channel (summaries without channel constraints produce FM-1: Summary, Not Derivative)
Anti-inputs (what this skill does NOT handle):
- Original content creation from scratch (this skill DERIVES, it does not originate)
- Content strategy or editorial calendar planning (this is a single-derivation tool)
- SEO optimization (channel format rules include discoverability principles, but this is not an SEO skill)
- Visual design or layout (this produces text; design is a separate discipline)
What ships with it
1 file 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.
- 10d ago First seen · 1,073 lines · 64 tokens per session scan A dc2b035ef055
multi-channel-publishing is a skill published in the GitHub repository Avyayalaya/agent-prime (8 stars, last pushed 3mo ago), licensed MIT. It adds 64 tokens to every session and 19,355 once invoked, about $0.0003 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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