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 rampstackco/claude-skills --skill content-distributiongit clone --depth 1 https://github.com/rampstackco/claude-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/rampstackco/claude-skills/content-distribution)<a href="https://agentmods.dev/skills/rampstackco/claude-skills/content-distribution"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/content-distribution/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/rampstackco/claude-skills/content-distribution"><img src="https://agentmods.dev/badge/skills/rampstackco/claude-skills/content-distribution.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 | $0.00142 | $0.05032 |
| Opus 5.5 | $0.00057 | $0.02013 |
| Sonnet 5.5 | $0.00028 | $0.01006 |
| Haiku 4.5 | $0.00014 | $0.00503 |
Grade A, and why
content-distribution 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 1mo 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Distribution
A senior editorial leader's playbook for content distribution as a discipline. Owned, earned, and paid channels matched to audience and content type, with the channel-fit decisions that distinguish strategic distribution from spam-everywhere or hope-and-pray.
Content distribution is half the work and gets a fraction of the attention. Most programs spend 90% of capacity on production and 10% on distribution; the resulting ratio of effort-to-reach is consistently poor. The teams producing content that reaches audiences are the ones who treat distribution as a real discipline: channels chosen for fit, cadence calibrated to audience attention, owned-earned-paid balance set by program strategy, and effectiveness measured per channel.
This skill is the channel discipline. Different from content-repurposing (which turns one piece into many formats), this skill is about getting content TO audiences via the right channels. The two skills compose: repurpose first, then distribute the right format on the right channel.
The voice is the senior editorial leader who has watched programs underperform because distribution was treated as posting-after-publishing rather than as the equal half of the work.
When to use this skill: building a distribution discipline for a content program, auditing why content publishes consistently but reach is low, calibrating owned-earned-paid balance, designing channel-fit decisions for a multi-format program.
What this skill is for
This skill spans channel selection, audience-channel matching, content-channel matching, and distribution cadence. The content suite distinction:
content-strategydecides what to produce.pillar-content-architecturedesigns the topical hub.content-brief-authoringbriefs each piece.content-and-copywrites pieces.editorial-qaverifies before publish.content-repurposingturns one piece into many formats (transformation).content-distribution(this skill) gets content TO audiences via channels (channel work).
What ships with it
10 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.
- README.md 361 B
- references/audience-channel-matching.md 10 KB
- references/channel-taxonomy.md 13 KB
- references/common-distribution-failures.md 12 KB
- references/content-channel-matching.md 9.2 KB
- references/distribution-cadence-patterns.md 10 KB
- references/distribution-measurement.md 11 KB
- references/earned-channel-work.md 11 KB
- references/owned-channel-discipline.md 11 KB
- references/paid-promotion-patterns.md 11 KB
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.
- 1mo ago First seen · 314 lines · 142 tokens per session scan A b23b4adccb17
content-distribution is a skill published in the GitHub repository rampstackco/claude-skills (993 stars, last pushed 5d ago), licensed MIT. It adds 142 tokens to every session and 5,032 once invoked, about $0.0006 per session on Opus 5.5. 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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