Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/thatrebeccarae/claude-marketingnpx agentmods add skills/thatrebeccarae/claude-marketing/content-pipelineWrote 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/thatrebeccarae/claude-marketing/content-pipeline)<a href="https://agentmods.dev/skills/thatrebeccarae/claude-marketing/content-pipeline"><img src="https://agentmods.dev/badge/skills/thatrebeccarae/claude-marketing/content-pipeline/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/thatrebeccarae/claude-marketing/content-pipeline"><img src="https://agentmods.dev/badge/skills/thatrebeccarae/claude-marketing/content-pipeline.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 | $0.00051 | $0.01039 |
| Opus 5 | $0.00026 | $0.00519 |
| Sonnet 5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
content-pipeline 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 7d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Pipeline
Orchestrate the full content creation workflow from research brief to published-ready content with social distribution pack.
Install
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/content-pipeline ~/.claude/skills/
What This Skill Does
Chains up to 3 agents in sequence:
- research-analyst — Generates a research brief on the topic
- editor-in-chief — Reviews the draft against voice, structure, SEO, and argument quality
- social-amplifier — Creates platform-specific distribution pack (LinkedIn, Twitter/X, email)
Users can enter at any stage or run the full pipeline.
How this differs from content-workflow: The content-workflow skill is a manual workflow guide with frameworks and checklists. This skill is the orchestration layer — it actually spawns subagents and manages the pipeline programmatically.
How to Use
/content-pipeline [topic or file path]
Full Pipeline (from scratch)
/content-pipeline "Why MCP is the new API"
Start from Existing Draft
/content-pipeline review ./drafts/article-draft.md
Social Pack Only
/content-pipeline distribute ./content/published-article.md
Workflow Stages
Stage 1: Research (optional — skip if draft exists)
- Spawn research-analyst agent with the topic
- Output:
./research-briefs/YYYY-MM-DD-topic-slug.md - User reviews brief, decides whether to proceed
Stage 2: Editorial Review
- Spawn editor-in-chief agent pointing at the draft/brief
- Output:
[draft-path]-review.mdsaved alongside the source - Agent provides: voice assessment, structure feedback, SEO evaluation, line-level notes, title variants
- User decides: publish as-is, revise, or iterate
Stage 3: Social Distribution
- Spawn social-amplifier agent pointing at the final content
- Output:
[content-path]-social-pack.mdsaved alongside the source - Generates: LinkedIn posts (2 variants), Twitter/X thread, email subject lines, pull quotes, hashtags
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.
- 7d ago First seen · 131 lines · 51 tokens per session scan A 5e6e4d9c43b2
content-pipeline is a skill published in the GitHub repository thatrebeccarae/claude-marketing (136 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 1,039 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 2026-09-03.
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