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.
git clone --depth 1 https://github.com/arturseo-geo/claude-code-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/commands/arturseo-geo/claude-code-skills/content-pipeline)<a href="https://agentmods.dev/commands/arturseo-geo/claude-code-skills/content-pipeline"><img src="https://agentmods.dev/badge/commands/arturseo-geo/claude-code-skills/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/commands/arturseo-geo/claude-code-skills/content-pipeline"><img src="https://agentmods.dev/badge/commands/arturseo-geo/claude-code-skills/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 | $0.00000 | $0.01018 |
| Opus 5.5 | $0.00000 | $0.00407 |
| Sonnet 5.5 | $0.00000 | $0.00204 |
| Haiku 4.5 | $0.00000 | $0.00102 |
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 2mo 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.
This is a copy
100% identical to content-pipeline — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/content-pipeline
Run the full multi-agent content production pipeline.
Usage
/content-pipeline "topic or brief"
/content-pipeline "topic or brief" --type [blog|guide|landing-page]
/content-pipeline "topic or brief" --keyword "primary keyword" --audience "target audience"
What This Command Does
- Creates the brief — writes
.claude/pipeline/brief.mdfrom your input - Phase 1 (parallel) — spawns Research Agent and Analytics Agent simultaneously
- Waits for both to complete (checks for COMPLETE status lines)
- Phase 2 — spawns Writer Agent (depends on Phase 1)
- Phase 3 — spawns Editor Agent (depends on Writer)
- Phase 4 — spawns SEO/GEO Agent (depends on Editor)
- Phase 5 — spawns Master Agent (reviews all outputs)
- Pauses — presents master-review.md for your approval
Step-by-Step Execution
Step 1: Create pipeline directory and brief
mkdir -p .claude/pipeline
Write .claude/pipeline/brief.md:
# Content Brief
Topic: [extracted from user input]
Content type: [blog post / guide / landing page — inferred or from --type flag]
Primary keyword: [from --keyword flag or infer from topic]
Target audience: [from --audience flag or infer]
Additional requirements: [any other specifics from user input]
Created: [timestamp]
Step 2: Spawn Phase 1 agents in parallel
Dispatch two Task tool calls simultaneously:
Task 1 — Research Agent:
Read .claude/agents/research-agent.md for your instructions.
Input file: .claude/pipeline/brief.md
Output file: .claude/pipeline/research.md
Task 2 — Analytics Agent:
Read .claude/agents/analytics-agent.md for your instructions.
Input file: .claude/pipeline/brief.md
Output file: .claude/pipeline/analytics.md
Wait until both files contain their COMPLETE status line before proceeding.
Step 3: Spawn Writer Agent
Read .claude/agents/writer-agent.md for your instructions.
Input files: .claude/pipeline/brief.md, .claude/pipeline/research.md, .claude/pipeline/analytics.md
Output file: .claude/pipeline/draft.md
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.
- 2mo ago First seen · 120 lines · 0 tokens per session scan A 157ec8f76c02
content-pipeline is a command published in the GitHub repository arturseo-geo/claude-code-skills (13 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,018 tokens. A static security scan graded it A with 0 findings. It is 100% identical to content-pipeline, differing in 0 lines, and is treated as a copy.
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