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 agentmods add commands/animalzinc/claude-plugins/audit-contentgit clone --depth 1 https://github.com/animalzinc/claude-pluginsWrote 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/animalzinc/claude-plugins/audit-content)<a href="https://agentmods.dev/commands/animalzinc/claude-plugins/audit-content"><img src="https://agentmods.dev/badge/commands/animalzinc/claude-plugins/audit-content.svg" alt="Measured on agentmods" 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 | $0.00009 | $0.01843 |
| Opus 5 | $0.00005 | $0.00922 |
| Sonnet 5 | $0.00002 | $0.00369 |
| Haiku 4.5 | $0.00001 | $0.00184 |
Grade A, and why
audit-content 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 5d 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Content Library
Analyze WordPress XML, CMS JSON, or CSV exports to reveal publishing trends, author contributions, topic distribution, and content opportunities.
Step 1: Detect and Parse Export File
Read export file ($1):
- Auto-detect format (XML/JSON/CSV) unless --format specified
- Validate file structure
- Count total items (posts, pages, etc.)
Report to user:
## 📁 Content Export Detected
**File:** $1
**Format:** [WordPress XML / JSON / CSV]
**Items found:** [number] posts/pages
**Date range:** [earliest] to [latest]
**File size:** [size]
Would you like to proceed with analysis?
Wait for user confirmation.
Step 2: Parse Content Data
Use data-parser agent to extract:
For all formats:
- Post titles
- Publication dates
- Authors
- Categories/tags
- Post length (word count)
- Status (published/draft)
- URLs/slugs
Format-specific:
WordPress XML:
<item>
<title>Post Title</title>
<pubDate>Date</pubDate>
<dc:creator>Author</dc:creator>
<category>Category</category>
<content:encoded>Body content</content:encoded>
</item>
JSON:
{
"title": "Post Title",
"date": "2024-01-15",
"author": "Name",
"categories": ["cat1", "cat2"],
"content": "Body..."
}
CSV:
title,date,author,category,word_count
"Post Title","2024-01-15","Author","Category",1500
Progress report:
🔄 Parsing content...
- ✅ Posts extracted: [number]
- ✅ Authors identified: [number]
- ✅ Date range: [range]
- ✅ Categories: [number]
Step 3: Analyze Publishing Trends
Use publishing-analyzer agent to calculate:
Publishing Cadence:
- Posts per month (last 6, 12, 24 months)
- Publishing frequency trends (increasing/decreasing)
- Seasonal patterns
- Content velocity
Present to user:
## 📊 Publishing Trends
### Overall Stats
- Total posts: [number]
- Active period: [months/years]
- Average: [X] posts/month
- Trend: [Increasing 15% / Stable / Decreasing 10%]
### Recent Performance (Last 6 months)
- [Month]: [count] posts
- [Month]: [count] posts
[etc...]
### Publishing Patterns
- Peak month: [month] ([count] posts)
- Lowest month: [month] ([count] posts)
- Most active day: [day of week]
Would you like to continue with author and topic analysis?
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.
- 5d ago First seen · 350 lines · 0 tokens per session scan A b28a273a26dd
audit-content is a command published in the GitHub repository animalzinc/claude-plugins (15 stars, last pushed 16d ago), licensed MIT. It adds 9 tokens to every session and 1,843 once invoked, about $0.0000 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 2026-08-30.
Other commands, from other repositories
brand-setup
Configure brand voice, terminology, compliance guardrails, and style guide for content production.
resume
Resume a ContentForge pipeline run that was interrupted partway through.
output-folder
Print the absolute path to the user-visible ContentForge output folder and open it in the OS file manager.
create-content
Run the full 10-phase content production pipeline — research, draft, fact-check, humanize, and publish.
audit-content
Audit your content library for freshness decay, coverage gaps, and optimization opportunities.
content-brief
Generate a research-backed content brief with keyword data, competitor analysis, search intent, and SEO strategy.