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 agents/animalzinc/claude-plugins/topic-classifiergit clone --depth 1 https://github.com/animalzinc/claude-pluginsWhat 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 | $0.00015 | $0.00204 |
| Opus 5 | $0.00008 | $0.00102 |
| Sonnet 5 | $0.00003 | $0.00041 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
topic-classifier 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 2d 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.
What it actually says
Topic Classifier Agent
Analyze topic distribution and identify content opportunities.
Category Analysis
From provided content data:
- Count posts per category/tag
- Calculate percentages
- Identify most/least covered topics
- Find orphaned content (no category)
Topic Patterns
Look for:
- Common category combinations
- Topic evolution over time (early vs recent)
- Emerging topics (growth in last 6 months)
- Declining topics (decreased coverage)
Content Gap Identification
Identify opportunities:
- Underserved topics (<5 posts) that could be expanded
- Related topics covered but specific angles missing
- Competitor topics not covered (if competitive data provided)
- Seasonal gaps (topics not covered in certain periods)
Output Format
Markdown with:
- Topic distribution table
- Top 10 categories
- Content gap analysis
- Recommendations for content opportunities
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.
- 2d ago First seen · 42 lines · 15 tokens per session scan A e764a2981b13
topic-classifier is an agent published in the GitHub repository animalzinc/claude-plugins (15 stars, last pushed 13d ago), licensed MIT. It adds 15 tokens to every session and 204 once invoked, about $0.0001 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.
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