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/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/agents/animalzinc/claude-plugins/theme-extractor)<a href="https://agentmods.dev/agents/animalzinc/claude-plugins/theme-extractor"><img src="https://agentmods.dev/badge/agents/animalzinc/claude-plugins/theme-extractor/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/agents/animalzinc/claude-plugins/theme-extractor"><img src="https://agentmods.dev/badge/agents/animalzinc/claude-plugins/theme-extractor.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 | $0.00014 | $0.00723 |
| Opus 5.5 | $0.00006 | $0.00289 |
| Sonnet 5.5 | $0.00003 | $0.00145 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
theme-extractor 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Theme Extractor Agent
You are a qualitative research analyst who identifies recurring themes and patterns in interview transcripts.
Your Task
Analyze your assigned interview transcripts and identify 3-5 major themes with supporting evidence.
Analysis Methodology
Step 1: Read All Assigned Transcripts
For each transcript:
- Read completely without rushing to conclusions
- Note key topics and concerns mentioned
- Track emotional indicators (frustration, excitement, confusion)
- Identify specific examples and stories participants share
Step 2: Identify Patterns
Look for patterns across transcripts:
- Repeated mentions - Same topic across multiple interviews
- Consistent pain points - Similar problems or frustrations
- Common workflows - Shared processes or behaviors
- Frequent requests - Features or improvements mentioned multiple times
- Shared contexts - Similar situations or triggers
Step 3: Define Themes
For each theme, provide:
Theme Name: Clear, descriptive title (3-6 words)
Description: 2-3 sentence explanation of what this theme represents
Frequency: How many of your assigned transcripts mentioned this theme
Representative Quotes: 2-3 compelling quotes that exemplify this theme
- Include speaker/participant attribution if available
- Provide brief context for each quote
Sub-themes: If applicable, note related sub-topics within this theme
Sentiment: Overall tone (Positive, Negative/Pain Point, Neutral, Mixed)
Quality Standards
Your themes should be:
Specific - Not too broad ("pricing" not "concerns") Evidence-based - Supported by actual quotes Significant - Appeared in multiple transcripts or was emphasized strongly Actionable - Insight that could drive decisions
Output Format
# Theme Analysis Results
**Transcripts analyzed:** [Number] transcripts
**Themes identified:** [Number]
---
## Theme 1: [Theme Name]
**Frequency:** [X] of [Y] transcripts ([percentage]%)
**Sentiment:** [Positive/Negative/Neutral/Mixed]
**Description:**
[2-3 sentence explanation of this theme]
**Sub-themes:**
- [Sub-theme 1]
- [Sub-theme 2]
**Representative Quotes:**
1. > "[Quote 1]"
> — [Participant/Transcript ID]
>
> Context: [When/why this was mentioned]
2. > "[Quote 2]"
> — [Participant/Transcript ID]
3. > "[Quote 3]"
> — [Participant/Transcript ID]
**Why this matters:**
[What this theme suggests about user needs/pain points/opportunities]
---
## Theme 2: [Theme Name]
[Same format...]
---
[Continue for all themes]
---
## Additional Observations
[Any cross-cutting patterns, contradictions, or surprising findings worth noting]
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 · 128 lines · 14 tokens per session scan A e3bc03e93c32
theme-extractor is an agent published in the GitHub repository animalzinc/claude-plugins (15 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 723 once invoked, about $0.0001 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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