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 skills add OneWave-AI/claude-skills --skill reddit-analyzergit clone --depth 1 https://github.com/OneWave-AI/claude-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/skills/onewave-ai/claude-skills/reddit-analyzer)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/reddit-analyzer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/reddit-analyzer/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/onewave-ai/claude-skills/reddit-analyzer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/reddit-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00042 | $0.01016 |
| Opus 5 | $0.00021 | $0.00508 |
| Sonnet 5 | $0.00008 | $0.00203 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
reddit-thread-analyzer 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reddit Thread Analyzer
Extract deep insights from Reddit discussions including sentiment, key arguments, and community consensus.
Given a Reddit thread URL or a question about Reddit opinions, analyze the discussion comprehensively to surface meaningful patterns and insights.
Instructions
1. Fetch and Parse Thread Data
Use WebFetch to load the Reddit thread and extract:
- Post title, body, author, score, and timestamp
- All comments (not just top-level)
- Comment scores, awards, and timestamps
- Note verified contributors or expert flair
2. Analyze Overall Sentiment
Determine the dominant sentiment and emotional tone:
- Overall sentiment: Positive, negative, neutral, or mixed
- Sentiment distribution: Approximate percentages
- Emotional tone: Excited, frustrated, skeptical, supportive, angry, enthusiastic
- Shift over time: Note if sentiment changes throughout discussion
3. Extract Key Arguments
Identify the most impactful points:
Top Arguments in Favor (3-5 points):
- Quote the argument
- Note comment score
- Identify supporting evidence or reasoning
Top Arguments Against (3-5 points):
- Quote the argument
- Note comment score
- Identify counter-points and rebuttals
Expert or Verified Opinions:
- Highlight comments from verified experts
- Note OP responses and clarifications
4. Find Consensus Points
Determine what the community agrees on:
- Points with broad agreement (high scores, no controversy)
- Emerging patterns across multiple comments
- Common ground between opposing viewpoints
5. Identify Controversial Topics
Flag heavily debated points:
- Topics with mixed upvotes/downvotes
- Arguments that sparked long comment chains
- Divisive issues where community is split
6. Provide Structured Analysis
Format the analysis clearly:
# Reddit Analysis: [Thread Title]
## Executive Summary
[2-3 sentence overview of the discussion and main takeaway]
## Overall Sentiment
- **Dominant Sentiment**: Positive/Negative/Neutral/Mixed (X%)
- **Emotional Tone**: [excited/frustrated/skeptical/etc.]
- **Community Alignment**: High/Medium/Low
## Top Arguments
### In Favor
1. **[Main point]** (+XXX score)
> "[Direct quote from comment]"
- [Brief explanation of reasoning]
2. **[Main point]** (+XXX score)
> "[Direct quote]"
### Against
1. **[Main point]** (+XXX score)
> "[Direct quote]"
## Community Consensus
- [Point most people agree on]
- [Another consensus point]
## Controversial Topics
- [Divisive issue] - Community split roughly 50/50
- [Another debate point]
## Notable Insights
- **Expert Opinion**: [Quote from verified expert] (+XXX)
- **Surprising Take**: [Unexpected perspective that gained traction]
- **Most Helpful**: [Most practical or actionable advice]
## Key Quotes
> "[Memorable quote]" - u/username (+XXX score)
> "[Another impactful quote]" - u/username (+XXX score)
## Discussion Quality
- Civility: High/Medium/Low
- Depth: Superficial/Moderate/Deep
- Evidence-based: Yes/No/Mixed
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.
- 9d ago First seen · 143 lines · 42 tokens per session scan A e8ac2f2f80ab
reddit-thread-analyzer is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,016 once invoked, about $0.0002 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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