name: alfred-search-reddit description: > Search Reddit for community insights and sentiment. Use when: "check Reddit", "what's Reddit saying", "r/LocalLLaMA", "r/MachineLearning", "Reddit sentiment". allowed-tools: Read, Write, Edit, Glob, WebFetch, WebSearch, mcp__claude-in-chrome__*

Alfred Search: Reddit

Search Reddit for community sentiment, real user experiences, and contrarian takes. Reddit provides signal that curated sources miss.

Why This Matters

Reddit captures what HN and Techmeme don't:

• Real user experiences — actual problems, not polished press releases
• Contrarian takes — people willing to call BS on hype
• Niche expertise — deep domain knowledge in specific subreddits
• Sentiment indicators — crowd wisdom and meme energy

Config

Read from: agents/agent-alfred/context/RESEARCH_CONFIG.md → Topics of Interest

Target Subreddits

Primary (Always Search)

Secondary (When Relevant)

Execution

1. Navigate to target subreddits (hot/top today)
2. Search for posts matching topics of interest
3. For each high-signal post:
   - Capture title and key points
   - Note upvotes/comments (engagement)
   - Read top comments for quality takes
   - Identify contrarian views
4. Group findings by theme
5. Add judgment — signal or noise?
6. Output to research report

What to Look For

High-Signal Posts

• Real user experiences with products/tools
• Technical deep dives with code/benchmarks
• Industry insiders sharing context
• Contrarian takes with good reasoning
• Emerging consensus forming

Noise to Skip

• Low-effort memes (unless sentiment indicator)
• Beginner questions
• Repetitive complaints
• Obvious shilling/FUD

Output

Write to agents/agent-alfred/outputs/YYYY-MM-DD-HHmm-search-reddit.md:

# Reddit Search — YYYY-MM-DD

## Signal Summary

| Signal | Subreddit | Relevance | Tags |
|--------|-----------|-----------|------|
| [Key insight] | r/LocalLLaMA | HIGH | #llm #inference |
| [Sentiment shift] | r/wallstreetbets | MEDIUM | #sentiment #NVDA |

## Insights

_Searched {N} posts across {N} subreddits_

### {Theme 1}: {Title}

**Subreddit:** r/LocalLLaMA
**Post:** <a href="url" target="_blank">{Title}</a>
**Engagement:** {upvotes} upvotes, {comments} comments

**Key Claim:**
> "{Quote or summary of main point}"

**Top Comments:**
- "{Best comment}" — u/username
- "{Contrarian view}" — u/username

**Judgment:** Why this matters / doesn't matter

---

### {Theme 2}: {Title}

[repeat for each insight worth capturing]

## Sentiment Check

| Topic | Reddit Sentiment | HN/Web Sentiment | Gap |
|-------|-----------------|------------------|-----|
| Claude vs GPT-4 | Claude preferred | Mixed | Reddit more bullish |
| NVDA valuation | Overvalued | Justified | Reddit more bearish |

## Contrarian Corner

Takes that go against mainstream narrative:

- **On {topic}:** "{contrarian view}" — r/subreddit
- **On {topic}:** "{contrarian view}" — r/subreddit

## Sources Searched

- [x] r/LocalLLaMA (X posts)
- [x] r/MachineLearning (X posts)
- [x] r/artificial (X posts)
- [ ] r/wallstreetbets (skipped — not relevant today)

Voice

Reddit is raw. Reflect that:

• Note when something is meme vs serious
• Flag when user might be shilling
• Highlight when crowd wisdom contradicts expert consensus

Benchmark

Success Criteria

• Primary subreddits searched
• High-signal posts captured with links
• Top comments included for context
• Sentiment compared to other sources
• Contrarian views highlighted
• Output formatted correctly

Notes

• Crowd wisdom has limits — Reddit can be wrong, especially on hype cycles
• Check credentials — some users have real expertise, many don't
• Engagement matters — high upvotes + comments = community resonance
• Look for the signal in the noise — buried comments often have the best takes