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Get Started Free →Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
.claude/skills/onewave-ai-reddit-thread-analyzer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-08 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-14 | ✓→✗ | ▼ Worse | -32% | 0% |
| case-15 | ✓→✗ | ▼ Worse | -39% | 0% |
| case-16 | ✓→✗ | ▼ Worse | -45% | 0% |
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.
Use WebFetch to load the Reddit thread and extract:
Determine the dominant sentiment and emotional tone:
Identify the most impactful points:
Top Arguments in Favor (3-5 points):
Top Arguments Against (3-5 points):
Expert or Verified Opinions:
Determine what the community agrees on:
Flag heavily debated points:
Format the analysis clearly:
markdown# 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
User: "What does Reddit think about the new iPhone?"
Analysis steps:
Remember: Focus on substance over noise. Prioritize well-reasoned arguments over emotional reactions.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→fail | 13,428 | 5,972 | -56% | 1 | 1 | 0% | 2,085 | 1,416 | -32% | 0 | 0 | — |
case-01 | fail→fail | 4,550 | 27,952 | +514% | 1 | 1 | 0% | 896 | 4,404 | +392% | 0 | 0 | — |
case-02 | fail→fail | 6,999 | 6,245 | -11% | 1 | 1 | 0% | 512 | 1,480 | +189% | 0 | 0 | — |
case-03 | fail→fail | 13,727 | 5,882 | -57% | 1 | 1 | 0% | 2,367 | 1,481 | -37% | 0 | 0 | — |
case-04 | pass→pass | 11,528 | 12,183 | +6% | 1 | 1 | 0% | 1,983 | 3,131 | +58% | 0 | 0 | — |
case-05 | pass→pass | 12,955 | 12,929 | -0% | 1 | 1 | 0% | 2,619 | 3,868 | +48% | 0 | 0 | — |
case-06 | fail→fail | 2,445 | 4,994 | +104% | 1 | 1 | 0% | 399 | 1,822 | +357% | 0 | 0 | — |
case-07 | fail→fail | 5,803 | 18,869 | +225% | 1 | 1 | 0% | 1,071 | 3,614 | +237% | 0 | 0 | — |
case-08 | fail→pass | 12,987 | 13,841 | +7% | 1 | 1 | 0% | 2,203 | 3,099 | +41% | 0 | 0 | — |
case-09 | fail→pass | 17,237 | 19,342 | +12% | 1 | 1 | 0% | 2,820 | 4,413 | +56% | 0 | 0 | — |
case-10 | fail→fail | 20,425 | 5,836 | -71% | 1 | 1 | 0% | 3,010 | 1,261 | -58% | 0 | 0 | — |
case-11 | fail→fail | 6,232 | 10,786 | +73% | 1 | 1 | 0% | 1,044 | 1,294 | +24% | 0 | 0 | — |
case-12 | fail→fail | 12,182 | 6,112 | -50% | 1 | 1 | 0% | 2,041 | 1,547 | -24% | 0 | 0 | — |
case-13 | pass→pass | 18,863 | 20,459 | +8% | 1 | 1 | 0% | 3,033 | 4,236 | +40% | 0 | 0 | — |
case-15 | pass→fail | 12,717 | 4,291 | -66% | 1 | 1 | 0% | 2,056 | 1,251 | -39% | 0 | 0 | — |
case-16 | pass→fail | 14,696 | 4,645 | -68% | 1 | 1 | 0% | 2,347 | 1,282 | -45% | 0 | 0 | — |
case-17 | fail→fail | 15,794 | 7,614 | -52% | 1 | 1 | 0% | 2,483 | 1,254 | -49% | 0 | 0 | — |
case-18 | pass→fail | 12,340 | 6,321 | -49% | 1 | 1 | 0% | 1,946 | 1,328 | -32% | 0 | 0 | — |
case-19 | fail→fail | 11,282 | 5,732 | -49% | 1 | 1 | 0% | 1,915 | 1,300 | -32% | 0 | 0 | — |
case-20 | fail→fail | 6,211 | 5,352 | -14% | 1 | 1 | 0% | 1,029 | 1,280 | +24% | 0 | 0 | — |
case-21 | pass→fail | 11,842 | 4,538 | -62% | 1 | 1 | 0% | 1,862 | 1,342 | -28% | 0 | 0 | — |
case-22 | pass→fail | 15,995 | 4,723 | -70% | 1 | 1 | 0% | 2,586 | 1,254 | -52% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 8 counted toward the lift figure. The other 14 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of -18 percentage points is the difference between those two pass rates over the 8 comparable cases. 8 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.