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Get Started Free →Monitor what developers say about your brand, competitors, and the problems they're solving. Track mentions and conversations across GitHub, Hacker News, Reddit, Stack Overflow, Twitter, and Discord. Trigger phrases: "developer listening", "monitor developer conversations", "track...
.claude/skills/sickn33-developer-listening/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 69% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-08 | ✓→✗ | ▼ Worse | 45% | 0% |
| case-15 | ✓→✓ | ＝ Same ✓ | 45% | 0% |
Use this skill when you need monitor what developers say about your brand, competitors, and the problems they're solving. Track mentions and conversations across GitHub, Hacker News, Reddit, Stack Overflow, Twitter, and Discord. Trigger phrases: "developer listening", "monitor developer conversations", "track...
Monitor developer conversations across platforms to understand sentiment, find engagement opportunities, and gather competitive intelligence.
Developer listening is the practice of systematically monitoring what developers say about your brand, competitors, and the problems your product solves. Unlike traditional social listening, developer listening requires monitoring technical platforms where developers actually spend time: GitHub, Hacker News, Reddit programming communities, Stack Overflow, Twitter/X, and Discord servers.
Effective developer listening helps you:
Before setting up monitoring, organize keywords into categories:
Brand Keywords:
Competitor Keywords:
Problem Keywords:
Buy Intent Keywords:
Choose a social listening tool that aggregates mentions across developer platforms (GitHub, Hacker News, Reddit, Stack Overflow, Twitter).
Recommended Keyword Structure:
GitHub:
Hacker News:
Reddit:
Stack Overflow:
Twitter/X:
Discord:
Not all mentions deserve equal attention. Prioritize based on:
High Priority (Respond within hours):
Medium Priority (Respond within 24-48 hours):
Low Priority (Monitor and aggregate):
Most monitoring tools offer sentiment filtering. Key queries to set up:
Frustrated Users:
Questions and Recommendations:
Buy Intent Signals:
Competitor Mentions:
Positioning Shifts:
Community Sentiment:
Track trends over time using your monitoring tool's analytics:
Use a monitoring tool that tracks developer platforms. Key capabilities to look for:
GitHub Search:
gh search issues and gh search repos for GitHub-specific monitoringTwitter/X Search:
Reddit:
User request:
> Monitor what developers say about your brand, competitors, and the problems they're solving.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 13,732 | 10,326 | -25% | 1 | 1 | 0% | 2,180 | 3,209 | +47% | 0 | 0 | — |
case-15 | pass→pass | 14,989 | 10,966 | -27% | 1 | 1 | 0% | 2,328 | 3,366 | +45% | 0 | 0 | — |
case-02 | pass→pass | 8,759 | 5,059 | -42% | 1 | 1 | 0% | 1,567 | 2,559 | +63% | 0 | 0 | — |
case-03 | fail→pass | 10,282 | 7,266 | -29% | 1 | 1 | 0% | 1,640 | 2,778 | +69% | 0 | 0 | — |
case-04 | pass→pass | 11,113 | 10,211 | -8% | 1 | 1 | 0% | 1,750 | 3,293 | +88% | 0 | 0 | — |
case-05 | pass→pass | 10,983 | 7,279 | -34% | 1 | 1 | 0% | 1,764 | 2,842 | +61% | 0 | 0 | — |
case-06 | pass→pass | 14,162 | 8,508 | -40% | 1 | 1 | 0% | 2,139 | 2,909 | +36% | 0 | 0 | — |
case-07 | pass→pass | 4,745 | 4,188 | -12% | 1 | 1 | 0% | 883 | 2,246 | +154% | 0 | 0 | — |
case-08 | pass→fail | 16,224 | 13,234 | -18% | 1 | 1 | 0% | 2,538 | 3,682 | +45% | 0 | 0 | — |
case-09 | fail→fail | 12,634 | 11,751 | -7% | 1 | 1 | 0% | 2,080 | 3,438 | +65% | 0 | 0 | — |
case-10 | pass→pass | 13,568 | 11,185 | -18% | 1 | 1 | 0% | 2,254 | 3,457 | +53% | 0 | 0 | — |
case-16 | pass→pass | 16,358 | 8,402 | -49% | 1 | 1 | 0% | 2,361 | 2,940 | +25% | 0 | 0 | — |
case-11 | pass→pass | 13,182 | 10,703 | -19% | 1 | 1 | 0% | 2,350 | 3,505 | +49% | 0 | 0 | — |
case-12 | fail→fail | 18,315 | 12,011 | -34% | 1 | 1 | 0% | 3,114 | 3,636 | +17% | 0 | 0 | — |
case-13 | pass→pass | 17,110 | 13,241 | -23% | 1 | 1 | 0% | 2,512 | 3,702 | +47% | 0 | 0 | — |
case-14 | pass→pass | 11,455 | 4,490 | -61% | 1 | 1 | 0% | 1,934 | 2,261 | +17% | 0 | 0 | — |
case-17 | pass→pass | 12,185 | 9,377 | -23% | 1 | 1 | 0% | 1,797 | 3,031 | +69% | 0 | 0 | — |
case-18 | pass→pass | 18,592 | 12,614 | -32% | 1 | 1 | 0% | 2,904 | 3,681 | +27% | 0 | 0 | — |
case-19 | fail→pass | 12,527 | 8,664 | -31% | 1 | 1 | 0% | 1,820 | 2,820 | +55% | 0 | 0 | — |
case-20 | fail→fail | 24,134 | 20,006 | -17% | 1 | 1 | 0% | 3,773 | 4,502 | +19% | 0 | 0 | — |
case-21 | fail→fail | 11,964 | 14,529 | +21% | 1 | 1 | 0% | 2,053 | 4,246 | +107% | 0 | 0 | — |
case-22 | fail→fail | 21,260 | 17,608 | -17% | 1 | 1 | 0% | 3,492 | 4,392 | +26% | 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. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
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.