01 — TL;DR
Both earnings-analysis and experimental-design are strong choices for agent skills — they score within 0.15 of each other on our composite (4.4 vs 4.3). Pick based on which source you trust more, not on raw score.
Side by side
4.4/5
4.3/5
Where they differ
- Trigger clarity. trigger clarity: experimental-design is clearly stronger (4.5 vs 5.0). For workloads where this dimension matters, prefer experimental-design.
- Output specificity. output specificity: a meaningful gap. earnings-analysis scores 4.5 vs 3.5 for the other. If you need this dimension, earnings-analysis is the right pick.
- Scope precision. scope precision: earnings-analysis and experimental-design score essentially the same (4.5 vs 4.5). Neither has an edge here.
- Self-containment. self-containment: earnings-analysis is clearly stronger (4.5 vs 4.0). For workloads where this dimension matters, prefer earnings-analysis.
- Reusability. reusability: experimental-design is clearly stronger (3.5 vs 4.0). For workloads where this dimension matters, prefer experimental-design.
How to decide
- Pick earnings-analysis if you weight community adoption — its upstream has more GitHub stars.
- If neither of the above tips the scale, default to the one whose author you've used before in other contexts — ecosystem familiarity compounds.
Scenario by scenario
| Scenario | Winner | Why |
|---|---|---|
| Agent must auto-select between many agent skills | experimental-design | Trigger clarity decides — clearer triggers reduce routing errors. |
| Output must be a specific file format or structured data | earnings-analysis | Output specificity determines whether downstream tools can rely on the result. |
| Skill must be readable and complete out of the box | earnings-analysis | Self-containment matters when you're not the original author. |
| Cross-team or cross-project reuse expected | experimental-design | Reusability separates one-off scripts from durable building blocks. |
Common questions
- Which is better, earnings-analysis or experimental-design?
- Neither is clearly better — they score within 0.15 of each other on our 0–5 composite. The decision should be driven by source preference, ecosystem fit, or specific dimension priorities (see "How to decide" above).
- Are earnings-analysis and experimental-design both free to use?
- Both skills are free and open-source (or freely licensed). earnings-analysis: See source repo. experimental-design: See source repo. Installation has no cost; usage costs depend on the underlying LLM tokens consumed when you invoke the skill.
- Can I install both earnings-analysis and experimental-design at the same time?
- Yes. Agent skills are not exclusive — an agent runtime (Claude Code, Codex, etc.) can have many skills installed and route to whichever matches the current task. Installing both is a low-cost way to keep your options open.
- Where do these skills come from?
- earnings-analysis is sourced from skillsmp.com (curated marketplace). experimental-design is sourced from skillsmp.com (curated marketplace). We verify each skill across multiple sources where possible; earnings-analysis appears in 1 source, experimental-design in 1.