Compare · Agent Infrastructure
agent-eval vs od-contribute
Which agent infrastructure piece is right for you?
01 — TL;DR
Both agent-eval and od-contribute are strong choices for agent infrastructure pieces — they score within 0.15 of each other on our composite (4.2 vs 4.1). Pick based on which source you trust more, not on raw score.
Side by side
4.2/5
4.1/5
Where they differ
- Trigger clarity. trigger clarity: od-contribute is clearly stronger (4.5 vs 5.0). For workloads where this dimension matters, prefer od-contribute.
- Output specificity. output specificity: agent-eval is clearly stronger (4.5 vs 4.0). For workloads where this dimension matters, prefer agent-eval.
- Scope precision. scope precision: agent-eval and od-contribute score essentially the same (4.5 vs 4.5). Neither has an edge here.
- Self-containment. self-containment: od-contribute is clearly stronger (3.5 vs 4.0). For workloads where this dimension matters, prefer od-contribute.
- Reusability. reusability: a meaningful gap. agent-eval scores 4.0 vs 2.5 for the other. If you need this dimension, agent-eval is the right pick.
How to decide
- Pick agent-eval 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 infrastructure pieces | od-contribute | Trigger clarity decides — clearer triggers reduce routing errors. |
| Output must be a specific file format or structured data | agent-eval | Output specificity determines whether downstream tools can rely on the result. |
| Skill must be readable and complete out of the box | od-contribute | Self-containment matters when you're not the original author. |
| Cross-team or cross-project reuse expected | agent-eval | Reusability separates one-off scripts from durable building blocks. |
Common questions
- Which is better, agent-eval or od-contribute?
- 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 agent-eval and od-contribute both free to use?
- Both skills are free and open-source (or freely licensed). agent-eval: See source repo. od-contribute: 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 agent-eval and od-contribute 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?
- agent-eval is sourced from skillsmp.com (curated marketplace). od-contribute is sourced from skillsmp.com (curated marketplace). We verify each skill across multiple sources where possible; agent-eval appears in 1 source, od-contribute in 1.