Compare · Agent Infrastructure
author-contributions vs exploring-ai-failures
Which agent infrastructure piece is right for you?
01 — TL;DR
If you need trigger clarity above all else, pick exploring-ai-failures (4.2/5). author-contributions (3.2/5) is a reasonable alternative if you're already in its source ecosystem. They overlap in agent infrastructure piece territory.
Side by side
3.2/5
4.2/5
Where they differ
- Trigger clarity. trigger clarity: exploring-ai-failures is clearly stronger (4.5 vs 5.0). For workloads where this dimension matters, prefer exploring-ai-failures.
- Output specificity. output specificity: author-contributions is clearly stronger (4.0 vs 3.5). For workloads where this dimension matters, prefer author-contributions.
- Scope precision. scope precision: author-contributions and exploring-ai-failures score essentially the same (4.5 vs 4.5). Neither has an edge here.
- Self-containment. self-containment: author-contributions and exploring-ai-failures score essentially the same (4.0 vs 4.0). Neither has an edge here.
- Reusability. reusability: author-contributions and exploring-ai-failures score essentially the same (3.5 vs 3.5). Neither has an edge here.
Which to pick
When to choose author-contributions
- Your workload emphasizes output specificity — author-contributions scores 4.0 vs 3.5 here.
- You weight community adoption — author-contributions's upstream repo has 185,014 stars vs 35,356.
- The agent infrastructure piece convention you're working in matches author-contributions's scope.
When to choose exploring-ai-failures
- Your workload emphasizes trigger clarity — exploring-ai-failures scores 4.5 vs 5.0 here.
- The agent infrastructure piece convention you're working in matches exploring-ai-failures's scope.
Scenario by scenario
| Scenario | Winner | Why |
|---|---|---|
| Agent must auto-select between many agent infrastructure pieces | exploring-ai-failures | Trigger clarity decides — clearer triggers reduce routing errors. |
| Output must be a specific file format or structured data | author-contributions | Output specificity determines whether downstream tools can rely on the result. |
| Skill must be readable and complete out of the box | either | Self-containment matters when you're not the original author. |
| Cross-team or cross-project reuse expected | either | Reusability separates one-off scripts from durable building blocks. |
Common questions
- Which is better, author-contributions or exploring-ai-failures?
- exploring-ai-failures ranks higher overall (4.2 vs 3.2 on our 0–5 rubric). That said, the better choice depends on which dimensions matter most for your use case.
- Are author-contributions and exploring-ai-failures both free to use?
- Both skills are free and open-source (or freely licensed). author-contributions: See source repo. exploring-ai-failures: 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 author-contributions and exploring-ai-failures 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?
- author-contributions is sourced from skillsmp.com (curated marketplace). exploring-ai-failures is sourced from skillsmp.com (curated marketplace). We verify each skill across multiple sources where possible; author-contributions appears in 1 source, exploring-ai-failures in 1.