Compare  ·  Crypto & Web3

eval-driven-dev vs investigating-ci-failures

Which web3 tool is right for you?

Notable score gap. investigating-ci-failures scores meaningfully higher; see individual reviews for nuance.


01 — TL;DR

If you need trigger clarity above all else, pick investigating-ci-failures (4.5/5). eval-driven-dev (3.1/5) is a reasonable alternative if you're already in its source ecosystem. They overlap in web3 tool territory.


02 — At a glance

Side by side

eval-driven-dev

3.1/5

Category
Crypto & Web3
Source
skillsmp.com
Tier
Reviewed
First published
2026-05-22
Trigger clarity
4.5
4.5
Output specificity
4.0
4.0
Scope precision
4.5
4.5
Self-containment
4.0
4.0
Reusability
3.5
3.5

investigating-ci-failures

4.5/5

Category
Crypto & Web3
Source
skillsmp.com
Tier
Reviewed
First published
2026-07-20
Trigger clarity
5.0
5.0
Output specificity
4.5
4.5
Scope precision
5.0
5.0
Self-containment
4.0
4.0
Reusability
3.5
3.5

03 — Dimension breakdown

Where they differ

  1. Trigger clarity. trigger clarity: investigating-ci-failures is clearly stronger (4.5 vs 5.0). For workloads where this dimension matters, prefer investigating-ci-failures.
  2. Output specificity. output specificity: investigating-ci-failures is clearly stronger (4.0 vs 4.5). For workloads where this dimension matters, prefer investigating-ci-failures.
  3. Scope precision. scope precision: investigating-ci-failures is clearly stronger (4.5 vs 5.0). For workloads where this dimension matters, prefer investigating-ci-failures.
  4. Self-containment. self-containment: eval-driven-dev and investigating-ci-failures score essentially the same (4.0 vs 4.0). Neither has an edge here.
  5. Reusability. reusability: eval-driven-dev and investigating-ci-failures score essentially the same (3.5 vs 3.5). Neither has an edge here.
04 — The decision

Which to pick

When to choose eval-driven-dev

  • The web3 tool convention you're working in matches eval-driven-dev's scope.

When to choose investigating-ci-failures

  • Your workload emphasizes trigger clarity — investigating-ci-failures scores 4.5 vs 5.0 here.
  • Your workload emphasizes output specificity — investigating-ci-failures scores 4.0 vs 4.5 here.
  • Your workload emphasizes scope precision — investigating-ci-failures scores 4.5 vs 5.0 here.
05 — Use cases

Scenario by scenario

Scenario Winner Why
Agent must auto-select between many web3 tools investigating-ci-failures Trigger clarity decides — clearer triggers reduce routing errors.
Output must be a specific file format or structured data investigating-ci-failures 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.
06 — FAQ

Common questions

Which is better, eval-driven-dev or investigating-ci-failures?
investigating-ci-failures ranks higher overall (4.5 vs 3.1 on our 0–5 rubric). That said, the better choice depends on which dimensions matter most for your use case.
Are eval-driven-dev and investigating-ci-failures both free to use?
Both skills are free and open-source (or freely licensed). eval-driven-dev: See source repo. investigating-ci-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 eval-driven-dev and investigating-ci-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?
eval-driven-dev is sourced from skillsmp.com (curated marketplace). investigating-ci-failures is sourced from skillsmp.com (curated marketplace). We verify each skill across multiple sources where possible; eval-driven-dev appears in 1 source, investigating-ci-failures in 1.

436 words · Tier S (same-cluster)