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SkillHub TRACE Evaluator

Data Analysis Updated 2026.08.29

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Please follow https://skillhub.cn/install/skillhub.md to install @user_290ac21c/skillhub-trace-evaluator.

About this skill

Problem

As the number of Skill packages grows, reading only SKILL.md is not enough to judge whether a package is trustworthy, maintainable, or suitable for a real workflow. Manual review can miss recurring issues: dependencies, output contracts, error handling, and convention fit may look fine in description text but behave inconsistently in practice. This skill frames the review around the TRACE five-dimension standard: Trust, Reliability, Adaptability, Convention, and Effectiveness, so the assessment is evidence-based and reproducible.

How It Works

The evaluator first locates the target package. It supports local directories, local zip files, installed skill paths, or a slug after a local copy is obtained. If the package cannot be found, it stops instead of guessing. It then follows references/explore.md to list files, read SKILL.md and key documents, and sample large files only where needed. Scoring is done one dimension at a time using the matching reference. Each subitem receives a score in (0, 5.0], a developer-facing reason, and a plain-user-facing userReason. trust.scan is fixed as skipped because this skill does not perform security scanning; it evaluates items such as trust.domestic. Finally, it assembles JSON according to output-schema.md and runs validation. The result is delivered only when validation returns exit 0.

Boundaries

It is best used for structured quality review of local Skill packages. The output includes a readable five-dimension table, subitem scores, brief reasons, and two summaries, plus the full validated JSON. It should not be treated as a security scanner, dependency reachability tester, or complete functional test. If only a slug is available without a local copy, evaluation should be stopped.

Use Cases

  • Before merging Skill packages, check a local directory against TRACE Trust, Reliability, and Convention subitems.
  • When reviewing a local zip, read SKILL.md and key files to produce a TRACE score table with per-subitem reasons.
  • Before release, evaluate an installed Skill path and ensure the output-schema JSON passes validation for team delivery.

Best For

  • Platform engineers merging Skill packages: need a reviewable TRACE score and JSON evidence.
  • Technical leads maintaining local Skill repos: need to compare Reliability, Convention, and Effectiveness gaps.
  • QA engineers auditing third-party Skill packages: need fixed subitem scores, pros and cons, and uncovered items.
  • Skill package authors preparing team delivery: need validated score JSON and readable summaries.