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eval-creator-ci

[Beta] CI-only eval regression runner using gh-aw (GitHub Agentic Workflows). Runs all eval cases in .evals/ on a schedule or per-PR, reports pass/fail results, and can block merges on regressions. Also creates new eval cases from promoted patterns flagged by learning-aggregator-ci. Use when: you want automated regression testing of promoted rules in CI/headless pipelines. For interactive eval creation and runs, use eval-creator.

65

Quality

78%

Does it follow best practices?

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SecuritybySnyk

Low

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tessl review fix ./agent-plugin/skills/eval-creator-ci/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured and largely actionable with concrete commands, YAML schemas, and clearly sequenced workflows for both modes. Main improvements are tightening redundant prose and providing executable templates for the verification methods.

Suggestions

Remove the Purpose section or fold it into the description, since it duplicates the frontmatter, and trim the Context Limitation / Differences sections to essentials.

Add a concrete eval case file template with frontmatter and a worked example for each verification method (grep-check, command-check, file-check, rule-check) so the agent can copy-paste rather than infer syntax.

Add an explicit error-recovery feedback loop in Run mode (e.g., on a spurious failure, re-run or inspect the target file) to push workflow clarity to a 5.

DimensionReasoningScore

Conciseness

Mostly efficient with concrete commands and schemas, but the Purpose section restates the frontmatter description and the Context Limitation / Differences sections add length that could be tightened.

3 / 5

Actionability

Provides concrete executable install/compile commands and complete YAML output/trigger examples, but the verification methods (grep-check, command-check, etc.) are described conceptually rather than given as executable templates.

4 / 5

Workflow Clarity

Both Run and Create modes are clearly numbered with sequenced sub-steps, precondition checks, result-to-expected comparison, and a gate decision; validation is present so the batch cap does not apply, though an explicit error-recovery loop is absent.

4 / 5

Progressive Disclosure

Well-organized sections with a clearly signaled one-level-deep reference to references/workflow-example.md (verified to exist); core spec content is appropriately inlined in the overview.

4 / 5

Total

15

/

20

Passed

Description

88%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it states concrete capabilities, gives an explicit 'Use when' trigger, and carves out a clear CI/headless niche. Minor room to expand trigger synonyms and further reduce overlap with the interactive variant.

DimensionReasoningScore

Specificity

Lists multiple concrete actions ('Runs all eval cases in .evals/', 'reports pass/fail results', 'can block merges on regressions', 'creates new eval cases from promoted patterns'), giving comprehensive coverage of the skill's capabilities.

5 / 5

Completeness

Clearly answers both what it does and when to use it via an explicit 'Use when: you want automated regression testing of promoted rules in CI/headless pipelines' clause with concrete trigger phrases.

5 / 5

Trigger Term Quality

Good natural keyword coverage ('regression testing', 'CI', 'headless pipelines', 'per-PR', 'schedule') but a few common synonyms a user might say are missing, so it falls just below the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

Has a clear CI-only/headless niche with distinct gh-aw triggers, but explicitly contrasts with the closely related interactive 'eval-creator' sibling, indicating minor overlap risk.

4 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
pskoett/pskoett-ai-skills
Reviewed

Table of Contents

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