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ci-scan

Analyze recent dotnet/machinelearning MachineLearning-CI failures locally, identify stable failure signatures, deduplicate them against Known Build Error issues, and draft up to three actionable KBE issues. Use when asked to scan ML.NET CI, investigate recurring main-branch failures, or run the former ci-scan agent locally.

72

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

86%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.

A concise, well-structured instruction-only skill with a clear sequenced workflow, explicit validation and approval gating for batch issue-writing, and correctly placed one-level references. Concrete endpoint URLs would push actionability to the top anchor.

Suggestions

Inline the exact anonymous AzDO ('dnceng-public/public') and Helix endpoint URLs so the workflow is fully executable without consulting the referenced files.

Add an explicit validate-before-draft feedback loop (e.g., 'if a signature fails a gate, re-classify and re-check') to make the recovery path as concrete as the approval gate.

DimensionReasoningScore

Conciseness

Lean and efficient across roughly 40 lines with no concept-padding; it assumes Claude's competence and every line earns its place (inputs table, workflow, substitutions, validation).

5 / 5

Actionability

Provides concrete, executable guidance such as 'gh auth status', the '/tmp/mlnet-ci-scan/' state path, named anonymous endpoints, a three-draft cap, and placeholder substitution rules, but omits exact AzDO/Helix endpoint URLs leaving minor gaps.

4 / 5

Workflow Clarity

An explicit 8-step sequence plus a dedicated Validation section and an approval gate ('Apply drafts only after the user explicitly approves') provides clear checkpoints for this batch/issue-writing skill, with only minor validation gaps.

4 / 5

Progressive Disclosure

Acts as a clear overview with well-signaled, one-level-deep references to references/playbook.md and references/ci-scan.instructions.md (both present in the bundle), with detail appropriately split into those files.

5 / 5

Total

18

/

20

Passed

Description

92%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.

A strong, specific description that clearly states multiple concrete capabilities and provides explicit use-when triggers tied to a narrow domain. Minor room to expand trigger synonyms, but otherwise exemplary.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Analyze ... failures', 'identify stable failure signatures', 'deduplicate them against Known Build Error issues', and 'draft up to three actionable KBE issues' — giving comprehensive coverage.

5 / 5

Completeness

Explicitly answers both what it does (four concrete actions) and when to use it via a clear 'Use when ...' clause with concrete trigger phrases.

5 / 5

Trigger Term Quality

Natural triggers like 'scan ML.NET CI', 'investigate recurring main-branch failures', and 'run the former ci-scan agent locally' are present, but a few common phrasings or synonyms are missing, so it is not the top anchor.

4 / 5

Distinctiveness Conflict Risk

Highly niche scope (dotnet/machinelearning CI, KBE issues, former ci-scan agent) gives it a clear trigger footprint with minimal overlap risk against other skills.

5 / 5

Total

19

/

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
dotnet/machinelearning
Reviewed

Table of Contents

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