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ci-scan-feedback

Audit the local dotnet/machinelearning ci-scan skill using recent ci-scan issues, maintainer feedback, and an optional local scan report, then draft targeted prompt improvements. Use when reviewing scanner quality, investigating false-positive KBEs, or improving the local ci-scan methodology.

Quality

88%

Does it follow best practices?

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Adds up to 20 points to the overall score

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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 lean, well-structured instruction-only skill: efficient token use, a clearly sequenced workflow with security and edit-gating checkpoints, and clean one-level references to the sibling ci-scan skill. The only gaps are minor: no concrete command for the issue-collection step and no guidance for handling cross-check mismatches.

Suggestions

Add a concrete issue-collection command (e.g., a gh issue list invocation with the `[ci-scan]` title filter, repo, and updated-since window) so step 2 is copy-paste executable.

Add a short mismatch-handling branch after step 6 (e.g., "if the report's tally or skipped reasons disagree with the issue set, flag the discrepancy and reconcile before proposing edits") to close the workflow's feedback loop.

State what to do when zero relevant issues are found in the window (e.g., report that and skip to report-only cross-check) so the workflow has a defined path for the empty case.

DimensionReasoningScore

Conciseness

The body is ~40 lean lines: an input table, eight numbered steps, and a bulleted output list, with no padding and no explanation of concepts Claude already knows. Every token earns its place, matching anchor 5.

5 / 5

Actionability

Concrete, executable guidance throughout — "issues whose title starts with `[ci-scan]`", a 30-day default window, enumerated rubric criteria, untrusted-data handling, and an explicit edit gate — but the issue-collection step lacks an executable command (e.g., a `gh issue list` invocation with the filter). Mostly executable with minor gaps, matching anchor 4.

4 / 5

Workflow Clarity

A clear, well-sequenced 8-step workflow with real checkpoints (rubric scoring in step 5, report cross-check in step 6, "Edit files only when the user explicitly asks" in step 8), but no error-recovery loop for mismatches found during the cross-check. This is a read-and-propose workflow rather than a destructive/batch mutation, so the cap-at-3 rule does not apply; it sits at anchor 4 rather than 5, which requires explicit validate-fix-retry feedback loops.

4 / 5

Progressive Disclosure

The skill is under 50 lines, needs no bundle files of its own, and is cleanly organized into Inputs/Workflow/Output sections with one-level-deep, clearly signaled links to the sibling ci-scan skill's files. The simple-skill exception applies and the structure matches anchor 5.

5 / 5

Total

18

/

20

Passed

Description

87%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 description: third-person, concrete about domain and actions, with an explicit "Use when…" clause covering three specific trigger scenarios. It is tightly scoped to the ci-scan niche with essentially no conflict risk, with only minor room to broaden natural keyword coverage.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions ("Audit the local dotnet/machinelearning ci-scan skill", "draft targeted prompt improvements") with inputs enumerated ("recent ci-scan issues, maintainer feedback, and an optional local scan report"), but coverage of what the audit covers is not fully comprehensive. It lists several specific actions, matching anchor 4 rather than 3 (more than 1-2 actions) or 5 (comprehensive multi-action enumeration).

4 / 5

Completeness

Explicitly answers both: what ("Audit the … ci-scan skill … then draft targeted prompt improvements") and when ("Use when reviewing scanner quality, investigating false-positive KBEs, or improving the local ci-scan methodology") with concrete trigger phrases, matching anchor 5's example shape.

5 / 5

Trigger Term Quality

"reviewing scanner quality, investigating false-positive KBEs, or improving the local ci-scan methodology" gives good natural-phrase coverage users of this niche would say. A few natural variations are missing (e.g., "false positives", "scanner tuning", "prompt improvements"), so it fits anchor 4 rather than 5's synonym-level density.

4 / 5

Distinctiveness Conflict Risk

"dotnet/machinelearning ci-scan skill" and "false-positive KBEs" define a clear niche with distinct triggers that no generic skill would match, and the description uses third-person voice throughout ("Audit", "draft") as required. Minimal conflict risk, matching anchor 5.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 4 suspicious

Warning

Total

15

/

16

Passed

Repository

dotnet/machinelearning

Path

.github/skills/ci-scan-feedback/SKILL.md

Reviewed

Table of Contents

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