Content
71%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill body is highly actionable — executable templates, specific scanner commands, severity-coded rules, and guarded behaviors — with clear per-mode workflows. Its weaknesses are token weight from inline templates, duplicated examples, and mock reports, and a total lack of progressive disclosure: everything, including four language templates, lives in SKILL.md with no reference files.
Suggestions
Move the four language Dockerfile templates into a references/ file (e.g., references/templates.md) and keep one short example inline, trimming the duplicated optimized-Dockerfile example in Optimize mode.
Add an explicit validation step to Optimize mode: after generating the optimized Dockerfile, re-run the Review rules (or hadolint) and confirm every finding is resolved before presenting the result.
Shorten the mock report blocks to their skeleton (headers + one example row) to cut tokens while preserving the output format contract.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The prose is lean and assumes Claude's competence (no explanation of what Docker is), but the ~400-line body could be tightened: the Optimize-mode example Dockerfile largely duplicates the Python template, and three full mock reports (Review, Optimize, Scan outputs) consume tokens beyond what is needed to convey the format. Not 4 because the duplication and mock-report padding are more than 'minor instances'; not 2 because there is no conceptual over-explanation and every section is operational. | 3 / 5 |
Actionability | Fully executable guidance throughout: concrete rule IDs with severities (SEC001-OPT006, MNT001-004), copy-paste-ready scanner commands ('trivy image --severity HIGH,CRITICAL {{image}}', 'grype {{image}} --only-fixed'), complete Dockerfile templates per language with explicit placeholders, and concrete output file paths. Covers the common cases (Python, Node, Go, .NET). | 5 / 5 |
Workflow Clarity | Each mode has a clear numbered procedure (locate → analyze → report; interview → generate → review), plus checkpoints like tool detection at skill start, 'Review the generated files with user', and guardrails in Forbidden Behaviors. Not 5 because there is no explicit validate→fix→retry loop (e.g., re-running the review rules or hadolint on the optimized Dockerfile to confirm all findings are resolved); not 3 because checkpoints are mostly present and operations are guarded rather than missing. | 4 / 5 |
Progressive Disclosure | The body is well-sectioned with clear headers per mode, but it is a monolithic ~400-line file: the four full language templates and the three rule catalogs are inline content that clearly belongs in separate reference files (e.g., templates.md, rules.md), and no references exist at all. Fits anchor 3 ('content that should be separate is inline'); not 4 because nothing is offloaded to a referenced file, and not 2 because internal structure and navigation within the file are good. | 3 / 5 |
Total | 15 / 20 Passed |