CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

agent-ops-docker-review

Docker image reviews, optimization, and step-building guidance. Analyzes Dockerfiles for best practices, security issues, and anti-patterns.

59

Quality

74%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

—

The risk profile of this skill

Fix and improve this skill with Tessl

tessl review fix ./analysis/agent-ops-docker-review/SKILL.md

The canonical home for this skill is agent-ops-docker-review in majiayu000/claude-skill-registry

SKILL.md
Quality
Evals
Security

Quality

Content

71%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 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.

DimensionReasoningScore

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

Description

66%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 specific, uses natural trigger terms, and occupies a distinct Docker-focused niche. Its main weakness is the complete absence of a 'Use when...' trigger clause, which caps completeness and leaves invocation conditions implicit.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user mentions Docker, Dockerfiles, container images, or wants to optimize or security-check a Docker image.'

Include common synonyms and file references users actually say, such as 'container' and 'Dockerfile', to broaden natural trigger matching.

Mention the scanning capability ('run security scanners on built images') in the description so the Scan mode is discoverable from the description alone.

DimensionReasoningScore

Specificity

Names the domain (Docker images/Dockerfiles) and several concrete actions ('reviews, optimization, and step-building guidance. Analyzes Dockerfiles for best practices, security issues, and anti-patterns'), with only minor coverage gaps such as security scanning of built images. Not 5 because it does not enumerate the full set of capabilities (e.g., interactive building, scanning), and not 3 because it lists more than 1-2 concrete actions.

4 / 5

Completeness

Has a clear 'what' (analyzes Dockerfiles for best practices, security, anti-patterns) but no 'Use when...' clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines. The 'when' is entirely absent rather than weakly implied, so it cannot score 4.

3 / 5

Trigger Term Quality

Good natural keyword coverage: 'Docker image', 'Dockerfiles', 'reviews', 'optimization', 'security issues' — terms a user would naturally say. Not 5 because common synonyms like 'container', 'image scanning', or vulnerability-related phrasing are missing; not 3 because the terms present go beyond minimal domain naming.

4 / 5

Distinctiveness Conflict Risk

The Docker/Dockerfile niche is clearly distinct with dedicated triggers ('Dockerfiles', 'Docker image'), with only minor overlap risk against generic linting or CI skills. Not 5 because 'best practices' and 'optimization' are broad phrases that could collide with general code-review skills.

4 / 5

Total

15

/

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

majiayu000/claude-skill-registry-data

Path

analysis/agent-ops-docker-review/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.