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BD docker-optimizer

Optimize Dockerfiles with multi-stage builds, layer caching, security best practices, and size reduction techniques

ClawHub Agent Skills author: Michael Atamuk v1.0.0 MIT-0 3 files body ≈ 5 575 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorDockerGitHubInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
98
Quality 40%
63
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • low Exfiltration read-dotenv DOCKER_PATTERNS.md:318
    Reads a .env file (Dockerfile instruction (build context, not runtime exfiltration); documentation of a security skill)
    COPY .env /app/.env  # Secret persists in layer
    Dockerfilesecurity skill

A further 1 matches are quotations in this security skill's documentation and are not counted as findings.

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5575 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 47/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5575 tokens
  • 100Steps. 180 steps
  • 100Consistency. Name and required fields are in place
  • low 14 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 115: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -2localhost URLs: will not work for another user
  • -220 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 180 items
  • +4Has examples (38 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.

External checks

ClawHub: clean
This skill is a Dockerfile optimization guide with disclosed, purpose-aligned examples and no hidden execution or persistence behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026