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AD docker-helper

Manage Docker containers, images, volumes, networks, and Docker Compose projects. Use when the user wants to: (1) List/start/stop/restart Docker containers, (2) View container logs and stats, (3) Build and manage Docker images, (4) Create and manage docker-compose.yml files, (5) Clean up unused Docker resources, (6) Debug container issues. Best for developers, DevOps engineers, and anyone running Docker on their server.

ClawHub Agent Skills author: ericlooi504 v1.0.0 MIT-0 2 files body ≈ 907 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorDockerInfrastructuretype 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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 · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 48/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 40Consistency. Frontmatter name (docker-helper) differs from the folder (eric-docker-helper)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 100Steps. 26 steps
    • 100When it triggers. States when to use and when not to
    • 100Execution cost. Instruction body is 907 tokens

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 423: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This skill is a transparent Docker helper, but it can affect local containers, images, volumes, and services if the user approves those actions.
    LLM: benign (high) · VirusTotal: · 29 May 2026