SKILLEMALL.ai
GitHub ↗ RU Sign in

CD project-containerize

对项目进行完整的容器化适配。自动检测分析项目代码和文档，配置和优化代码容器化打包方案，生成专用Dockerfile，生成专用compose.yaml用于快捷部署，输出说明文档，最终生成适配项目的容器化部署方案。触发词：容器化、Docker、docker run、docker-compose、Dockerfile、容器部署、镜像打包、生成 Dockerfile、创建 Dockerfile、自动 Dockerfile、项目容器化、容器化适配、容器化改造

ClawHub Agent Skills author: zhu v1.0.0 MIT-0 8 files body ≈ 2 366 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

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
C
74/100
safety, quality, tests
Safety 60%
99
Quality 40%
36
Run on models
none yet
Process rating
D
43/100
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration read-dotenv scripts/generate_deployment.py:59
    Reads a .env file (detector / deny-list definition)
    export $(cat .env | grep -v '^#' | xargs)
    detector

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error name-missing SKILL.md: frontmatter has no `name`
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "title"

Process rating: all ten parameters 43/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 23 mutating operations with no state check
  • 60Tools and files. Uses tools (python, node) that frontmatter does not declare
  • 100Steps. 109 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2366 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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -36 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 226: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 109 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 36.

External checks

ClawHub: suspicious
This skill is a legitimate Docker containerization helper, but it can copy real configuration secrets and run project-controlled Docker builds without strong review controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026