SKILLEMALL.ai

BF docker-sandbox

Create and manage Docker sandboxed VM environments for safe agent execution。Use。Use when 需要代码生成、编程辅助、调试测试、开发部署时使用。不适用于无明确技术栈的模糊需求。适用于独立开发者、企业团队和自动化工作流场景。

ClawHub Hermes author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 1 388 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 33/100 · Will not run — References files that are not bundled: references/style.md, assets/output.json

GeneratorDockerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
57
Run on models
none yet
Process rating
F
33/100
Will not run
References files that are not bundled: references/style.md, assets/output.json
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
  2. The text references files that are not there: add them or drop the references.
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

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Map keys must be unique at line 28, column 1: category: Development homepage: "https://skillhub.cn/skill/" ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning description-long-hermes description is 153 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning missing-ref reference to a missing file: references/style.md
  • warning missing-ref reference to a missing file: assets/output.json
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "summary_zh"
  • note frontmatter-key unknown frontmatter key "tools"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 33/100

Will not run. References files that are not bundled: references/style.md, assets/output.json
  • 0Tools and files. 2 referenced file(s) missing: references/style.md, assets/output.json
  • 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. 4 mutating operations with no state check
  • 100Steps. 24 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1388 tokens
  • low 16 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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 153: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (8 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.

External checks

ClawHub: suspicious
This skill appears intended to manage Docker sandboxes, but its broad and mismatched instructions could cause agents to run powerful commands or expose project files in more situations than users expect.
LLM: suspicious (high) · 10 Aug 2026