BC docker-pilot
Safe, intelligent Docker container management — fleet status, lifecycle operations, cleanup, compose stacks, troubleshooting, and security hardening. Classifies every command by risk level (READ / RISKY / DESTRUCTIVE) with mandatory confirmation gates. Use when managing Docker containers, images, volumes, networks, compose stacks, or debugging container issues.
As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
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high Dangerous commands
cmd-destructive-fsSKILL.md:99Destructive filesystem command (wipes root/home/drive) (documentation of a security skill)docker exec CONTAINER rm -rf / # Destructive inside container — catch pattern
security skill
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5349 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "changelog"
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (python, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 5349 tokens
- 100Steps. 46 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 17 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
- -2localhost URLs: will not work for another user
- -236 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 363: enough signal without eating the budget
- +4Structure: 55 headings
- +3Step-by-step instructions: 46 items
- +4Has examples (35 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.