BC gitlab-agent
Operate assigned GitLab work with owner-verified project access and guarded MR delivery.
Operate assigned GitLab work with owner-verified project access and guarded MR delivery.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
IntegrationGitLabAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
The same skill appears in 1 more place: ClawHub
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 0
✓ No critical or high findings
Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5669 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 59/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (gitlab-agent) differs from the folder (xrowgmbh-gitlab-agent)
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, git) that frontmatter does not declare
- 70Execution cost. Instruction body is 5669 tokens
- 85Steps. 96 steps, 2 vague phrases
- 100Failures and branches. 1 branches, has a failure section
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (6 tags): a typed call is more reliable
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 88: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -5TODO / placeholder text left in the skill
- -32 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Structure: 36 headings
- +3Step-by-step instructions: 96 items
- +4Has examples (30 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.
External checks
ClawHub: suspicious
This skill is not clearly malicious, but it asks for broad recurring GitLab automation with automatic writes and too little action-by-action user control.
LLM: suspicious (high) · 11 Sept 2026