DC ai-business
(no description)
As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".
If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.
How to improve
- Add a description to the frontmatter: without it the skill never triggers.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
medium Exfiltration
net-credential-useSKILL.md:73Credential used in a network call (verify the destination is the intended service)curl -H "Authorization: Bearer $WLDPASS_API_TOKEN" \
-
medium Exfiltration
net-credential-useSKILL.md:82Credential used in a network call (verify the destination is the intended service)curl -H "Authorization: Bearer $WLDPASS_API_TOKEN" \
-
medium Exfiltration
net-credential-useSKILL.md:95Credential used in a network call (verify the destination is the intended service)curl -H "Authorization: Bearer $WLDPASS_API_TOKEN" \
-
medium Exfiltration
net-credential-useSKILL.md:108Credential used in a network call (verify the destination is the intended service)curl -X POST -H "Authorization: Bearer $WLDPASS_API_TOKEN" \
-
medium Exfiltration
net-credential-useSKILL.md:124Credential used in a network call (verify the destination is the intended service)curl -X POST -H "Authorization: Bearer $WLDPASS_API_TOKEN" \
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
frontmatterSKILL.md: no YAML frontmatter block found - error
name-missingSKILL.md: frontmatter has no `name` - error
description-missingSKILL.md: no `description` — the skill can never trigger - warning
body-longSKILL.md body ≈ 5960 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 58/100
- 0When it triggers. No condition that starts the skill
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 18 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 5960 tokens
- 100Steps. 19 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- low 22 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)
- +3Description length 0: 120–800 characters recommended
- +2Single-language instructions
- +4Structure: 72 headings
- +3Step-by-step instructions: 19 items
- +3Output format is stated explicitly
- +4Has examples (51 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 0.