BC vercel-deployment-watchdog
Monitor Vercel-hosted site deployments with automated health checks, cache freshness verification, and API validation. Use when you need to ensure deployments succeed and sites remain healthy after updates. Integrates with Vercel API to detect new deployments and run comprehensive post-deployment checks.
As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers
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
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
medium Exfiltration
net-credential-usescripts/vercel-monitor.sh:152Credential used in a network call (verify the destination is the intended service)curl -s -H "Authorization: Bearer $VERCEL_TOKEN" "$url"
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medium Exfiltration
net-credential-usescripts/watchdog-check.sh:273Credential used in a network call (verify the destination is the intended service)response=$(curl -s -H "Authorization: Bearer $token" "${api_url}?${query_params}" 2>/dev/null || true)
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 61/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 70 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2328 tokens
- 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 10 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
- +1No license
- +2Single-language instructions
- +3Description length 305: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 70 items
- +4Has examples (11 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.