BB arb-finder
Detect guaranteed-profit arbitrage opportunities across sportsbooks and prediction markets. Compares odds from 20+ bookmakers, Polymarket, and Kalshi. Calculates optimal stake distribution. Use when asked about arbs, arbitrage, guaranteed profit, or cross-market pricing.
As a process B 73/100 · Nearly there — weak spots: inputs and preconditions
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
exfil-secret-in-urlSKILL.md:37Credential passed in a URL query string (normal for some APIs — verify the host is the intended service) (quoted — discussed, not commanded)curl -s "https://api.the-odds-api.com/v4/sports/SPORT_KEY/odds?apiKey=…®ions=us&markets=h2h&oddsFormat=…" \
quoted -
medium Exfiltration
net-credential-useSKILL.md:37Credential used in a network call (verify the destination is the intended service)curl -s "https://api.the-odds-api.com/v4/sports/SPORT_KEY/odds?apiKey=…®ions=us&markets=h2h&oddsFormat=…" \
Files scanned: 2. 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 73/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 100Steps. 18 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1851 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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)
- +1No license
- +2Single-language instructions
- +3Description length 271: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 18 items
- +3Output format is stated explicitly
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.