AB surf
Your AI agent's crypto brain. One skill, 83+ commands across 14 data domains — real-time prices, wallets, social intelligence, DeFi, on-chain SQL, prediction markets, and more. Natural language in, structured data out. Install once, access everything. Use whenever the user needs crypto data, asks about prices/wallets/tokens/DeFi, wants to investigate on-chain activity, or is building something that consumes crypto data — even if they don't say "surf" explicitly.
As a process B 72/100 · Nearly there — weak spots: inputs and preconditions, consistency, running it twice
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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 72/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 8 mutating operations with no state check
- 40Consistency. Frontmatter name (surf) differs from the folder (surf-ai-crypto)
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 4541 tokens
- 85Steps. 43 steps, 1 vague phrases
- 100Tools and files. Tools declared in frontmatter
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 10 branches, has a failure section
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (8 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)
- +1No license
- +2Single-language instructions
- +3Description length 466: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 43 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.