AC hyperliquid-prime
Trade on Hyperliquid's perp markets (native + HIP-3) with intelligent order routing and cross-market splitting. Use when the user wants to trade crypto, stocks, or commodities on Hyperliquid, get best execution across fragmented markets, split large orders across multiple venues, compare funding rates, view aggregated orderbooks, or manage positions across multiple collateral types. Routes across both native HL perps (ETH, BTC) and HIP-3 deployer markets. Handles collateral swaps (USDC→USDH/USDT0) automatically during execution when the best liquidity requires it.
As a process C 55/100 · Has gaps — weak spots: result and completion, 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 purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.
Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.
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
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medium Risky intent
intent-wallet-secretsskill-card.md:14Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer targetDevelopers and trading operators use this skill to ask an agent for Hyperliquid market discovery, order routing quotes, funding and orderbook comparisons, position views, and explicit trade-execution
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low Secrets in code
secret-high-entropy-tokenopenclaw.plugin.json:39High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"recipient": "0x34…c37",
quoted
Files scanned: 3. 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 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (web, node) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 100Steps. 41 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2176 tokens
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 570: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.