AD openbytes
End-to-end OpenBytes network API workflows for AI agents. Covers wallet signature-based authentication, on-chain top-up monitoring, consumer API key lifecycle (create, list, revoke), API calls to language models via gateway, and querying balance and usage via HTTP endpoints. Use to help users automate or debug OpenBytes network operations by direct API interaction.
As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:170High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)- USDC address: `0x10…bEB`
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:171High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)- ConsumerDeposit contract: `0xB0…dCc`
quoted
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 47/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 8 mutating operations with no state check
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 85Steps. 46 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2083 tokens
- 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 (4 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 367: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 46 items
- +4Has examples (17 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.