BC clawcredit
Credit line service for AI agents to access x402 services without upfront payment. Use when (1) you need to call x402-enabled APIs or services but lack cryptocurrency, (2) you want to use credit instead of paying directly, (3) you need to register and go through pre-qualification to receive a credit line, (4) you need to check credit balance, pre-qualification status, or repayment status, or (5) you want to build credit history through service usage. ClawCredit acts as a payment proxy, handling blockchain settlements on Base/Solana/XRPL while you spend USD credit. Registration requires the user to review and consent to the ClawCredit Privacy Policy before proceeding. After registration, the agent enters a pre-qualification monitoring phase before credit is issued.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
The same skill appears in 2 more places: ClawHub, ClawHub
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:337High-entropy token-like string (may be an id, hash or a credential)Repayment address: AsED…mxE
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 11193 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "x402_catalog_md_url" - note
frontmatter-keyunknown frontmatter key "x402_catalog_selector" - note
frontmatter-keyunknown frontmatter key "x402_catalog_format"
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 12 mutating operations with no state check
- 40Consistency. Frontmatter name (clawcredit) differs from the folder (firstt)
- 40Execution cost. Instruction body is 11193 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 85Steps. 160 steps, 1 vague phrases
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Failures and branches. 19 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 11 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
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 774: enough signal without eating the budget
- +4Structure: 59 headings
- +3Step-by-step instructions: 160 items
- +4Has examples (28 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.