SKILLEMALL.ai
GitHub ↗ RU Sign in

AC agentcash-wallet

Manage your agentcash wallet and call any x402-protected API with automatic payment. No API keys, no subscriptions — just a funded wallet (USDC on Base). USE FOR: - Checking wallet balance before API calls - Redeeming invite codes for free credits - Getting deposit address for USDC - Discovering endpoints and pricing on any x402-protected origin - Making paid API requests via the agentcash CLI - Troubleshooting payment failures TRIGGERS: - "balance", "wallet", "funds", "credits" - "redeem", "invite code", "promo code" - "deposit", "add funds", "top up" - "discover", "endpoints", "what APIs", "pricing" - "insufficient balance", "payment failed"

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 088 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Manage your agentcash wallet and call any x402-protected API with automatic payment.

As a process C 57/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

IntegrationSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 57/100

    • 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. 4 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1088 tokens
    • low The response is described with custom markup (7 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 15 example trigger phrases
    • +3Description length 653: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 9 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.