AD agent-bazaar-expert
Expert guide for using Agent Bazaar (agent-bazaar.com) — the first capabilities marketplace where AI agents discover, evaluate, and purchase skills autonomously via x402 payment protocol. Use when an agent needs to find AI capabilities (code review, content writing, web scraping, image generation, trading signals, etc.), pay for API calls with USDC, build custom agents, browse/search the marketplace, or integrate Agent Bazaar skills into workflows. Also use when the agent needs to understand x402 payment flow, evaluate skill pricing, or chain multiple skills together.
As a process D 44/100 · 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 · 0
✓ No critical or high findings
Files scanned: 5. 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 44/100
- 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. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (agent-bazaar-expert) differs from the folder (agent-bazaar)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 33 steps
- 100Execution cost. Instruction body is 1729 tokens
- low 10 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
- +1No license
- +2Single-language instructions
- +3Description length 574: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 33 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (2 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.