BC warden-messari-agent
Communicate with the Messari Deep Research agent by Warden Protocol. Covers A2A protocol discovery, JSON-RPC 2.0 task messaging, x402 USDC micropayments on Base and Solana, and ERC-8004 on-chain identity verification. No API key needed to query the agent; payment is handled per-request via x402.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 5
✓ No critical or high findings
Medium and low: 5
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:198High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"asset": "0x83…913",
quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:255High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Base mainnet | `eip155:8453` | `0x83…913` | `https://facilitator.payai.network` |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:256High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Base Sepolia (testnet) | `eip1…532` | `0x03…F7e` | `https://x402.org/facilitator` |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:257High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; documentation table row)| Solana mainnet | `sola…vdp` | `EPjF…t1v` | `https://facilitator.payai.network` |
detectortable -
low Secrets in code
secret-high-entropy-tokenSKILL.md:297High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Base Sepolia | 853 | `0x80…D9e` |
table
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 53/100
- 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. 7 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 29 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2995 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 296: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 29 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.