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BC AgentMarket Skill

Interact with the AgentMarket prediction market protocol on Base Sepolia. Create markets, trade YES/NO positions, provide liquidity, and resolve outcomes — all settled in USDC.

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 2 362 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Interact with the AgentMarket prediction market protocol on Base Sepolia.

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

IntegrationAI 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
B
80/100
safety, quality, tests
Safety 60%
89
Quality 40%
67
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Risky intent medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 7

✓ No critical or high findings

Medium and low: 7
  • medium Risky intent intent-wallet-secrets SKILL.md:37
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - `WALLET_PRIVATE_KEY`: Your wallet private key
  • low Secrets in code secret-high-entropy-token index.ts:19
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const FACTORY_ADDRESS = process.env.AGENT_MARKET_FACTORY_ADDRESS || '0xDd…598';
    quoted
  • low Secrets in code secret-high-entropy-token index.ts:20
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const USDC_ADDRESS = process.env.USDC_ADDRESS || '0x03…F7e'; // Base Sepolia USDC
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:18
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | MarketFactory | `0xDd…598` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:19
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | USDC | `0x03…F7e` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:34
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - `AGENT_MARKET_FACTORY_ADDRESS`: The deployed MarketFactory contract address (default: `0xDd…598`)
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:35
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - `USDC_ADDRESS`: USDC token address on the network (default: `0x03…F7e`)
    quoted

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 40Consistency. Frontmatter name (AgentMarket Skill) differs from the folder (agent-market)
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 42 steps
  • 100Execution cost. Instruction body is 2362 tokens

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 176: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 42 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.