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BC quickintel-scan

Scan any token for security risks, honeypots, and scams using Quick Intel's contract analysis API. Use when: checking if a token is safe to buy, detecting honeypots, analyzing contract ownership and permissions, finding hidden mint/blacklist functions, or evaluating token risk before trading. Triggers: 'is this token safe', 'scan token', 'check for honeypot', 'audit contract', 'rug pull check', 'token security', 'safe to buy', 'scam check'. Supports 63 chains including Base, Ethereum, Solana, Sui, Tron. Costs $0.03 USDC per scan via x402 payment protocol. Requires an x402-compatible wallet — recommended: managed wallet service (Sponge, AgentWallet) with no raw key exposure. Programmatic signing with dedicated hot wallet also supported. This skill is read-only and never accesses your tokens or assets.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 3 727 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Scan any token for security risks, honeypots, and scams using Quick Intel's contract analysis API.

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
86/100
safety, quality, tests
Safety 60%
90
Quality 40%
80
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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

    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 · 6

    ✓ No critical or high findings

    Medium and low: 6
    • medium Risky intent intent-wallet-secrets SKILL.md:111
      Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
      // NEVER use your main wallet or trading wallet private key here
    • low Secrets in code secret-high-entropy-token Reference.md:19
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Base | `eip155:8453` | USDC | `0x83…913` |
      table
    • low Secrets in code secret-high-entropy-token Reference.md:20
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Ethereum | `eip155:1` | USDC | `0xA0…B48` |
      table
    • low Secrets in code secret-high-entropy-token Reference.md:21
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Arbitrum | `eip1…161` | USDC | `0xaf…831` |
      table
    • low Secrets in code secret-high-entropy-token Reference.md:22
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Optimism | `eip155:10` | USDC | `0x0b…f85` |
      table
    • low Secrets in code secret-high-entropy-token Reference.md:23
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Polygon | `eip155:137` | USDC | `0x3c…359` |
      table

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "credentials"

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 16 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3727 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 12 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)
    • +3Description length 811: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 22 headings
    • +3Step-by-step instructions: 16 items
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.