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NFT operations on BNB Chain — get NFT metadata, check ownership, list NFTs by owner, transfer ERC-721 tokens, get collection info. Use for any NFT-related tasks on BSC.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 545 tokens Open the sourcegithub.com↗ analyzed 3 d ago

NFT operations on BNB Chain — get NFT metadata, check ownership, list NFTs by owner, transfer ERC-721 tokens, get collection info.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmentCommercetype 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
91/100
safety, quality, tests
Safety 60%
96
Quality 40%
84
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • low Secrets in code secret-high-entropy-token package-lock.json:36
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…jgR+Mkmf+D189…ihQ==",
      detector
    • low Secrets in code secret-high-entropy-token SKILL.md:98
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Pancake Squad | `0x0a…EBA` |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:99
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Pancake Bunnies | `0xDf…a07` |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:100
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | BakerySwap | `0x5d…e6D` |
      table

    Files scanned: 4. 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 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 4 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 545 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 168: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 4 items
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.