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AB cmc-api-crypto

API reference for CoinMarketCap cryptocurrency endpoints including quotes, listings, OHLCV, trending, and categories. Use this skill whenever the user mentions CMC API, asks how to get crypto data programmatically, wants to build price integrations, or needs REST endpoint documentation. This is the go-to reference for any CMC cryptocurrency API question. Trigger: "CMC API", "coinmarketcap api", "crypto price API", "get bitcoin price via API", "/cmc-api-crypto"

modbender/skill-library-mcp Agent Skills author: modbender MIT 11 files body ≈ 1 408 tokens Open the sourcegithub.com↗ analyzed 2 d ago

API reference for CoinMarketCap cryptocurrency endpoints including quotes, listings, OHLCV, trending, and categories.

As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, progress reporting

IntegrationSoftware developmentWriting and documentstype 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
95/100
safety, quality, tests
Safety 60%
95
Quality 40%
94
Run on models
none yet
Process rating
B
73/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Failures and branches w 10
50
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.

Broad scope 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 skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash Read

    Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "source"

    Process rating: all ten parameters 73/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 16 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1408 tokens
    • 100Running it twice. No mutating operations

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 464: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 16 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (10 of 10)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.