SKILLEMALL.ai
RU Sign in

AB crypto-research

Performs comprehensive due diligence on a cryptocurrency using CoinMarketCap MCP data. Use when users ask about a specific coin beyond just its price. This includes questions like "what is [coin]", "is [coin] legit", "analyze [coin]", tokenomics questions, holder distribution, or any request for deep information about a single cryptocurrency. Trigger: "research [coin]", "tell me about [coin]", "should I invest in [coin]", "DYOR [coin]", "is [coin] safe", "/crypto-research"

ClawHub Agent Skills author: CoinMarketCap | Skills v1.0.0 MIT-0 2 files body ≈ 1 396 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, when it triggers, running it twice

AnalyzerAI and agentsInfrastructuretype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
69/100
Nearly there
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
This is a copy of a skill from another catalog; the rating counts the canonical one: crypto-research (ClawHub)

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 · 0

    ✓ No critical or high findings

    Files scanned: 2. 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 69/100

    • 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. 1 mutating operations with no state check
    • 85Steps. 48 steps, 1 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1396 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +3Description length 477: enough signal without eating the budget
    • +4Structure: 19 headings
    • +3Step-by-step instructions: 48 items
    • +4Has examples (2 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This Cesto skill is a disclosed market-data and basket-management helper with user confirmation before public posting, though users should treat its investment outputs cautiously.
    LLM: benign (medium) · VirusTotal: · 9 Jul 2026