SKILLEMALL.ai

AC crypto-trading

The pre-trade data stack for crypto trading and sniping agents: discover new/trending token launches, run a safety/rug-check on any token BEFORE buying, and pull live DEX price, liquidity and buy/sell flow. All live, all pay-per-call over x402. USE FOR: - Finding newly launched / boosted tokens to evaluate (discovery feed to poll) - Rug-checking a token before buying: honeypot, buy/sell tax, mint/owner risk, blacklist - Live DEX data for any token: USD price, 24h volume, liquidity, buy/sell counts, momentum TRIGGERS: - "new tokens", "new launches", "trending tokens", "what just launched", "snipe" - "is this token safe", "rug check", "honeypot", "can I sell", "token tax", "is it a scam" - "token price", "dex data", "liquidity", "volume", "buy sell pressure" Recommended flow per candidate: launches -> security (rug check) -> dex (data) = decide. Use x402 GET calls. Never guess paths — use the exact URLs below or GET /samples first.

ClawHub Agent Skills author: rccola990-cloud v1.0.0 MIT-0 2 files body ≈ 428 tokens Open the sourceclawhub.ai analyzed 2 d ago

The pre-trade data stack for crypto trading and sniping agents: discover new/trending token launches, run a safety/rug-check on any token BEFORE buying, and…

As a process C 50/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 0

    ✓ No critical or high findings

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "mcp"

    Process rating: all ten parameters 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (crypto-trading) differs from the folder (riley-crypto-trading)
    • 50Failures and branches. 0 branches, has a failure section
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 428 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)
    • +3Description length 946: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 15 example trigger phrases
    • +4Structure: 3 headings
    • +3Step-by-step instructions: 3 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    This is a disclosed crypto data skill that makes paid read-only market-data calls and does not show hidden code, trading execution, or persistence.
    LLM: benign (high) · VirusTotal: · 19 Jun 2026