SKILLEMALL.ai
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Analyze Polymarket prediction market wallets — get copy trading scores (1-10), P&L, win rate, risk metrics (Sharpe ratio, Sortino ratio, max drawdown), red flags, position sizing, market category performance, recent performance (7d/30d/90d), streak analysis, individual open positions with entry/current prices, and recent trade history. Also discover elite traders via daily leaderboard, hot bets from top traders, and random wallet discovery. Connects via MCP server or REST API. Use when evaluating whether to copy trade a Polymarket trader, comparing multiple wallets side-by-side, screening for elite prediction market performers, checking if a wallet has bot-like trading patterns or hidden losses, researching a trader's risk profile, viewing recent trade activity, finding today's best open bets, or discovering new traders to follow. Free API key, no daily limits, 6-hour result caching.

modbender/skill-library-mcp Claude Code author: modbender MIT 2 files body ≈ 3 885 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Analyze Polymarket prediction market wallets — get copy trading scores (1-10), P&L, win rate, risk metrics (Sharpe ratio, Sortino ratio, max drawdown), red…

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

AnalyzerTelegramAI 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
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
46/100
Unfinished process
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 "homepage"

    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
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 8 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 85Steps. 19 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3885 tokens
    • low 10 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 896: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.