ai-trading-skill-e3b0c4 — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited ai-trading-skill-e3b0c4 (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
This skill equips any agent with the ability to run market analysis, generate explainable trade rationales, backtest strategies, evaluate risk, and manage execution on the Hyperliquid exchange.
Before running any script, activate the workspace environment:
cd <project-root>
source .venv/bin/activateUser must configure private keys in .env for active trades. See .env.example for required fields.
The primary output of this framework. Generates a structured trade rationale covering trend, volatility, momentum, key levels, sizing, and invalidity conditions.
# Default analysis (BTC, moderate risk profile)
python3 analyze.py
# Custom asset, interval, and risk profile
python3 analyze.py --symbol ETH --interval 5m --risk-profile conservative
# Adjust per-trade risk percentage
python3 analyze.py --symbol SOL --account-risk-pct 0.5Available risk profiles: conservative, moderate, aggressive
The output includes four sections:
Verify strategy calculations, rationale engine, and risk layer:
python3 -m unittest tests/test_strategies.pyWalk-forward simulation with no lookahead bias:
python3 backtest.py
python3 backtest.py --days 60 --confidence 75 --min-agree 4Calculate compounding returns and drawdowns from backtest results:
python3 pnl_calc.pyInspect per-trade strategy agreement matrix:
python3 show_signals.pyFull execution loop with safety gates, risk circuit breakers, and LLM audit:
# One-shot dry tick
python3 main.py
# Continuous polling loop
python3 main.py --loopEdit config.yaml to adjust:
agree_threshold: Minimum confidence for a strategy to count as agreeing (default: 50)min_agree: Minimum strategies required for consensus (default: 4)symbol: Asset to analyze (default: BTC)interval: Candle timeframe (default: 15m)strategies section~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.