This extracts position sizing and stop-loss patterns from 13,385 historical trades and surfaces them as actionable rules with success rates and confidence scores. You get specific guidance like "cap frequency at 10 trades per 24h in bullish markets" (90% success rate) or "halt all SHORT entries when all 5 tracked assets are up 5%+ over 24h" (95% success rate). The data is opinionated and uneven. Some rules have high confidence from hundreds of samples, others are tagged experimental with under 50 observations. Worth scanning before you size a position, but you'll need to filter the noise yourself based on the confidence thresholds and sample counts.
3what-if-oraclek-dense-ai/scientific-agent-skillsRun structured What-If scenario analysis with 4–6 branch possibility exploration (best, likely, worst, wild card, contrarian, second-order). Use when the user a2.1Kskills.sh installs
4datapackagecatalyst-cooperative/agent-skillsExplore and query any dataset annotated with a Frictionless Data Package descriptor (datapackage.json). Use this skill whenever a user wants to discover what ta2.1Kskills.sh installs
5bioservicesk-dense-ai/scientific-agent-skillsUnified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consis2.1Kskills.sh installs
6query-onchain-datacoinbase/agentic-wallet-skillsQuery onchain data on Base using the CDP SQL API via x402. Use when you or your user want to view onchain information about decoded blocks, transactions, and ev2.1Kskills.sh installs