Browse reusable Agent Skills, each with a clear purpose and practical guidance.
The human needs something that exists somewhere in the world but they don't know where. A decision, a person, a file, a reference — it's been captured, they just can't find it. Searches decisions, people, files, references, insights, and log history across all walnuts in priority order.
Reference for Bevy's picking system — pointer events, backends, hover/click/drag observers, sprite picking, UI picking, and mesh picking.
Use when you need to design or audit an identification strategy for an observational study.
Use when you need to scaffold, run, or publish computational research experiments.
Crypto Twitter intelligence and alpha research. Search X/Twitter for real-time crypto narratives, trending tokens, yield strategies, smart money signals, and protocol research. Features TweetRank (PageRank-inspired credibility scoring), multi-signal token detection, coordinated raid detection, and dynamic tool discovery for execution suggestions. Solana-first but covers all major chains.
Solve Dataset_Metadata_and_Business_Rules questions in the dabstep payment processing dataset. Use this skill for questions about dataset column meanings, fee factor directionality, business rules encoded in fee structures, "Not Applicable" detection, and metadata interpretation for the dabstep payment processing domain. Trigger whenever questions reference payment fees, fraud indicators, boolean fee factors, volume tiers, ACI codes, or any field from the dabstep dataset.
Answer questions about the dabstep payment processing dataset's metadata, schema, column definitions, and business rules governing fees. Use this skill whenever questions ask about column names/meanings, fee rule factors and their directions (e.g., "which boolean factor makes fees cheaper when True"), business rule definitions (e.g., capture delay, fraud level, volume tiers), whether a concept exists in the dataset (e.g., "is there an excessive retry fee?"), or how fee parameters interact (intracountry, is_credit, monthly_fraud_level, monthly_volume, capture_delay). Also applies to questions about ACI codes, account types, MCC codes, and any metadata from payments-readme.md or manual.md.
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones, generating QA pairs about behavioral/psychological changes over time, working with EMA, depression scores, activity, sleep, communication, location, or phone-usage data, or any user-profile analysis in the DDR/GLOBEM context.
Solve fraud analysis and general macro transaction analysis questions on the dabstep payment dataset. Use this skill for questions about fraud rates, transaction distributions, merchant rankings, card scheme analysis, country breakdowns, correlation studies, and yes/no comparative questions on payment data.