Explore in MochiBot. Install in chat.Hosted workspaces and MochiBot lightweight setups include the Skills assistant. Choose a Skill and ask for installation in chat. Other OpenClaw setups can follow the first-install guide.
How to use Skills →
MOCHILAB / AI SKILL
Fund Style Drift and Disclosure Lag
Compare NAV-based disclosed category exposures while retaining unknown weights and report/publication lag.
What it does
Compare NAV-based disclosed category exposures while retaining unknown weights and report/publication lag.
- NAV-based disclosure weights
- Unknown undisclosed categories
- Report versus publication lag
Try saying
Use my supplied records to compare NAV-based disclosed category exposures while retaining unknown weights and report/publication lag.
Export a reproducible report with the assumptions, key metrics and detailed CSV. Ask me for missing facts first.
Calculation scope
Exactly two snapshots, same reviewed category schema, weights on total NAV basis and total<=1. Missing categories are unknown, not zero; this is essential for top-ten-only holdings. Publication cannot exceed as_of. Report age differs from publication age; neither is a live position.
Deliverables and runtime
Includes executable Python 3.10+ standard-library calculations, independent numerical tests, fictional examples and an input reference. Supported flat collections accept CSV. No extra key is needed for supplied data. Live data needs an available permitted source; no all-market real-time feed is bundled. Fictional rule profiles are not current legal/broker defaults. Files stay in the active assistant workspace, with input and output hashes. Analysis only; no account access or automatic trading.
References