Agent skill · data analytics · daymade
bigdata-skill
Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/* REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series, annotated chunk search, screener. Use when the Bigdata MCP's tearsheets/search feel thin and you need the machine-readable substrate: mentions of Bigdata.com, RavenPack, a bd_v2_ key, rp_entity_id, or chunk/query_unit cost.
Why this skill is useful
Adds a toolkit for accessing structured financial data via Bigdata.com REST endpoints, including reusable data-pull scripts and cost-guarding features.
What it needs
Requires bigdata-client installed locally. Requires bigdata account access. About 8k tokens when loaded. Last updated 2026-08-07. 1,320 stars on the source repository.
What this skill does
Bigdata.com SDK + REST Toolkit Get the structured substrate the Bigdata.com MCP server doesn't hand over. The MCP returns clean prose and pre-synthesized tearsheets, but its search tool gives chunks with no per-chunk sentiment or entity spans, and its tearsheets give aggregate values — not the fiscal-period time series, universe screener, or per-field JSON you'd build a pipeline on. The official bigdata-client SDK plus a thin REST passthrough over the same backend, same JWT reach the official /v1/ endpoints that hold it. This skill bundles a toolkit that does exactly that — already debugged, already cost-guarded — so you don't re-pay the discovery cost. The core problem this solves (read this first) The Bigdata MCP server answers "what's the sentiment around NVIDIA?" with a readable paragraph or a pre-synthesized tearsheet — genuinely useful for a chat turn. But the moment you need the machine-readable substrate to build a pipeline on, the MCP doesn't hand it over: its search tool returns chunks with text + relevance only — no per-chunk sentiment number, no entity character spans; its tearsheets give aggregate values (a single sentiment score, a summary of estimates) — not a fiscal-period time series you can compute on, a universe screener, or per-field JSON. The fix is a general pattern, not a Bigdata trick: When an MCP data source returns only synthesized output but you need the structured fields underneath, drop to the vendor SDK or REST. MCP optimizes for a chat turn, not a pipeline. Crucially, for Bigdata these structured fields are official, publicly documented REST endpoints (docs.bigdata.com/api-reference/...), not a hidden backend — and Bigdata is sunsetting the SDK (EOL 2026-12-31) in favour of this REST API, so the REST layer here is the forward-compatible path, not a hack. The SDK (bigdataclient.Bigdata) covers search + knowledge-graph; bd.api.http reaches every /v1/ endpoint the SDK never wrapped. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills daymade/bigdata-skill