# bigdata-skill

Extracts structured Bigdata.com (RavenPack) financial and news data via SDK and REST API. Built for data pipelines when MCP tear sheets lack machine-readable fields.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 473 installs · ★ 1493
- Author: daymade
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/daymade/claude-code-skills/tree/HEAD/daymade-financial/bigdata-skill/SKILL.md
- Updated: 2026-10-25
- HTML: https://affpapa.org/en/nevibecode/skills/bigdata-skill

## What it does

The standard Bigdata MCP server returns synthesized paragraphs and aggregate tearsheets, but omits per-chunk sentiment numbers and entity spans. This skill bypasses that restriction using a bundled `bigdata_toolkit`. It brings together the official `bigdata-client` SDK and direct calls to `/v1/*` endpoints under one `BigdataClient`.

The toolkit includes an `EntityResolver` to turn tickers or ISINs into `rp_entity_id`, a `StructuredDataREST` helper for analyst estimates, and an `AnnotatedSearcher` for cost-guarded news chunks. It also tracks spend via `CostTracker`. Entity resolution and quota checks remain free, while searches consume billed query units.

Running this requires Python 3.12 with `bigdata-client` installed, a `BIGDATA_API_KEY` starting with `bd_v2_`, and the `scripts/` path on `PYTHONPATH`. To prevent failures, all network requests should use the included `rc` retry wrapper because SSL unexpected EOF errors frequently drop the initial handshake.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add daymade/claude-code-skills --skill bigdata-skill -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/bigdata-skill/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/bigdata-skill/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add daymade/claude-code-skills --skill bigdata-skill -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/bigdata-skill/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add daymade/claude-code-skills --skill bigdata-skill -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/bigdata-skill/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install daymade/claude-code-skills bigdata-skill
```

2. **Manually: put the files in place**
- File: `.github/skills/bigdata-skill/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add daymade/claude-code-skills --skill bigdata-skill -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/daymade/claude-code-skills --path daymade-financial/bigdata-skill
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/bigdata-skill/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add daymade/claude-code-skills --skill bigdata-skill -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/daymade/claude-code-skills/tree/HEAD/daymade-financial/bigdata-skill/SKILL.md

## When to use it
- retrieve numeric sentiment scores and entity character spans from news chunks
- pull forward analyst estimates, earnings surprises, and corporate event calendars
- resolve company names, tickers, or ISIN codes to an `rp_entity_id`
- monitor query unit and chunk consumption through `CostTracker`

## Example prompts
- Search Bigdata for NVIDIA news chunks with sentiment scores and entity character spans
- Pull forward consensus estimates and earnings surprise history for rp_entity_id E09E2B
- Check query unit consumption for the last search batch using BigdataClient

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 95a3fda325cc49af.

BEGIN THIRD-PARTY FILE 95a3fda325cc49af
```text
---
name: bigdata-skill
disable-model-invocation: true
description: >-
  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.
---

# 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 (`bigdata_client.Bigdata`) covers search + knowledge-graph; **`bd._api.http`**
reaches every `/v1/*` endpoint the SDK never wrapped. The bundled
`bigdata_toolkit` packages both behind one `BigdataClient`.
```
END THIRD-PARTY FILE 95a3fda325cc49af

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First 2616 of 15177 bytes. © daymade · MIT · https://github.com/daymade/claude-code-skills/tree/HEAD/daymade-financial/bigdata-skill/SKILL.md

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