---
name: diffbot-deals
description: "Search funding rounds, investments, and acquisitions in the Diffbot Knowledge Graph by date, deal size, round series, investor, acquirer, or industry — returns deals as rows: target, counterparty, amount, currency, date. MUST USE skill when the answer is a list of deals: deal flow, funding history for a company, or an investor's recent activity. Rows are deals, not companies. Triggers on: funding rounds, raised, Series A, venture funding, funding history, deal flow, deal size, investments in, acquisitions, M&A, acquired for, valuation round."
---

# Diffbot Deal Search

Find funding rounds, acquisitions, and transactions in the Diffbot Knowledge Graph via
`diffbot_dql`. Deals live in two places — as standalone `Investment`/`Transaction`
records, and as fields hanging off the `Organization` involved. **Choosing the right one
is the whole skill**; see the routing table below.

**First, check the row shape.** This skill is for when the rows are deals — target,
acquirer/investors, amount, date, series. If the rows the user wants are *companies*
("everything Microsoft acquired", "companies Sequoia backed"), hand off to
`/skill:diffbot-organizations`; it filters on the same `acquiredBy` and `investments`
fields but presents companies.

Sibling skills: `/skill:diffbot-organizations` (companies as rows), `/skill:diffbot-news`,
`/skill:diffbot-places`. Use `/skill:diffbot-dql` for anything outside these shapes.

Auth is handled by the extension; check with `/diffbot` if needed. Never echo the token.

## Step 1 — route the question

| The user asks about | Query | Why |
| --- | --- | --- |
| Funding rounds by date, size, series, or investor | `type:Investment` | One row per round, with amount, series, date, investors |
| Deals in an **industry / location / company-size** segment | `type:Organization` + `investments.{...}` | Industry lives on the company, not the deal — see the gap below |
| Acquisitions ("who bought X", "everything Microsoft acquired") | `type:Organization isAcquired:true acquiredBy.name:"…"` | `Acquisition` records are name-only stubs |
| Any money movement, not just equity | `type:Transaction` | Superset: `Investment` and `Acquisition` are both subtypes |

### The industry gap — read this before filtering deals by sector

`investee` on an `Investment` is a `LinkedEntity`, which carries only `name`, `types`,
`summary`, `image`, `diffbotUri`. It has **no `categories`**, so
`investee.categories.name:"…"` returns zero — it looks like "no such deals" rather than
"no such field."

Flip the query to the company side instead, and use `{}` so the round's conditions
co-constrain:

```
diffbot_dql({ query: 'type:Organization categories.name:"Artificial Intelligence Software" investments.{amount.value>50000000 date>="2026-01-01"}' })
diffbot_dql({ query: 'type:Organization categories.name:"Artificial Intelligence Software" investments.{series:"Series A" date>="2026-01-01"}' })
```

Without `{}` the clauses are independent — a company with *any* $50M round and *any* 2026
round matches, even if they're different rounds.

## Step 2 — field reference

**`type:Investment`** — a funding round.

| Field | Type | Notes |
| --- | --- | --- |
| `investee` | LinkedEntity (Organization) | `investee.name` — the company raising |
| `investment.series` | String | `"Series A"`, `"Series B"`, `"Series Unknown"`, … |
| `investment.amount.value` | Float | Raw number; pair with `.currency` |
| `investment.amount.currency` | String | `"USD"`, … — **always filter or display currency**, values are not normalized |
| `investment.date` | DDate | `investment.date>="2026-01-01"` |
| `investment.investors` | list of LinkedEntity | `investment.investors.name` |
| `date`, `name`, `amount` | | Inherited from `Transaction`; `date` mirrors `investment.date` |

**`type:Transaction`** — any transaction; `Investment` and `Acquisition` are subtypes
(filter with `types:"Acquisition"`).

| Field | Notes |
| --- | --- |
| `payee` | LinkedEntity (Organization) — receiving side |
| `payers` | list of LinkedEntity — paying side |
| `amount.value` / `amount.currency` | |
| `date` | DDate |
| `name` | e.g. `"Venture Round - OpenAI"` |

**`Organization.acquiredBy`** — the reliable path for M&A.

| Field | Notes |
| --- | --- |
| `acquiredBy.name` | The acquirer |
| `acquiredBy.amount.value` | Deal size |
| `acquiredBy.date` | |
| `isAcquired` | Boolean flag on the target |

Also on Organization: `investments` (full round history), `totalInvestment.value`,
`nbUniqueInvestors`.

**Sorting.** There *is* a useful default: unsorted,
`investment.investors.name:"Sequoia Capital"` returns the headline rounds (OpenAI $122B,
Anthropic $50B, OpenAI $40B), whereas `revSortBy:investment.date` returns whatever closed
most recently regardless of size. Leave the sort off unless the ordering is the question,
then use:

```
revSortBy:investment.date         # newest rounds first
revSortBy:investment.amount.value # largest rounds first
```

### Worked examples

```
type:Investment investment.investors.name:"Sequoia Capital" revSortBy:investment.date
type:Investment investment.series:"Series A" investment.date>="2026-01-01" revSortBy:investment.amount.value
type:Investment investment.amount.value>100000000 investment.amount.currency:"USD" revSortBy:investment.date
type:Investment investee.name:"OpenAI" revSortBy:investment.date
type:Organization isAcquired:true acquiredBy.name:"Microsoft" revSortBy:acquiredBy.amount.value
type:Organization categories.name:"Semiconductor Companies" investments.{amount.value>100000000 date>="2025-01-01"}
type:Transaction types:"Acquisition" sortBy:date
```

**Note on `type:Acquisition`.** These records exist (~209k) but are near-empty stubs —
typically just a `name` like `"Reliance Motor Car Company acquired by General Motors"`,
with `date`, `amount`, `payee`, and `payers` all null. Don't build a deal table from
them; route acquisitions through `Organization.acquiredBy`.

### Distribution questions are facets, not row lists

"What stage is most funding at?", "which investors are most active?" — aggregate instead
of listing:

```
diffbot_dql({ query: 'type:Investment investment.date>="2026-01-01" facet:investment.series', size: 12 })
```

Returns buckets like `Seed 4568 · Grant 3796 · Series A 2061 · Debt Financing 2015 ·
Pre Seed 1965`. Also useful: `facet:investment.investors.name` (most active investors),
`facet:investment.amount.value` (auto-bucketed deal sizes).

Facets need `size` ≥ 1 — `size: 0` errors — and `size` sets the **number of buckets**,
not rows. A facet response has `value`/`count` per bucket, not entities, so don't use it
when the user wants individual deals.

**`or()` is usually unnecessary on `series`.** `investment.series:"Series A"` is a
contains match and already covers `"Series A-1"`; `or("Series A","Series A-1")` returns
the identical count. Reach for `or()` only across genuinely different strings.

## Step 3 — probe before committing

```
diffbot_dql_probe({
  queries: [
    'type:Investment investment.series:"Series A" investment.date>="2026-01-01"',
    'type:Investment investment.amount.value>100000000',
    'type:Organization categories.name:"Artificial Intelligence Software" investments.{amount.value>50000000 date>="2026-01-01"}',
  ],
})
```

Zero hits on an `investee.<something>` path almost always means the field doesn't exist
on `LinkedEntity` — re-read the gap above rather than loosening the filter. The probe
fails the whole batch if any one variant is rejected by the API, so fix the bad clause
and re-run.

## Step 4 — export and display

Rounds:

```
diffbot_dql({
  query: '<DQL>',
  size: 50,
  format: "csv",
  exportspec: "investee.name,Company;investment.series,Round;investment.amount.value,Amount;investment.amount.currency,Currency;investment.date.str,Date;investment.investors.name,Lead Investor",
})
```

Acquisitions:

```
diffbot_dql({
  query: '<DQL>',
  size: 50,
  format: "csv",
  exportspec: "name,Target;acquiredBy.name,Acquirer;acquiredBy.amount.value,Amount;acquiredBy.date.str,Date;categories.name,Industry",
})
```

Export JSON when the full investor list matters — `exportspec` renders only the
**primary** value of a list field, so the CSV shows one investor even when a round had
thirty. Request the investor list via `filter` and read it from the returned payload.

**Display**

1. Render a markdown table: **Date | Company | Round | Amount | Investors**.
2. **Reformat amounts.** CSV emits scientific notation (`3.0E8`) — show `$300M`. Never
   present a bare number without its currency.
3. Dates come back as `d2026-07-23` — strip the leading `d`.
4. Blank amount means the round was reported without a disclosed size — label it
   "undisclosed", not `0`.
5. `"Series Unknown"` is a real value in the data, not a lookup failure.
6. Print the final DQL in a plain code block, and offer more rows (`size: N`, `from: K`)
   or a refinement.