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<!-- Verbatim upstream SKILL.md follows, YAML frontmatter included. -->

---
name: opendata-api
description: Query the OpenData API for data research and analysis. Use when fetching dataset rows, filtering, sorting, aggregating, inspecting columns, composing cross-dataset joins, exploring graph intelligence, or building data pipelines against OpenData endpoints.
---

# OpenData Query API

Query datasets stored as Parquet files through a REST API backed by DuckDB. The API returns JSON by default, with support for CSV, TSV, and XLSX exports.

**Base URL:** `https://api.tryopendata.ai` (production) or `http://localhost:8000` (local dev). Default to use production

## Authentication

All endpoints require authentication in production. Before making API calls, resolve a Bearer token using this sequence:

1. **Check env var**: If `OPENDATA_API_KEY` is set, use it.
2. **Check auth.json**: Read `~/.config/opendata/auth.json`. If it exists, extract the token:
   - `method: "api_key"` -> use the `api_key` field
   - `method: "clerk"` -> use the `access_token` field (check `expires_at` hasn't passed)
   - Legacy format (no `method` field, just `api_key`) -> use the `api_key` field
3. **Prompt to authenticate**: If neither source has a token:
   - Check if the `opendata` CLI is installed (`which opendata`)
   - If installed: run `opendata auth login` and let the user authenticate
   - If not installed: tell the user to install it (`brew install tryopendata/opendata/opendata` or `curl -fsSL https://raw.githubusercontent.com/tryopendata/opendata/main/scripts/install-cli.sh | bash`), then run `opendata auth login`
   - As a fallback, the user can set `OPENDATA_API_KEY` manually with a key from https://tryopendata.ai/settings/api-keys

Once resolved, pass the token via `Authorization: Bearer` header:

```bash
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.tryopendata.ai/v1/datasets/fred/cpi?limit=5"
```

If you get a 401 during a session, re-run the resolution sequence (the token may have expired).

Local dev (`localhost:8000`) does not require auth when running the standalone opendata server (`make quickstart`). The backend server (`make dev-all`) requires auth for write endpoints but allows unauthenticated reads.

## Quick Start

**For analysis (aggregations, joins, window functions), use SQL:**

```bash
# Average CPI by year, most recent first
curl -X POST "https://api.tryopendata.ai/v1/datasets/fred/cpi/query" \
  -H "Authorization: Bearer ${OPENDATA_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"sql": "SELECT EXTRACT(YEAR FROM date) as year, AVG(value) as avg_cpi FROM data GROUP BY 1 ORDER BY 1 DESC LIMIT 10"}'
```

**Parameterized queries (avoids escaping issues):**

```bash
curl -X POST "https://api.tryopendata.ai/v1/datasets/owid/gdp/query" \
  -H "Authorization: Bearer ${OPENDATA_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"sql": "SELECT * FROM data WHERE country_name = ? AND year >= ? ORDER BY year", "params": ["United States", 2020]}'
```

**For simple row fetches (no aggregation), use the REST endpoint:**

```bash
# Get the 5 most recent CPI values
curl -H "Authorization: Bearer ${OPENDATA_API_KEY}" \
  "https://api.tryopendata.ai/v1/datasets/fred/cpi?limit=5&sort=-date"
```

**Do NOT append `/query` to GET requests.** `GET /v1/datasets/fred/cpi/query` will fail with a `SUBDATASET_NOT_FOUND` error because the API interprets `query` as a subdataset name. The `POST /query` endpoint is a separate SQL interface (see [sql-query.md](references/sql-query.md)).

All data endpoints live under `/v1/datasets/`.

## MCP Tools (Preferred When Available)

If you have access to OpenData MCP tools (`search_datasets`, `query_dataset`, `query_sql`), prefer them over direct API calls. The MCP tools handle auth, pagination, and response formatting automatically. **Use `query_sql` for analytical queries** (aggregations, joins, window functions) and `query_dataset` for simple row fetches. Fall back to the REST API below only when:
- MCP tools are not connected
- You need endpoints the MCP doesn't cover (graph intelligence, composition, activity feeds)
- You need raw HTTP control (custom headers, streaming, specific formats)

## Endpoints

### Data & Schema

| Method | Path                                               | Description                                                  |
| ------ | -------------------------------------------------- | ------------------------------------------------------------ |
| GET    | `/v1/datasets/{provider}/{dataset}`                | Query dataset rows (flat) or list subdatasets (hierarchical) |
| GET    | `/v1/datasets/{provider}/{dataset}/{subdataset}`   | Query subdataset rows                                        |
| GET    | `/v1/datasets/{provider}/{dataset}/columns`        | Column metadata and statistics                               |
| GET    | `/v1/datasets/{provider}/{dataset}/columns/{name}` | Single column detail with full value list                    |
| GET    | `/v1/datasets/{provider}/{dataset}/meta`           | Dataset metadata (schema, views, graph scores, merged enrichment) |
| GET    | `/v1/datasets/{provider}/{dataset}/views`          | List available views                                         |
| POST   | `/v1/datasets/{provider}/{dataset}/query`          | Execute SQL query (authenticated)                            |
| POST   | `/v1/query`                                        | Cross-dataset SQL query (join multiple datasets)             |

### Enrichment & Intelligence

| Method | Path                                               | Description                                                  |
| ------ | -------------------------------------------------- | ------------------------------------------------------------ |
| GET    | `/v1/datasets/{provider}/{dataset}/meta/enriched`  | AI-enriched metadata (descriptions, tags, methodology, coverage) |
| GET    | `/v1/datasets/{provider}/{dataset}/meta/view-suggestions` | AI-suggested views (timeseries, lookup, wide_to_long, pivot) |
| POST   | `/v1/datasets/{provider}/{dataset}/meta/view-suggestions/{id}/apply` | Apply a view suggestion (admin) |
| GET    | `/v1/datasets/{provider}/{dataset}/chart`           | Dataset chart data with auto-downsampling                    |
| GET    | `/v1/datasets/{provider}/{dataset}/activity`       | Recent activity events (ingestion, enrichment, schema changes) |
| GET    | `/v1/datasets/{provider}/{dataset}/related`        | Related datasets (semantic + join + graph signals)           |

### Composition (Cross-Dataset Joins)

| Method | Path                                               | Description                                                  |
| ------ | -------------------------------------------------- | ------------------------------------------------------------ |
| GET    | `/v1/datasets/{provider}/{dataset}/joinable`       | List joinable datasets for composition                       |
| POST   | `/v1/datasets/{provider}/{dataset}/compose/preview`| Preview a cross-dataset join (LEFT JOIN)                     |
| GET    | `/v1/datasets/{provider}/{dataset}/compose/download.csv` | Download a composed join as CSV (auth required)        |

### Search & Discovery

| Method | Path                                               | Description                                                  |
| ------ | -------------------------------------------------- | ------------------------------------------------------------ |
| GET    | `/v1/search`                                       | Search datasets (keyword/semantic/hybrid, graph-boosted)     |
| GET    | `/v1/search/suggest`                               | Autocomplete suggestions for search typeahead                |
| GET    | `/v1/discover`                                     | Search datasets with enriched metadata for LLM agents        |
| POST   | `/v1/discover/batch`                               | Batch discover across multiple queries with deduplication    |
| GET    | `/v1/categories/{slug}`                            | Browse datasets by category (supports graph sorting)         |

### Graph Intelligence

| Method | Path                                               | Description                                                  |
| ------ | -------------------------------------------------- | ------------------------------------------------------------ |
| GET    | `/v1/graph/datasets/{provider}/{dataset}/stats`    | Graph statistics for a dataset (importance, bridge, community) |
| GET    | `/v1/graph/datasets/{provider}/{dataset}/join-paths` | Multi-hop join paths from a dataset                        |
| GET    | `/v1/graph/datasets/{provider}/{dataset}/related`  | Graph-powered related datasets (structural + semantic)       |
| GET    | `/v1/graph/datasets/{provider}/{dataset}/neighbors`| Direct 1-hop connections (filterable by edge type)           |
| GET    | `/v1/graph/datasets/{provider}/{dataset}/schema-graph` | Schema-level subgraph for D3 visualization               |
| GET    | `/v1/graph/communities`                            | List communities with top datasets and dominant topics       |
| GET    | `/v1/graph/communities/{community_id}/datasets`    | List datasets in a community by importance                   |
| GET    | `/v1/graph/bridges`                                | Top bridge datasets by betweenness centrality                |
| GET    | `/v1/graph/subgraph`                               | Seeded subgraph for graph explorer                           |
| GET    | `/v1/graph/entities/{type}/{id}/datasets`          | Datasets referencing a specific entity                       |
| GET    | `/v1/graph/health`                                 | Graph health and sync status                                 |

## Subdatasets

Some datasets contain multiple tables (e.g., multi-sheet Excel workbooks, BLS series groups). For these:

- `GET /v1/datasets/{provider}/{dataset}` returns data for the default subdataset, or lists available subdatasets
- `GET /v1/datasets/{provider}/{dataset}/{subdataset}` queries a specific subdataset

If you get a `SUBDATASET_NOT_FOUND` error, the dataset likely has subdatasets. Check the error response's `suggestions` field - it includes a link to browse available subdatasets. Any unrecognized path segment after the dataset slug is interpreted as a subdataset name, which is why paths like `/query` or `/search` appended to a dataset path produce this error.

## Query Parameters

| Parameter         | Example                  | Description                                | Reference                                                   |
| ----------------- | ------------------------ | ------------------------------------------ | ----------------------------------------------------------- |
| `filter[col]`     | `filter[year]=2024`      | Filter rows by column value                | [filtering.md](references/filtering.md)                     |
| `filter[col][op]` | `filter[year][gte]=2020` | Filter with operator                       | [filtering.md](references/filtering.md)                     |
| `sort`            | `sort=-year`             | Sort by column (prefix `-` for desc)       | [pagination-and-sort.md](references/pagination-and-sort.md) |
| `limit`           | `limit=50`               | Max rows to return (1-1000, default 100)   | [pagination-and-sort.md](references/pagination-and-sort.md) |
| `offset`          | `offset=100`             | Skip N rows                                | [pagination-and-sort.md](references/pagination-and-sort.md) |
| `cursor`          | `cursor=...`             | Keyset pagination token                    | [pagination-and-sort.md](references/pagination-and-sort.md) |
| `fields`          | `fields=year,score`      | Column projection                          | [output-formats.md](references/output-formats.md)           |
| `format`          | `format=csv`             | Output format (json, csv, tsv, xlsx)       | [output-formats.md](references/output-formats.md)           |
| `aggregate`       | `aggregate=avg(score)`   | Aggregate functions                        | [aggregation.md](references/aggregation.md)                 |
| `group_by`        | `group_by=year`          | Group rows by column                       | [aggregation.md](references/aggregation.md)                 |
| `view`            | `view=enriched`          | Apply a named view (for SQL, prefer colon syntax: `FROM "bls/cpi-u:enriched"`) | [sql-query.md](references/sql-query.md) |
| `expand`          | `expand=area`            | Expand joined dimensions inline            |                                                             |
| `include_sources` | `include_sources=true`   | Show `_source_url`, `_source_page` columns |                                                             |
| `response_format` | `response_format=columnar` | Response shape: `objects` (default) or `columnar` (compact) | [output-formats.md](references/output-formats.md)           |
| `include_graph`   | `include_graph=true`     | Attach graph scores to `/meta` response    | [graph.md](references/graph.md)                             |
| `debug`           | `debug=true`             | Include generated SQL and query echo       |                                                             |

## Common Pitfalls

**Use `filter[col]=val`, not `?col=val`.** Bare column names as query params are silently ignored. The API returns a structured warning, but you still get unfiltered data back.

```bash
# Wrong - returns ALL rows, with a warning
curl '.../nces/naep?year=2024'

# Right
curl '.../nces/naep?filter[year]=2024'
```

**URL-encode brackets in curl.** Some shells interpret `[` and `]`. Use `%5B` / `%5D` or quote the URL.

```bash
curl 'https://api.tryopendata.ai/v1/datasets/nces/naep?filter%5Byear%5D=2024'
```

**Check `warnings` in the response.** Unknown parameters produce structured `QueryWarning` objects with `code`, `message`, and `param`. The `X-OpenData-Warnings` HTTP header also carries these for piped workflows.

**Use `?debug=true` to see generated SQL.** Returns a `debug` object with `debug.query` (echo of your parameters) and `debug.sql` (the DuckDB SQL that ran). Useful for verifying filters and sorts are applied correctly.

**`aggregate` and `nest_fields` are mutually exclusive.** You get a 400 error if you combine them. Aggregation produces flat summary rows; nesting produces grouped hierarchical data.

**If a SQL query returns an error**, check the error response body for details. Common causes: invalid column names (verify with `GET .../columns`), syntax issues, or timeout on very large datasets. For simple aggregations that don't need SQL features (window functions, CTEs, joins), the REST `aggregate` + `group_by` params are an alternative.

**Sorting on computed aggregation columns works.** When using `aggregate` + `group_by`, you can sort on the computed column names (e.g., `sort=-count_event_id` for `aggregate=count(event_id)`). Invalid sort fields return a 400 with `valid_values` showing available options.

**Always use `api.tryopendata.ai` for POST endpoints.** The frontend at `tryopendata.ai/api/` proxies GET requests only. POST requests to `tryopendata.ai/api/v1/query` return 405. Use `api.tryopendata.ai/v1/query` directly for SQL and cross-dataset queries.

**Set a User-Agent header in API requests.** Some CDN/WAF configurations may block requests with missing or generic user agents. Include a descriptive identifier:

```bash
curl -H "User-Agent: claude-code/opendata-skill" \
  -H "Authorization: Bearer ${OPENDATA_API_KEY}" \
  "https://api.tryopendata.ai/v1/datasets/fred/cpi?limit=5"
```

## SQL Query

The `POST /v1/datasets/{provider}/{dataset}/query` endpoint accepts raw SQL and executes it against the dataset. Requires authentication (API key or session). The dataset table is available as `data` or `"provider/dataset"`. SQL is validated against an allowlist (SELECT only, no DDL/DML/IO) and runs with resource limits (5s timeout, 10k rows, 512MB memory).

**Parameterized queries:** Use `?` placeholders with a `params` array to avoid string quoting issues:

```json
{
  "sql": "SELECT * FROM data WHERE country IN (?, ?) AND year >= ?",
  "params": ["United States", "Japan", 2020]
}
```

This eliminates the triple-nested escaping problem (SQL quotes inside JSON inside shell). See [sql-query.md](references/sql-query.md) for details.

## Composition (Cross-Dataset Joins)

The compose endpoints let you join two datasets and preview or download the result without writing SQL. Useful for enriching a dataset with columns from a related one (e.g., joining county-level education data with census demographics).

**Workflow:** Call `/joinable` to discover what can be joined, `/compose/preview` to check the result, then `/compose/download.csv` to export. See [composition.md](references/composition.md) for full details.

**Composite keys:** `source_column` and `join_column` accept arrays for multi-column joins. Both arrays must have the same length.

```bash
# 1. What can this dataset join with?
curl 'https://api.tryopendata.ai/v1/datasets/nces/naep/joinable'

# 2. Preview the join (anonymous: 100 rows, authenticated: 5000 rows)
curl -X POST 'https://api.tryopendata.ai/v1/datasets/nces/naep/compose/preview' \
  -H 'Content-Type: application/json' \
  -d '{"joins": [{"target": "census/saipe", "source_column": "jurisdiction_name", "join_column": "name"}]}'

# 2b. Composite key join (match on multiple columns)
curl -X POST 'https://api.tryopendata.ai/v1/datasets/nces/naep/compose/preview' \
  -H 'Content-Type: application/json' \
  -d '{"joins": [{"target": "census/saipe", "source_column": ["state", "year"], "join_column": ["name", "year"]}]}'

# 3. Download the full join as CSV (auth required)
curl -H "Authorization: Bearer ${OPENDATA_API_KEY}" \
  'https://api.tryopendata.ai/v1/datasets/nces/naep/compose/download.csv?target=census/saipe&source_column=jurisdiction_name&join_column=name' \
  -o composed.csv
```

## Search

The `GET /v1/search` endpoint supports three modes:

- **`keyword`**: Traditional full-text search with tsvector matching. Supports Google-style query syntax: quotes for phrases, `-` to exclude, `OR` for alternatives.
- **`semantic`**: Embedding-based similarity search for conceptual matching (e.g., "inflation data" finds CPI datasets).
- **`hybrid`** (default): Combines both using Reciprocal Rank Fusion (RRF). Best for most queries.

**Sort options:** `relevance` (default), `recency`, `name`, `popularity` (stars), `trending` (time-decayed activity), `queries`, `downloads`.

**Filters:** `provider`, `format`, `category`, `status` (defaults to "ready").

**Time ranges (for trending/queries/downloads sort):** `today`, `week`, `month`, `year`, `all_time`.

**Autocomplete:** `GET /v1/search/suggest?q=con` returns dataset names matching the prefix for typeahead.

All search results include graph intelligence fields (`importance`, `bridge_score`, `community_id`, `community_label`, `graph_available`). Graph scores contribute to search ranking via a multiplicative boost.

**View results:** Search may return dataset views alongside regular datasets. View results have `result_type: "view"`, a `view_name` field, and a `parent_ref` linking to the parent dataset. Query views using colon syntax: `FROM "provider/dataset:view_name"`.

## Enriched Metadata

The `GET /v1/datasets/{provider}/{dataset}/meta/enriched` endpoint returns AI-enriched metadata including:
- Provider and dataset-level descriptions (short, long, layman, technical)
- Subject tags, geographic/temporal granularity
- Column metadata (display names, descriptions, aliases, semantic types)
- Methodology (structured bullets or summary text)
- Known limitations
- Canonical questions
- Shape classification and KPI snapshot
- Metadata coverage score (8 quality checks across 3 tiers)
- YAML-declared joins with measured coverage percentages

## Chart Data

The `GET /v1/datasets/{provider}/{dataset}/chart` endpoint returns pre-aggregated chart data optimized for each dataset shape:

| Shape | Response key | Payload |
|-------|-------------|---------|
| `timeseries` | `series` | `{date, value}[]` with auto-downsampling when >500 points |
| `panel` | `panel` | Top-5 entities, each with `{date, value}[]` series |
| `categorical` | `buckets` | Top-20 `{label, count}[]` |
| `geo` | `regions` | `{region: value}` map using latest time period |

**Downsampling (timeseries only):** When raw data exceeds 500 points, the endpoint auto-buckets via `date_trunc` at the finest granularity that fits (week/month/quarter/year). Response includes `downsampled: true`, `granularity`, `aggregation` ("avg" or "count"), and `raw_count`. Returns 404 for tabular/text shapes.

## Activity Feed

The `GET /v1/datasets/{provider}/{dataset}/activity` endpoint returns recent system events (enrichment, ingestion, schema changes) in reverse chronological order. Accepts `?limit=` (1-50, default 20).

## Graph Intelligence

Datasets are connected in a knowledge graph (Neo4j). Graph algorithms (PageRank, betweenness centrality, Leiden community detection) produce scores that surface in search rankings, dataset metadata, and related datasets.

**On dataset metadata:** Pass `?include_graph=true` to `/meta` to get a `graph` block with importance, bridge_score, and community info.

**Dataset-specific graph endpoints** live under `/v1/graph/datasets/{provider}/{dataset}/`:
- `stats` - Graph-computed statistics (importance, bridge score, community, connection count)
- `join-paths` - Multi-hop join paths with configurable `max_hops` (1-3), `min_confidence`, and `limit`
- `related` - Blended structural + semantic related datasets
- `neighbors` - Direct 1-hop connections, filterable by `edge_types` (comma-separated, e.g., `SIMILAR_TO,BELONGS_TO`)
- `schema-graph` - Schema-level subgraph for D3 visualization with configurable `depth` (1-3)

**Global graph endpoints** live under `/v1/graph/`:
- `communities` - List communities with top datasets and dominant topics
- `communities/{id}/datasets` - Datasets in a community, sorted by importance
- `bridges` - Top bridge datasets by betweenness centrality
- `subgraph` - Seeded subgraph for graph explorer (accepts `seed_type`, `seed_id`, `depth`, `limit`). Dataset seeds use `provider/slug` format.
- `entities/{type}/{id}/datasets` - Datasets referencing a specific entity
- `health` - Graph connection status and sync info

All graph endpoints return 503 when Neo4j is unavailable. See [graph.md](references/graph.md) for details.

## Discovery

The `GET /v1/discover` endpoint returns datasets matching a natural language query, enriched with metadata tailored for LLM agents and programmatic integrations. Results include column schemas (with units, value ranges, display names), available views, canonical questions, methodology summaries, sample rows, and relevance scores. Unlike `/v1/search`, discover is authenticated and optimized for machine consumption rather than human browsing.

**Batch discover:** `POST /v1/discover/batch` accepts multiple queries in one call, deduplicates results, and returns per-query dataset references alongside the full metadata. See [discover.md](references/discover.md) for details.

## Reference Files

| File                                                                     | When to load                                               |
| ------------------------------------------------------------------------ | ---------------------------------------------------------- |
| [references/filtering.md](references/filtering.md)                       | Writing filter expressions, checking operator syntax       |
| [references/aggregation.md](references/aggregation.md)                   | Using group_by, aggregate functions, summary queries       |
| [references/pagination-and-sort.md](references/pagination-and-sort.md)   | Paginating large results, sorting, cursor-based pagination |
| [references/column-introspection.md](references/column-introspection.md) | Discovering schema, column types, value distributions      |
| [references/output-formats.md](references/output-formats.md)             | Exporting CSV/TSV/XLSX, field projection, system columns   |
| [references/common-patterns.md](references/common-patterns.md)           | Recipes for exploratory analysis and data research         |
| [references/sql-query.md](references/sql-query.md)                       | Raw SQL query endpoint, allowed functions, security model  |
| [references/discover.md](references/discover.md)                         | Using the discover endpoint, LLM agent integration, dataset discovery |
| [references/composition.md](references/composition.md)                   | Cross-dataset joins: joinable, preview, CSV download       |
| [references/graph.md](references/graph.md)                               | Graph intelligence: communities, importance, bridge scores |