# Build Connector

> Build a new Fivetran connector from a description. Use when the user wants to create, generate, or scaffold a new connector for an API or data source.

- Skill: `fivetran/build-connector-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add fivetran/build-connector-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fivetran/build-connector-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Integrations & APIs
- Author: fivetran (https://skillmd.com/u/fivetran)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fivetran/build-connector-2

---


> **Context**: This plugin is for the Fivetran Connector SDK (CSDK). "CSDK" is shorthand for "Connector SDK".

# Build a New Fivetran Connector

**FIRST**: Read `sdk-reference.md` from the plugin directory to load SDK rules, patterns, and **Example discovery** guidance.

You are building a complete Fivetran connector from the user's description. This skill orchestrates the build; detailed per-phase logic lives in the plugin's workflow files.

## Phase 0: Determine the Best Build Approach

**Before doing any API research or code generation**, check whether a simpler path exists. A custom CSDK connector means the user writes and maintains code; other paths may let Fivetran manage the connector for them. Always surface better options to the user before building custom.

Run all three checks in order and present findings to the user. The user decides — if they prefer to build custom even when a managed option exists, proceed to Phase 1.

### Check 1: Is there already a Fivetran connector for this source?

Fetch all five category pages in parallel using WebFetch and scan each for the source name the user mentioned (case-insensitive fuzzy match):

- https://fivetran.com/docs/connectors/databases
- https://fivetran.com/docs/connectors/events
- https://fivetran.com/docs/connectors/files
- https://fivetran.com/docs/connectors/functions
- https://fivetran.com/docs/connectors/applications

Ask each fetch: "List all connector names on this page, one per line."

- **If you find a match or a very close match in any page**, tell the user:

  > *"Would you prefer to use our **{Match}** managed native connector or need to customize it for your use case? The connector is maintained by Fivetran — no code to write or maintain. You can configure it directly in the Fivetran dashboard."*

  Wait for the user's answer. If they choose the managed connector, stop and direct them to the Fivetran dashboard. If they want custom CSDK anyway, go straight to Phase 1.

- **If there's no match**, the applications page may be incomplete (it is a large list and can be truncated). As a fallback, probe a few direct URL slug variations using WebFetch — try the most likely slugs derived from the source name (lowercase, spaces to hyphens, common abbreviations) across all categories:

  ```
  https://fivetran.com/docs/connectors/applications/{slug}
  https://fivetran.com/docs/connectors/databases/{slug}
  https://fivetran.com/docs/connectors/files/{slug}
  https://fivetran.com/docs/connectors/events/{slug}
  ```

  For example, for "Google Analytics 4" try: `google-analytics-4`, `google-analytics`. For "Amazon RDS for MySQL" try: `amazon-rds-mysql`, `mysql-rds`. Fetch 2–3 variations — if a page returns a valid connector description, a match exists.

  If the slug probes also return no match, tell the user:

  > *"I didn't find **{Source}** in Fivetran's connector catalog, but the catalog may be incomplete. Please quickly verify at https://fivetran.com/integrations before we proceed."*

  Proceed to Check 2 once the user confirms.

### Check 2: Is this a good fit for a Lite Connector (AI builder)?

Lite Connectors are built with Fivetran's AI builder and **managed by Fivetran** — the customer doesn't write or maintain any code. Docs: https://fivetran.com/docs/connectors/applications/lite-connectors

Criteria for recommending Lite:
- Source is a **SaaS application** (not a database, file system, event stream, or in-house system)
- Exposes a **REST API** with **JSON responses**
- Uses **standard authentication** (API key, Bearer token, or OAuth 2)
- No complex stateful logic or heavy transformations needed

If the user's source fits these criteria, tell them:

> *"Based on what you described, **{Source}** looks like a good fit for a Fivetran [Lite Connector](https://fivetran.com/docs/connectors/applications/lite-connectors). Lite Connectors are built with Fivetran's AI builder and are **managed by Fivetran** — you won't have to write or maintain any connector code. Fivetran handles API changes, bug fixes, and upgrades. Want to try the Lite path first? It's typically faster to set up and lower ongoing maintenance than a custom CSDK connector."*

Wait for the user's answer. If they choose the Lite path, stop and direct them to the Lite Connector builder. If they want custom CSDK, continue to Phase 1.

### Check 3: Proceed with custom CSDK

If neither Check 1 nor Check 2 produced a better option, or the user explicitly chose custom CSDK, continue to Phase 1.

## Phase 1: Research & Validate Requirements

Apply the validator workflow — read `workflows/validator.md` in the plugin directory (or, in plugins that support subagents, invoke the `connector-validator` subagent) to research the API and produce a complete specification **and a discovery result**: `EXACT MATCH`, `FUZZY MATCH`, or `BUILD ON TEMPLATE`.

**Stop and wait** for the user to answer any clarifying questions the validator surfaces before proceeding to Phase 2.

## Phase 2: Scaffold the Project with `fivetran init`

`fivetran init` is the canonical scaffolding path — it produces a complete, runnable connector with the correct structure (`validate_configuration()`, docstrings, the `__main__` block). **Always scaffold with `fivetran init`; never hand-write the project from scratch.** Pick the command from the Phase 1 discovery result. The project directory is the connector name (lowercase, underscores).

- **EXACT MATCH / FUZZY MATCH** — start from the community connector:
  ```bash
  printf '\n' | fivetran init "<connector_dir>" --template connectors/<name> --force
  ```
- **BUILD ON TEMPLATE** — start from the default template:
  ```bash
  printf '\n' | fivetran init "<connector_dir>" --force
  ```

Windows PowerShell: replace `printf '\n' |` with `"" |`.

**Why the piped newline and `--force`:** `fivetran init` always runs an interactive "which coding agent shall we install the plugin for?" prompt, and there is no flag to skip it. `--force` auto-confirms project creation and file overwrites; the piped empty line answers the agent prompt with an invalid choice, so it logs `invalid choice; skipping agent setup` (this is **expected and benign** — the plugin is already installed) and continues.

**Verify success by checking that `<connector_dir>/connector.py` exists**, not by the exit code — an exhausted input pipe can make `init` exit non-zero even after the files download correctly. `connectors/<name>` resolves to the `community_connectors` repo; `examples/<path>` resolves to `connector_sdk`.

## Phase 3: Customize the Scaffolded Files

Apply the generator workflow — read `workflows/generator.md` (or invoke the `connector-generator` subagent) to adapt the **already-scaffolded** files to the Phase 1 specification using `Edit` (do not rewrite from scratch). For an EXACT MATCH, the connector may need little or no change beyond configuration; for FUZZY/BUILD ON TEMPLATE, study 2–4 relevant SDK examples and adapt `connector.py`, `configuration.json`, and `README.md` to the spec. Preserve the template structure (`validate_configuration()`, docstrings, global `connector = Connector(...)`, the `__main__` block).

## Phase 4: Setup Environment

`fivetran init` already created `requirements.txt` (or `pyproject.toml`). Set up the virtual environment and install dependencies with commands appropriate for the user's OS:

macOS/Linux:
```bash
cd "<project_directory>"
uv venv .venv
uv pip install --python .venv/bin/python -r requirements.txt fivetran_connector_sdk
```

Windows PowerShell:
```powershell
cd "<project_directory>"
uv venv .venv
uv pip install --python .\.venv\Scripts\python.exe -r requirements.txt fivetran_connector_sdk
```

## Phase 5: Enter Configuration & Test

Follow **Configuration entry** in `sdk-reference.md`: reuse local values, try
`fivetran configuration` for missing values, offer a setup form if absent, or fill
ordinary JSON values as agreed with the user. Do not force custom encryption.
Collect missing secrets through the form or the user's local editor/terminal;
do not ask users to paste them into chat.

Once configuration is ready, run the connector:

```bash
python "<plugin>/tools/run_connector.py" "<connector_dir>" --timeout-seconds 600
```

The runner defaults to 120 seconds and accepts up to 600. Use 600 for debug
runs because the first run downloads and starts the Java tester. Set the
harness command timeout to 600 seconds as well.


The runner passes plaintext values through and decrypts only existing encrypted
fields. Plaintext configuration does not require a local encryption key.

Check results:

macOS/Linux:
```bash
.venv/bin/python -c "
import duckdb
conn = duckdb.connect('files/warehouse.db')
tables = conn.execute(\"SELECT table_name FROM information_schema.tables WHERE table_schema = 'tester'\").fetchall()
print(f'Tables synced: {len(tables)}')
for (t,) in tables:
    count = conn.execute(f'SELECT COUNT(*) FROM tester.{t}').fetchone()[0]
    print(f'  tester.{t}: {count} rows')"
```

Windows PowerShell:
```powershell
.\.venv\Scripts\python.exe -c 'import duckdb; conn = duckdb.connect("files/warehouse.db"); tables = conn.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = ''tester''").fetchall(); print("Tables synced:", len(tables)); [print("  tester." + t + ": " + str(conn.execute("SELECT COUNT(*) FROM tester." + t).fetchone()[0]) + " rows") for (t,) in tables]; conn.close()'
```

Report: tables synced, row counts, any errors.

## Phase 6: Auto-Fix on Failure

If the test fails:

1. Read the error output carefully.
2. Classify the error:
   - **INFRA error** (network, JVM, SDK internal): explain the infrastructure issue. Do NOT change code.
   - **FIRST_RUN error** (connector has never succeeded — likely credentials/config): guide the user to verify config. Do NOT change code.
   - **CODE error** (syntax, logic, SDK misuse): apply the fixer workflow — read `workflows/fixer.md` (or invoke the `connector-fixer` subagent). Re-test after fixing.
   - **TOOL error** (`run_connector.py` fails): report to the user. Do NOT modify plugin tools.

**IMPORTANT**: Never modify plugin tools. Only fix the user's connector code.