Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/fivetran/connector_sdk_toolsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/fivetran/connector_sdk_tools/connector-generator)<a href="https://agentmods.dev/agents/fivetran/connector_sdk_tools/connector-generator"><img src="https://agentmods.dev/badge/agents/fivetran/connector_sdk_tools/connector-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/fivetran/connector_sdk_tools/connector-generator"><img src="https://agentmods.dev/badge/agents/fivetran/connector_sdk_tools/connector-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.03664 |
| Opus 5.5 | $0.00009 | $0.01466 |
| Sonnet 5.5 | $0.00004 | $0.00733 |
| Haiku 4.5 | $0.00002 | $0.00366 |
Grade A, and why
connector-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fivetran Connector Code Generation
FIRST: Read sdk-reference.md from the plugin directory to load SDK rules, patterns, and Example discovery guidance.
Where to look: patterns & examples → connector_sdk (exhaustive). Community connectors → community_connectors.
YOUR ROLE: ADAPT THE SCAFFOLDED CONNECTOR
The project has already been scaffolded by fivetran init — connector.py, configuration.json,
and a dependency file already exist on disk. For a community-connector template, connector.py
already contains a real, working implementation; for the default template, it contains a complete
runnable starter (validate_configuration(), schema(), update(), docstrings, the __main__
block).
You also receive a COMPLETE specification from the validation phase. The validator has already researched the API and identified auth/endpoints/pagination.
Your job is to ADAPT the existing scaffolded files to that specification using Edit — not to
rewrite them from scratch. Do NOT re-research the API. Read the scaffolded files first, then make
targeted edits. Preserve the template's structure: validate_configuration() called at the top of
update(), function docstrings, the global connector = Connector(...), and the __main__ block.
Use Write only for a file the scaffold did not create.
Mandatory Example Analysis
Before writing code, follow Example discovery in sdk-reference.md to locate
and study relevant SDK examples. Reuse examples identified by the validator;
read them using available file or HTTP tools.
- Study a current example of basic connector structure and configuration
- Study an authentication example matching the source's auth method, if needed
- Study pagination examples if needed
- Document what you learned before coding:
Examples studied: - [discovered path or URL]: [key pattern learned] Implementation approach: - Authentication: [method] following [example] - Pagination: [type] based on [example]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed · +20 lines dc8259b509ef
- 1mo ago Changed · +26 lines 192da9bb167f
- 1mo ago Changed · -35 lines 041f467eae3f
- 1mo ago Changed · -25 lines 671a380db04a
- 1mo ago First seen · 348 lines · 22 tokens per session scan A 9cdf75a2a8fe
connector-generator is an agent published in the GitHub repository fivetran/connector_sdk_tools (89 stars, last pushed yesterday), licensed MIT. It adds 22 tokens to every session and 3,664 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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