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.
npx skills add Canner/wren-engine --skill wren-dlt-connectorgit clone --depth 1 https://github.com/Canner/wren-engineWrote 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/skills/canner/wren-engine/wren-dlt-connector)<a href="https://agentmods.dev/skills/canner/wren-engine/wren-dlt-connector"><img src="https://agentmods.dev/badge/skills/canner/wren-engine/wren-dlt-connector/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/skills/canner/wren-engine/wren-dlt-connector"><img src="https://agentmods.dev/badge/skills/canner/wren-engine/wren-dlt-connector.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 | $0.00160 | $0.02668 |
| Opus 5 | $0.00080 | $0.01334 |
| Sonnet 5 | $0.00032 | $0.00534 |
| Haiku 4.5 | $0.00016 | $0.00267 |
Grade A, and why
wren-dlt-connector 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 9d 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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wren-dlt-connector
Connect SaaS data to Wren Engine for SQL analysis — from zero to a verified, queryable project in one conversation.
Who this is for
Data analysts who know SQL and some Python, but may not have used dlt or Wren before. Explain concepts briefly when they first appear, but don't over-explain things a SQL-literate person would already know.
Overview
This skill walks through a four-phase workflow:
- Extract — Use dlt (data load tool) to pull data from a SaaS API into a local DuckDB file
- Model — Introspect the DuckDB schema and auto-generate a Wren semantic project (YAML models, relationships, profile)
- Build & Verify — Build the project and run actual SQL queries to confirm everything works end-to-end
- Handoff — Show the user their data and next steps
The user might enter at any phase. Ask which phase they're starting from — they may already have a .duckdb file and just need phases 2–4.
The goal is a project that actually queries successfully, not just files that look correct. Always run the verification step before declaring success.
Critical: DuckDB catalog naming
When wren engine connects to a DuckDB file, it ATTACHes it using the filename (without .duckdb extension) as the catalog alias:
ATTACH DATABASE 'stripe_data.duckdb' AS "stripe_data" (READ_ONLY)
This means every model's table_reference.catalog must equal the DuckDB filename stem. If the file is hubspot.duckdb, the catalog is hubspot. If it's my_pipeline.duckdb, the catalog is my_pipeline.
Getting this wrong causes "table not found" errors at query time. The introspect_dlt.py script handles this automatically.
Critical: Type normalization
Column types must be normalized using wren SDK's type_mapping.parse_type() function, which uses sqlglot to convert database-specific types (like DuckDB's HUGEINT, TIMESTAMP WITH TIME ZONE) into canonical SQL types that wren-core understands. Do not hardcode type mappings — always delegate to parse_type(raw_type, "duckdb").
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 270 lines · 160 tokens per session scan A c97903ad746c
wren-dlt-connector is a skill published in the GitHub repository Canner/wren-engine (663 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 160 tokens to every session and 2,668 once invoked, about $0.0008 per session on Opus 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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