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 agentmods add skills/jpoutrin/product-forge/sqlmeshnpx skills add jpoutrin/product-forge --skill sqlmeshgit clone --depth 1 https://github.com/jpoutrin/product-forgeWhat 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 | $0.00035 | $0.00418 |
| Opus 5 | $0.00017 | $0.00209 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
sqlmesh 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 2d 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.
What it actually says
SQLMesh Skill
This skill provides SQLMesh patterns for data transformation.
Project Structure
sqlmesh_project/
├── config.yaml
├── models/
│ ├── staging/
│ │ └── stg_customers.sql
│ └── marts/
│ └── dim_customers.sql
├── macros/
├── seeds/
├── audits/
└── tests/
Model Definition
-- models/staging/stg_customers.sql
MODEL (
name staging.stg_customers,
kind INCREMENTAL_BY_TIME_RANGE (
time_column created_at
),
cron '@daily'
);
SELECT
id AS customer_id,
LOWER(email) AS email,
created_at
FROM raw.customers
WHERE created_at BETWEEN @start_ds AND @end_ds
Model Kinds
| Kind | Use Case |
|---|---|
FULL |
Complete refresh each run |
INCREMENTAL_BY_TIME_RANGE |
Time-based incremental |
INCREMENTAL_BY_UNIQUE_KEY |
Key-based merge |
VIEW |
Virtual table |
SEED |
Static CSV data |
Virtual Environments
# Create a virtual environment for testing
sqlmesh plan dev
# Apply to production
sqlmesh plan prod
Audits
-- audits/no_nulls.sql
AUDIT (
name assert_no_null_customer_id,
model staging.stg_customers
);
SELECT * FROM staging.stg_customers
WHERE customer_id IS NULL
Best Practices
- Use column-level lineage for impact analysis
- Leverage virtual environments for testing
- Define audits for data quality
- Use incremental models for efficiency
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.
- 2d ago First seen · 86 lines · 35 tokens per session scan A d15e9157ebb6
sqlmesh is a skill published in the GitHub repository jpoutrin/product-forge (15 stars, last pushed 6mo ago), licensed MIT. It adds 35 tokens to every session and 418 once invoked, about $0.0002 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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