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 sfc-gh-dflippo/snowflake-dbt-demo --skill etl-assessmentgit clone --depth 1 https://github.com/sfc-gh-dflippo/snowflake-dbt-demoWrote 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/sfc-gh-dflippo/snowflake-dbt-demo/etl-assessment)<a href="https://agentmods.dev/skills/sfc-gh-dflippo/snowflake-dbt-demo/etl-assessment"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/etl-assessment/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/sfc-gh-dflippo/snowflake-dbt-demo/etl-assessment"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/etl-assessment.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 | $0.00040 | $0.01862 |
| Opus 5 | $0.00020 | $0.00931 |
| Sonnet 5 | $0.00008 | $0.00372 |
| Haiku 4.5 | $0.00004 | $0.00186 |
Grade A, and why
etl-assessment 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.
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SSIS Assessment
SnowConvert AI migrates SSIS packages to Snowflake. This skill analyzes packages from their source code and SnowConvert assessment CSV reports to generate detailed migration analysis including package classification and complexity assessment.
Sub-Agent Mode
When invoked from a parent skill (e.g., assessment/SKILL.md) as a sub-agent, the parent provides a context block with the fields below. The review_mode field controls whether the per-package review loop runs.
| Field | Required | Notes |
|---|---|---|
project_dir |
yes | absolute path to the SCAI project root |
output_dir |
yes | typically <project_dir>/assessment/ssis |
etl_replatform_sources_path |
no | absolute path to the SSIS .dtsx source directory; falls back to auto-detection per Step 1 |
review_mode |
yes | generate-only, auto-review-all, or skip |
On entry: call the configure MCP tool with project_dir from the context block. Snowflake credentials are not required — scai assessment etl generate reads converted CSVs and SSIS .dtsx sources.
Branching by review_mode:
generate-only— run Steps 1–2 (locate inputs + generate JSON). Return theetl_assessment_analysis.jsonpath; do not run per-package analysis or the AI summary.auto-review-all— run all four steps (locate inputs, generate, analyze every package per references/analyze_ssis_package.md, draft the AI HTML summary, register it). Stop only whenstatsreports no pending packages.skip— return immediately with"status": "skipped".
On completion, return JSON only:
{
"sub_skill": "etl-assessment",
"status": "ok",
"output_json": "<abs path to etl_assessment_analysis.json>",
"summary": "<one-line: total packages, classified count, pending count>",
"error": null
}
On skip: "status": "skipped", "output_json": null. On failure: "status": "error", "error": "<message>".
What ships with it
9 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.
- pyproject.toml 376 B
- references/ai_summary_guide.md 19 KB
- references/analysis_example.md 12 KB
- references/analyze_ssis_package.md 2.0 KB
- references/writing_analysis.md 16 KB
- scripts/__init__.py 666 B runs code
- scripts/dag_renderer/__init__.py 0 B runs code
- scripts/dag_renderer/dag_template.html 17 KB
- scripts/dag_renderer/render_dags.py 1.5 KB runs code
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 · 173 lines · 40 tokens per session scan A d136b88798d4
etl-assessment is a skill published in the GitHub repository sfc-gh-dflippo/snowflake-dbt-demo (33 stars, last pushed 3d ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,862 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 2026-09-10.
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