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 extractgit 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/extract)<a href="https://agentmods.dev/skills/sfc-gh-dflippo/snowflake-dbt-demo/extract"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/extract/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/extract"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/extract.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 | $0.00055 | $0.01202 |
| Opus 5 | $0.00028 | $0.00601 |
| Sonnet 5 | $0.00011 | $0.00240 |
| Haiku 4.5 | $0.00006 | $0.00120 |
Grade A, and why
rule-engine-extract 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Rules
Analyze changes to converted SQL files and extract reusable migration rules. Works in two modes:
- Interactive fix mode — the user fixed a file; compare before/after to extract a rule
- Git history mode — analyze committed changes retroactively
Prerequisites
- Rule engine set up (../SKILL.md)
- Snowflake connection active
Mode Selection
If the user says something like "I fixed X, extract a rule" or "deduce a rule from my fix" → use Interactive Fix Mode.
If the user says "analyze recent changes" or "extract rules from git history" → use Git History Mode.
Interactive Fix Mode
Step 1: Get the "After" SQL
Read the current converted file (the fixed version):
cat <project_dir>/snowflake/<type>/<schema>/<name>.sql
Or ask the user which file they fixed.
Step 2: Get the "Before" SQL
Find the changes in the file — use git diff, git log, or any other method to obtain the previous version. If no history is available, ask the user.
Step 3: Analyze the Diff
Compare before and after to identify distinct fix patterns. For each pattern:
- What changed — describe the transformation (before → after)
- Why — what Snowflake compatibility issue does this fix?
- Is it mechanical? — can a regex handle it, or does it need AI context?
If mechanical (direct find/replace):
- Propose
replacement_mode = 'regex' - Write
match_pattern(detection regex) - Write
replacement_findandreplacement_replace(substitution regex)
If context-dependent:
- Propose
replacement_mode = 'ai' - Write
match_pattern(detection regex) - Write
ai_contextdescribing: what to look for, how to fix it, edge cases to watch for
Always generate examples from the actual before/after.
Step 4: Check for Duplicates
Use the find_similar_rules tool with query set to a description of the pattern.
If a similar rule exists:
This pattern matches existing rule "". No new rule needed.
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 · 159 lines · 0 tokens per session scan A bd42e9d22a39
rule-engine-extract 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 55 tokens to every session and 1,202 once invoked, about $0.0003 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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