Borrowing it
Nothing to install: this file belongs to sfc-gh-abannerjee/SnowGram. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sfc-gh-abannerjee/SnowGram/main/CLAUDE.mdgit clone --depth 1 https://github.com/sfc-gh-abannerjee/SnowGramWrote 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/instructions/sfc-gh-abannerjee/snowgram/claude-md)<a href="https://agentmods.dev/instructions/sfc-gh-abannerjee/snowgram/claude-md"><img src="https://agentmods.dev/badge/instructions/sfc-gh-abannerjee/snowgram/claude-md/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/instructions/sfc-gh-abannerjee/snowgram/claude-md"><img src="https://agentmods.dev/badge/instructions/sfc-gh-abannerjee/snowgram/claude-md.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 | $0.01300 | $0.01300 |
| Opus 5 | $0.00650 | $0.00650 |
| Sonnet 5 | $0.00260 | $0.00260 |
| Haiku 4.5 | $0.00130 | $0.00130 |
Grade A, and why
SnowGram CLAUDE.md 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 8d 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SnowGram
Snowflake architecture diagram generator using Cortex Agents with 14 pre-built reference architecture templates.
Quick Reference
| Key | Value |
|---|---|
| Connection | se_demo |
| Database | SNOWGRAM_DB |
| Agent | SNOWGRAM_DB.AGENTS.SNOWGRAM_AGENT |
| Model | claude-sonnet-4-5 |
| Warehouse | COMPUTE_WH |
| Frontend | Next.js 15 at localhost:3002 |
Commands
# Test agent
cd backend/tests/agent && python run_tests.py
# Frontend dev
cd frontend && npm run dev
# Test template output
SELECT SNOWGRAM_DB.CORE.COMPOSE_DIAGRAM_FROM_TEMPLATE('MEDALLION_LAKEHOUSE');
# Recreate agent (after spec changes)
DROP AGENT IF EXISTS SNOWGRAM_DB.AGENTS.SNOWGRAM_AGENT;
CREATE OR REPLACE AGENT SNOWGRAM_DB.AGENTS.SNOWGRAM_AGENT FROM SPECIFICATION $$ ... $$;
Templates (14 Total)
| Template ID | Use Case |
|---|---|
MEDALLION_LAKEHOUSE |
Bronze/Silver/Gold with external sources |
MEDALLION_LAKEHOUSE_SNOWFLAKE_ONLY |
Snowflake-native medallion |
STREAMING_DATA_STACK |
Kafka Connector, Dynamic Tables |
SECURITY_ANALYTICS |
SIEM/log analytics with SOS |
CUSTOMER_360 |
CDP with ML predictions |
ML_FEATURE_ENGINEERING |
Model Registry, Cortex, SPCS |
BATCH_DATA_WAREHOUSE |
Traditional star schema ETL |
REALTIME_IOT_PIPELINE |
Edge software, MQTT, rules engine |
DATA_GOVERNANCE_COMPLIANCE |
Masking, RLS policies |
EMBEDDED_ANALYTICS |
Hybrid Tables, Multi-Cluster WH |
MULTI_CLOUD_DATA_MESH |
Cross-cloud federated |
SERVERLESS_DATA_STACK |
Lambda/Functions, API Gateway |
REALTIME_FINANCIAL_TRANSACTIONS |
High-volume transaction processing |
HYBRID_CLOUD_LAKEHOUSE |
Iceberg, external catalog |
Dual-Track Design
SnowGram has two independent diagram generation tracks (no shared layout code):
| Track 1 — CoCo Skill | Track 2 — Agent + GUI | |
|---|---|---|
| Location | skills/snowflake-architecture-diagram/ |
SNOWGRAM_DB.AGENTS.SNOWGRAM_AGENT + frontend/ |
| Flow | CoCo skill → flow_builder.py → state JSON → index.html viewer |
Cortex Agent + tools + semantic view → ReactFlow + ELK.js |
| Requires Snowflake deploy? | No | Yes |
| Audience | Quick ad-hoc diagrams, no-deploy environments | Interactive GUI with conversational refinement |
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.
- 8d ago First seen · 134 lines · 1,300 tokens per session scan A 3188d7d9eeb3
SnowGram CLAUDE.md is an instructions file published in the GitHub repository sfc-gh-abannerjee/SnowGram (2 stars, last pushed 2mo ago), licensed MIT. It adds 1,300 tokens to every session, about $0.0065 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 2026-08-31.
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