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 neo4j-contrib/neo4j-skills --skill neo4j-snowflake-graph-analytics-skillgit clone --depth 1 https://github.com/neo4j-contrib/neo4j-skillsWrote 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/neo4j-contrib/neo4j-skills/neo4j-snowflake-graph-analytics-skill)<a href="https://agentmods.dev/skills/neo4j-contrib/neo4j-skills/neo4j-snowflake-graph-analytics-skill"><img src="https://agentmods.dev/badge/skills/neo4j-contrib/neo4j-skills/neo4j-snowflake-graph-analytics-skill/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/neo4j-contrib/neo4j-skills/neo4j-snowflake-graph-analytics-skill"><img src="https://agentmods.dev/badge/skills/neo4j-contrib/neo4j-skills/neo4j-snowflake-graph-analytics-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 371 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 10 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 373 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 395 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 450 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 460 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00222 | $0.07039 |
| Opus 5.5 | $0.00089 | $0.02816 |
| Sonnet 5.5 | $0.00044 | $0.01408 |
| Haiku 4.5 | $0.00022 | $0.00704 |
Grade A, and why
neo4j-snowflake-graph-analytics-skill 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 6d 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 — 525 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Snowflake Native App — graph algorithm power inside Snowflake. Data stays in Snowflake; project into a graph, run algorithms via SQL CALL, results written back to Snowflake tables.
Docs: https://neo4j.com/docs/snowflake-graph-analytics/current/
When to Use
- Running graph algorithms / GDS in Snowflake
- Data already lives in Snowflake tables
- On-demand / pipeline workloads — ephemeral sessions, pay per session-minute
- Full isolation from the live database during analytics
When NOT to Use
- Aura Pro with embedded GDS plugin →
neo4j-gds-skill - Aura Graph Analytics →
neo4j-aura-graph-analytics-skill - Self-managed Neo4j with embedded GDS plugin →
neo4j-gds-skill - Writing Cypher queries →
neo4j-cypher-skill
The End-to-End Flow
This is the flow that works. Don't jump straight to a CALL — most failures come from skipping the data-preparation step.
- Explore the source data — inspect table DDLs to learn columns and types.
- Prepare projection views — create node/relationship views that expose the required key columns and cast every property to a supported type (see the strict rules below). This is the step that matters most.
- Project → Compute → Write — run the algorithm with a single
CALL, assembling theproject,compute, andwriteconfig. - Inspect & look up names — join numeric results back to the source table to get human-readable labels.
Step 1 — Explore the Source Data
Look at the table definitions before designing the graph:
SELECT GET_DDL('TABLE', 'MY_DATABASE.MY_SCHEMA.MY_TABLE');
-- or inspect columns/types:
SELECT COLUMN_NAME, DATA_TYPE
FROM MY_DATABASE.INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = 'MY_SCHEMA' AND TABLE_NAME = 'MY_TABLE';
Decide which tables are nodes and which represent relationships (edges) between them.
Step 2 — Prepare Projection Views (the important part)
The graph engine is strict about column names and types. Snowflake views inherit the source column type by default, so you MUST add explicit CASTs — never SELECT col without one for a property column.
What ships with it
2 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.
- 6d ago Changed · +1 lines f3a3c2ffe163
- 17d ago Changed c211d528aa9f
- 1mo ago First seen · 524 lines · 222 tokens per session scan A 14b7a321b3b0
neo4j-snowflake-graph-analytics-skill is a skill published in the GitHub repository neo4j-contrib/neo4j-skills (116 stars, last pushed yesterday), licensed MIT. It adds 222 tokens to every session and 7,039 once invoked, about $0.0009 per session on Opus 5.5. 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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