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 dominodatalab/domino-claude-plugin --skill data-connectivitygit clone --depth 1 https://github.com/dominodatalab/domino-claude-pluginWrote 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/dominodatalab/domino-claude-plugin/data-connectivity)<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/data-connectivity"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/data-connectivity/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/dominodatalab/domino-claude-plugin/data-connectivity"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/data-connectivity.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 | $0.00063 | $0.00717 |
| Opus 5 | $0.00032 | $0.00358 |
| Sonnet 5 | $0.00013 | $0.00143 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
domino-data-connectivity 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 9d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domino Data Connectivity Skill
This skill provides comprehensive knowledge for connecting Domino workloads to external data sources, including AWS S3, Azure storage, and credential propagation.
Key Concepts
Data Access Options in Domino
| Option | Use Case |
|---|---|
| Datasets | Project-level data storage |
| Data Sources | External database connections |
| External Data Volumes (EDV) | Mount external storage as volumes |
| S3 Mountpoint | Direct S3 access as file system |
| Credential Propagation | Pass user identity to cloud services |
Credential Propagation Methods
| Method | Cloud | Description |
|---|---|---|
| IRSA | AWS | IAM Role for Service Accounts via OIDC |
| Azure Entra ID | Azure | User-based credential propagation |
Related Documentation
- S3-MOUNTPOINT.md - AWS S3 as local file system
- AWS-IRSA.md - AWS IAM Role for Service Accounts
- AZURE-CREDENTIALS.md - Azure Entra ID integration
Quick Start
Accessing S3 Data
With Mountpoint S3 configured, access S3 as a local file system:
import pandas as pd
# S3 data appears as local files
df = pd.read_parquet("/mnt/s3-data/datasets/sales.parquet")
Using IRSA for AWS Services
With IRSA configured, AWS SDK uses automatic credentials:
import boto3
# No explicit credentials needed - IRSA provides them
s3 = boto3.client('s3')
response = s3.list_objects_v2(Bucket='my-bucket')
Accessing External Data Volumes
EDVs are mounted at configured paths:
# Read from external volume
with open("/mnt/external-data/config.json") as f:
config = json.load(f)
When to Use Each Option
Use S3 Mountpoint When:
- Working with large datasets stored in S3
- Need file system interface to S3
- Want to avoid EFS costs for large data
- Require multi-region data access
Use IRSA When:
- Need AWS service access from Domino workloads
- Policy prohibits long-lived credentials
- Require user-level audit trails
- Need cross-account role assumption
What ships with it
3 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.
- 9d ago First seen · 100 lines · 63 tokens per session scan A 967ca41985a9
domino-data-connectivity is a skill published in the GitHub repository dominodatalab/domino-claude-plugin (6 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 717 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 2026-08-30.
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