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 exasol-labs/exasol-agent-skills --skill exasol-notebook-connector-configgit clone --depth 1 https://github.com/exasol-labs/exasol-agent-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/exasol-labs/exasol-agent-skills/exasol-notebook-connector-config)<a href="https://agentmods.dev/skills/exasol-labs/exasol-agent-skills/exasol-notebook-connector-config"><img src="https://agentmods.dev/badge/skills/exasol-labs/exasol-agent-skills/exasol-notebook-connector-config/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/exasol-labs/exasol-agent-skills/exasol-notebook-connector-config"><img src="https://agentmods.dev/badge/skills/exasol-labs/exasol-agent-skills/exasol-notebook-connector-config.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 | $0.00088 | $0.00778 |
| Opus 5 | $0.00044 | $0.00389 |
| Sonnet 5 | $0.00018 | $0.00156 |
| Haiku 4.5 | $0.00009 | $0.00078 |
Grade A, and why
exasol-notebook-connector-config 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exasol Notebook-Connector Config Skill
Trigger when the user mentions notebook-connector, secure config store, Secrets, configure notebook-connector, set up credentials for TE, set up credentials for TXAIE, first-time notebook-connector setup, or similar setup tasks.
Purpose
This skill establishes the Python-managed secure configuration that later notebook-connector skills depend on.
After configuration is complete:
- activate exasol-itde for local Docker DB lifecycle
- activate exasol-notebook-connections for Python DB and BucketFS helper APIs
- activate exasol-transformers for Transformers Extension setup and usage
- activate exasol-text-ai for Text AI Extension setup and usage
- activate exasol-bucketfs or exasol-udfs only for deeper BucketFS or SLC work beyond notebook-connector setup
Routing Algorithm
Choose the narrowest path that matches the user request:
-
Python-based configuration
- Trigger phrases:
Secrets,AILabConfig,StorageBackend,save config in python,notebook cell,script - Load:
references/secrets-python.md - Use scripts from:
scripts/
- Trigger phrases:
-
Validation / smoke tests
- Trigger phrases:
check config,verify connection,validate notebook-connector,smoke test - Load:
references/validation.md - Use scripts from:
scripts/
- Trigger phrases:
-
Downstream notebook-connector work
- Trigger phrases:
bring_itde_up,open_pyexasol_connection,open_sqlalchemy_connection,open_ibis_connection,open_bucketfs_bucket,initialize_te_extension,initialize_text_ai_extension,deploy_license - Hand off to exasol-itde, exasol-notebook-connections, exasol-transformers, or exasol-text-ai after setup validation succeeds
- Trigger phrases:
Multiple routes can apply. Load all matching references before responding.
Default Guidance
- Prefer the Python path when the user wants notebook cells, automation, or agent-generated code.
- Before handing off to DB helpers, BucketFS helpers, TE, or TXAIE work, run
scripts/validate_config.pyto confirm theSecretsstore is populated for the expected backend. - Use
AILabConfigas the source of the common key names stored inSecrets.
What ships with it
5 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.
- 8d ago First seen · 68 lines · 88 tokens per session scan A 3f0ce23172ff
exasol-notebook-connector-config is a skill published in the GitHub repository exasol-labs/exasol-agent-skills (10 stars, last pushed 8d ago), licensed MIT. It adds 88 tokens to every session and 778 once invoked, about $0.0004 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 2026-09-04.
Other skills, from other repositories
using-datavault4dbt
Builds Data Vault 2 models in dbt with the datavault4dbt package — staging, hubs, links, satellites, and business-vault entities — using the YAML-metadata macro pattern with correct hashkeys, hashdiffs, naming, and materializations. Use when creating or editing datavault4dbt models, setting up a raw vault, choosing…
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. Trigger when user asks to: Store or search vector embeddings in PostgreSQL Set up semantic search, similarity search, or nearest neighbor search Create HNSW or IVFFlat indexes for vectors…
postgres-hybrid-text-search
Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). Trigger when user asks to: Combine keyword and semantic search Implement hybrid search or multi-modal retrieval Use BM25/pgtextsearch with pgvector together Implement RRF (Reciprocal…
bedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
dynamodb
AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.