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 VincentChuWaiChow/vanguard-frontier-agentic --skill databricks-data-quality-observabilitygit clone --depth 1 https://github.com/VincentChuWaiChow/vanguard-frontier-agenticWrote 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/vincentchuwaichow/vanguard-frontier-agentic/databricks-data-quality-observability)<a href="https://agentmods.dev/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-data-quality-observability"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-data-quality-observability/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/vincentchuwaichow/vanguard-frontier-agentic/databricks-data-quality-observability"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-data-quality-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 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 74 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.
- medium Excessive Agency · line 3 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00076 | $0.03261 |
| Opus 5 | $0.00038 | $0.01631 |
| Sonnet 5 | $0.00015 | $0.00652 |
| Haiku 4.5 | $0.00008 | $0.00326 |
Grade A, and why
databricks-data-quality-observability 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
databricks-data-quality-observability
Purpose
This skill decides whether a Lakeflow pipeline's quality is visible, measurable, and actionable. Quality is verifiable only when expectations are declared with violation modes matching risk, table constraints are appropriate (enforced vs informational), Lakehouse Monitoring profiles the table and detects drift, freshness is monitored with a learned baseline, event logs are interrogated for lineage and results, quality SLAs are explicit and communicated to consumers, and quality evidence surfaces downstream.
When to use
- A user is designing a Lakeflow pipeline and needs guidance on expectations (violation modes), table constraints, and Lakehouse Monitoring configuration.
- A user is diagnosing unexpected data quality failures (missing nulls, unexpected duplicates, staleness) and needs to interrogate pipeline event logs and monitor metrics.
- A user is defining a data quality SLA (e.g. 100% NOT NULL, 99% unique keys, freshness within 1 hour) and needs to verify it is monitorable and communicated to downstream consumers.
- A user is implementing quality evidence surfaces for downstream data consumers and needs to decide what metrics/signals to publish.
- A user is migrating quality logic from manual checks to Lakeflow expectations and needs to understand violation modes and their pipeline impact.
When NOT to use
- No pipeline source, table schema, or quality audit plan is available — ask for the pipeline definition or a list of known quality issues rather than guessing.
- The concern is pipeline structure, medallion layering, or table layout — route to
databricks-lakeflow-pipeline-engineering-agent. - The concern is streaming state schema or checkpoint correctness → route to
databricks-streaming-reliability-agent. - The concern is PII classification or data masking → route to
databricks-data-protection-privacy-agent. - The concern is job or cluster operational reliability → route to
databricks-platform-reliability-agent.
What ships with it
6 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 · 138 lines · 76 tokens per session scan A 46aab890503a
databricks-data-quality-observability is a skill published in the GitHub repository VincentChuWaiChow/vanguard-frontier-agentic (22 stars, last pushed today), licensed Apache-2.0. It adds 76 tokens to every session and 3,261 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
r-ml
R machine learning packages. Use for classification, regression, clustering, deep learning, gradient boosting (xgboost, lightgbm), random forests, neural networks, and time series forecasting.
xgboost
R xgboost package for gradient boosting. Use for high-performance classification, regression, and ranking.
r-ml-deeplearning
R deep learning with torch, keras, tensorflow. Use for neural networks, CNNs, RNNs, and GPU acceleration.
gbm
R gbm package for gradient boosting. Use for gradient boosted regression and classification models.
kernlab
R kernlab package for kernel methods. Use for support vector machines and kernel-based learning.
nlme
R nlme package for mixed-effects models. Use for linear and nonlinear mixed-effects models with correlation structures.