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-lakeflow-pipeline-engineeringgit 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-lakeflow-pipeline-engineering)<a href="https://agentmods.dev/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakeflow-pipeline-engineering"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakeflow-pipeline-engineering/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-lakeflow-pipeline-engineering"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/databricks-lakeflow-pipeline-engineering.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 72 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 | $0.00102 | $0.03293 |
| Opus 5 | $0.00051 | $0.01647 |
| Sonnet 5 | $0.00020 | $0.00659 |
| Haiku 4.5 | $0.00010 | $0.00329 |
Grade A, and why
databricks-lakeflow-pipeline-engineering 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 7d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
databricks-lakeflow-pipeline-engineering
Purpose
This skill decides whether a Lakeflow pipeline architecture is correct, efficient, and aligned to the business domain. A pipeline design is sound only when medallion layers align to data ownership and SLA boundaries, Lakeflow Jobs dependencies are explicit and acyclic, table layout choices match the workload (liquid clustering for all new tables, deletion vectors only on DBR 15.4+, Predictive Optimization only on supported tiers), Auto Loader is justified by volume, schema evolution is intentional and _rescued_data is monitored, materialized views and streaming tables are chosen per refresh cadence, and backfill restarts are safe.
When to use
- A user is designing a new Lakeflow pipeline and needs medallion layering, job orchestration, and table-layout guidance.
- A user is migrating existing Delta Live Tables code and needs to understand the Lakeflow Spark Declarative Pipelines naming and API (pyspark pipelines decorator syntax).
- A user is diagnosing pipeline performance or cost issues tied to table layout, Auto Loader schema evolution, or materialized-view refresh overhead.
- A user is choosing between Auto Loader and COPY INTO for file ingestion at scale.
- A user is planning a backfill strategy and needs to verify checkpoint isolation and state-schema safety.
When NOT to use
- No pipeline source or table metadata is available — ask for the pipeline definition or table schema rather than guessing.
- The concern is streaming state schema immutability, checkpoints, or trigger choice — route to
databricks-streaming-reliability-agent. - The concern is data quality expectations, violations, or Lakehouse Monitoring — route to
databricks-data-quality-observability-agent. - The concern is SQL warehouse performance on the pipeline's output tables →
databricks-sql-performance-agent. - The concern is bundle promotion or CI/CD workflow →
databricks-developer-platform-agent.
Scope
- Medallion-layer design and boundary alignment to data ownership and SLAs.
- Lakeflow Jobs orchestration, task dependencies, and DAG structure.
- Delta table layout: liquid clustering vs Z-order, partitioning, deletion vectors (DBR 15.4+), Predictive Optimization (tier-dependent).
- Auto Loader justification, detection-mode choice, schema-evolution modes (
addNewColumns,addNewColumnsWithTypeWidening,rescue,failOnNewColumns,none), and_rescued_datahandling. - Materialized view versus streaming table selection per refresh cadence and correctness requirements.
- Backfill strategy: checkpoint lifecycle, state-schema immutability, restart safety.
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.
- 7d ago First seen · 135 lines · 102 tokens per session scan A e409b6e4f22c
databricks-lakeflow-pipeline-engineering is a skill published in the GitHub repository VincentChuWaiChow/vanguard-frontier-agentic (22 stars, last pushed 3d ago), licensed Apache-2.0. It adds 102 tokens to every session and 3,293 once invoked, about $0.0005 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.
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