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 BauplanLabs/bauplan-skills --skill bauplan-data-semanticsgit clone --depth 1 https://github.com/BauplanLabs/bauplan-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/bauplanlabs/bauplan-skills/bauplan-data-semantics)<a href="https://agentmods.dev/skills/bauplanlabs/bauplan-skills/bauplan-data-semantics"><img src="https://agentmods.dev/badge/skills/bauplanlabs/bauplan-skills/bauplan-data-semantics/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/bauplanlabs/bauplan-skills/bauplan-data-semantics"><img src="https://agentmods.dev/badge/skills/bauplanlabs/bauplan-skills/bauplan-data-semantics.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 | $0.00119 | $0.03065 |
| Opus 5.5 | $0.00048 | $0.01226 |
| Sonnet 5.5 | $0.00024 | $0.00613 |
| Haiku 4.5 | $0.00012 | $0.00307 |
Grade A, and why
bauplan-data-semantics 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 25d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Semantics
Bauplan can hold contextual meaning for data in table metadata: a table-level comment and a per-column doc, both stored in Iceberg, both versioned per data branch. The contextual meaning, stored as table and column documentation, provides support for the semantic layer. This skill reads table and column documentation and drafts what it should say when it is missing.
Documentation cannot be written through the CLI or SDK — it is authored in pipeline code and materialized by a run — so applying a draft always belongs to a write-capable skill.
When to Use This Skill
- Before using a semantically loaded column — money, duration, distance, counts, status
- When a query returned a plausible number you suspect is wrong
- When a user asks what a table or column means
- When
bauplan-data-pipelineorbauplan-migrate-to-typed-sdkneeds docstring andTableField(doc=)text for schemas it is about to declare
For data quality profiling use bauplan-explore-data; for whether a question is answerable at all, bauplan-data-assessment; for a failed run, bauplan-debug-and-fix-pipeline.
Updating documentation that already exists is not yet covered here.
CRITICAL RULES
- Read-only. Never edit
models.py,*.sql, or a schema class. Never run, merge, or create a branch. Draft text and hand it off. - Never invent meaning. Draft only what the producing code, the upstream catalog, or the user supports. A confident wrong doc is worse than none, because it gets trusted.
- Label every claim as read from the catalog or inferred by you.
- Mind the branch. Documentation is branch-scoped: a table documented on one branch may be bare on another. Prefer the active branch; pass
--refwhen reading a different branch, or comparing several at once. - Read docs as JSON whenever you act on what they say. The
ttyview truncates.
If any rule cannot be satisfied, STOP and report the blocker.
Reading the Semantic Layer
bauplan table get <namespace>.<table> -O json
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.
- 25d ago Changed d022281b1e25
- 1mo ago First seen · 274 lines · 119 tokens per session scan A dc5a575ed71b
bauplan-data-semantics is a skill published in the GitHub repository BauplanLabs/bauplan-skills (17 stars, last pushed 3d ago), licensed MIT. It adds 119 tokens to every session and 3,065 once invoked, about $0.0005 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 2026-09-19.
bauplan-data-semantics compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| bauplan-data-semanticsBauplanLabs/bauplan-skills | Skill | 17 | 3d ago | MIT | A | 119 | Claude Code |
| arboretoK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 55 | Claude Code, Codex |
| deepspot-mK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 70 | Claude Code |
| torchdrugK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 64 | Claude Code |
| pyhealthK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 84 | Claude Code, Codex |
| nemo-mbridge-perf-expert-parallel-overlapNVIDIA/skills | Skill | 3.9k | yesterday | Apache-2.0 | A | 56 | Claude Code |
| ml-training-recipesOrchestra-Research/AI-Research-SKILLs | Skill | 14k | 27d ago | MIT | A | 88 | Claude Code |