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 ai-analyst-lab/ai-analyst-plugin --skill data-mapgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-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/ai-analyst-lab/ai-analyst-plugin/data-map)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/data-map"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/data-map/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/ai-analyst-lab/ai-analyst-plugin/data-map"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/data-map.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00106 | $0.03044 |
| Opus 5.5 | $0.00042 | $0.01218 |
| Sonnet 5.5 | $0.00021 | $0.00609 |
| Haiku 4.5 | $0.00011 | $0.00304 |
Grade A, and why
data-map 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 1mo 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.
This is a copy
88% identical to data-map — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Data Map
Purpose
Answer the broadest possible question — "tell me about this data" — with the broadest substantive answer: cross-table health, relationships, date alignment, and an opening analytical thread. This is first-contact dataset exploration, not a schema dump and not a steering question.
When to Fire
Fires on dataset-wide open questions:
- "tell me about this data / the data / this dataset / the dataset"
- "what's in here / what's in this data / what's in the data"
- "give me an overview / give me the map / map out the data"
- "what do I have / what do we have here"
- "what does this data look like / show me what we've got"
- Any open question that references the dataset as a whole
Does NOT fire when:
- The question names a specific table → use
data-quality-check - User invoked
/exploreor is mid-exploration within an already-mapped dataset → useexplore - User invoked
/dataor/data {table}→ usedata-inspect(schema-only) - User invoked
/run-pipelineor a specific analysis → run the pipeline
Fires regardless of:
- Whether the dataset was recently profiled (data-profiling populates
.knowledge/, this skill produces a live report) - Whether the user explicitly asked for DQ (it's implicit in "tell me about")
Instructions
Step 0 — Resolve active dataset
- Read
.knowledge/active.yamlto getactive_dataset. If missing, halt and tell the user to run/connect-data(knowledge-bootstrap can create the rest of the tree). - Read
.knowledge/datasets/{active}/manifest.yamlfor connection type and local paths. - Read
.knowledge/datasets/{active}/schema.mdfor table list and column types. - Read
.knowledge/datasets/{active}/quirks.mdto surface known gotchas inline. - Verify connectivity through the session's active data connection (a connected warehouse, local DuckDB, or the CSV files, in that order of preference). Announce which source is active in one line.
Step 1 — Table inventory and PK health
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.
- 1mo ago First seen · 237 lines · 106 tokens per session scan A ea886c338649
data-map is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (33 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 3,044 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to data-map, differing in 16 lines, and is treated as a copy.
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