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 --skill data-inspectgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/data-inspect)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/data-inspect"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/data-inspect/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/data-inspect"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/data-inspect.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 | $0.00251 | $0.01546 |
| Opus 5.5 | $0.00100 | $0.00618 |
| Sonnet 5.5 | $0.00050 | $0.00309 |
| Haiku 4.5 | $0.00025 | $0.00155 |
Grade A, and why
data-inspect 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-inspect — 88% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Data Inspect
Purpose
Show the active dataset's schema — tables, columns, row counts, and relationships. Optionally drill into a specific table.
When to Use
Invoke as /data to see the full schema summary, or /data {table} to see column details for a specific table.
Instructions
Start here
Before doing ANYTHING else:
- Read
.knowledge/active.yamlto determine the active dataset name - If no active dataset exists, jump to Mode 3 (No Active Dataset)
- Otherwise, read
.knowledge/datasets/{active}/schema.mdfor schema information
Why this matters: Users often have multiple datasets connected. You MUST use the active one from the config file, never guess or use a different dataset.
Mode 1: /data (full schema overview)
When: User invokes /data or asks "what tables do I have?" / "show me the schema"
Steps:
- ✅ Confirm you've already read
.knowledge/active.yamlandschema.md(see above) - Extract from schema.md:
- Dataset display name
- Connection type and location
- Table list with: name, row count, column count, primary key
- Display in this condensed format:
Active Dataset: {display_name}
Connection: {type} ({database}.{schema} or file path)
Tables:
users ~50,000 rows 8 columns user_id (PK)
products 500 rows 7 columns product_id (PK)
events ~6.5M rows 9 columns event_id (PK)
sessions ~1.4M rows 8 columns session_id (PK)
orders ~30-50K rows 6 columns order_id (PK)
order_items — rows 4 columns order_id + product_id (composite PK)
Use `/data {table}` for column details.
Format notes:
- Left-align table names
- Show approximate row counts (use
~for estimates) - Show column count
- Show primary key or composite key
- Keep it visually scannable — this is a quick reference, not exhaustive detail
Mode 2: /data {table} (table detail)
When: User invokes /data {table} or asks "what columns are in X?" / "show me the X table structure"
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 · 160 lines · 251 tokens per session scan A 633c675a0470
data-inspect is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (322 stars, last pushed 1mo ago), licensed MIT. It adds 251 tokens to every session and 1,546 once invoked, about $0.0010 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 2026-09-12.
data-inspect compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| data-inspectai-analyst-lab/ai-analyst | Skill | 322 | 1mo ago | MIT | A | 251 | Claude Code |
| create-prprisma/orm | Skill | 48k | yesterday | Apache-2.0 | A | 48 | Claude Code, Codex |
| schema-explorationlangchain-ai/deepagents | Skill | 31k | yesterday | MIT | A | 57 | Claude Code, Codex |
| atlas-mssqlariga/atlas | Skill | 8.8k | yesterday | Apache-2.0 | A | 163 | Claude Code |
| ha-data-storesshiwenwen/hope-agent | Skill | 1.9k | yesterday | MIT | A | 115 | Claude Code, Codex |
| nornicdb-cypher-queriesorneryd/NornicDB | Skill | 923 | yesterday | MIT | A | 79 | Claude Code, Codex |
| volcengine-rds-postgresqlbytedance/agentkit-samples | Skill | 484 | today | Apache-2.0 | A | 63 | Claude Code, Codex |