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 agentmods add skills/insanetic/data-max/data-reviewnpx skills add insanetic/data-max --skill data-reviewgit clone --depth 1 https://github.com/insanetic/data-maxWhat 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 | $0.00211 | $0.03226 |
| Opus 5 | $0.00105 | $0.01613 |
| Sonnet 5 | $0.00042 | $0.00645 |
| Haiku 4.5 | $0.00021 | $0.00323 |
Grade A, and why
data-review 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 2d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Max — Data Review
Review a data file for warehouse readiness and tell the user, with reasons, how to cleanse it. The mental model: the user wants to upload this file mostly as-is into a columnar data warehouse and then run analytics and calculations on it. Your job is to find everything that will make that painful or wrong, and to propose the smallest set of changes that buys the biggest benefit — while still showing the ideal target.
This skill reviews; it does not transform. The deliverable is a markdown
report (<filename>-review.md) plus a short inline summary. You never hand-edit
data and you never write transformation scripts here. When the user wants the
cleanup actually done, that report is the hand-off to the data-transform skill
(see "Handing off" below).
Core principles
- Explain the why, every time. A recommendation without a reason is noise. The user often can't change the source freely (it's an export, an API dump, a vendor file), so they need to understand the payoff to decide what's worth it.
- Tier everything by cost/benefit. Separate what breaks loading from what improves analytics from what's merely nice. See the report template.
- Minimal viable change first, ideal second. Always give a cheap path that still helps, and the better redesign. Let the user choose. Don't bury them.
- Denormalization is fine. This is analytics, not OLTP. Duplication that makes one flat, query-friendly table is usually better than textbook 3NF. Don't normalize for its own sake.
- CSV is the target output. If the input is JSON/XML/Excel, describe the CSV(s) it should become. If it's already CSV, describe the cleaned CSV.
Workflow
- Inspect the file yourself. Read it directly — the header row, the first
chunk of rows, and the tail (footers hide there). Judge each review dimension
by eye:
- rows that aren't part of the table (title banners, blank separators,
repeated mid-table headers,
TOTALfooters) and rows whose column count differs from the rest; - column-name hygiene (spaces, capitals, punctuation, units-as-symbols, leading digits, blanks, duplicates);
- money columns stored as text (
"$1,234.50") or float; - date/datetime columns and whether they're ISO 8601 / RFC 3339;
- whether any column anchors each row in time;
- headers that are really period or category values (
Jan, Feb, …,2021, 2022) — the wide-format smell.
- rows that aren't part of the table (title banners, blank separators,
repeated mid-table headers,
What ships with it
1 file 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.
- 2d ago First seen · 255 lines · 211 tokens per session scan A 01f5693d2dd8
data-review is a skill published in the GitHub repository insanetic/data-max (2 stars, last pushed 3mo ago), licensed MIT. It adds 211 tokens to every session and 3,226 once invoked, about $0.0011 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 2026-08-31.
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