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/altimateai/altimate-code/data-paritynpx skills add AltimateAI/altimate-code --skill data-paritygit clone --depth 1 https://github.com/AltimateAI/altimate-codeWhat 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 | $0.00048 | $0.04920 |
| Opus 5 | $0.00024 | $0.02460 |
| Sonnet 5 | $0.00010 | $0.00984 |
| Haiku 4.5 | $0.00005 | $0.00492 |
Grade A, and why
data-parity 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 3d 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 — 493 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Parity (Table Diff)
CRITICAL: Regulated / Sensitive Data
data_diff includes up to 5 sample diff rows in the tool output so you can see which values differ. Those rows are part of the conversation and are sent to the LLM provider you're using.
Before running data_diff against a table that might contain PII, PHI, PCI, or other regulated data:
- Ask the user whether the target contains regulated columns.
- If yes, prefer
algorithm: "profile"— it compares column-level statistics (count, nulls, min/max, distinct count) without any row values leaving the database. - If a row-level diff is genuinely required, tell the user that up to 5 sample rows will be sent to the LLM and get explicit approval before calling the tool.
- Consider scoping with
where_clauseto exclude sensitive customers/accounts first.
Default to profile mode whenever the table name suggests regulated data (customers, patients, orders, payments, accounts, users, etc.) unless the user explicitly requests row-level comparison.
CRITICAL: Always Start With a Plan
Before doing anything else, generate a numbered TODO list for the user:
Here's my plan:
1. [ ] List available warehouse connections
2. [ ] Inspect schema, discover primary key candidates, and detect auto-timestamp columns
3. [ ] Confirm primary keys with you
4. [ ] Confirm which auto-timestamp columns to exclude
5. [ ] Check row counts on both sides
6. [ ] Run column-level profile (cheap — no row scan)
7. [ ] Ask whether to proceed with row-level diff (may be expensive for large tables)
8. [ ] Run targeted row-level diff on diverging columns only
9. [ ] Present findings with scope, filters, time period, columns compared/excluded, and assumptions
Update each item to [x] as you complete it. This plan should be visible before any tool is called.
CRITICAL: Use data_diff Tool — Never Write Manual Diff SQL
NEVER write SQL to diff tables manually (e.g., EXCEPT, FULL OUTER JOIN, MINUS).
ALWAYS use the data_diff tool for any comparison operation.
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.
- 3d ago First seen · 493 lines · 48 tokens per session scan A 797a60c19051
data-parity is a skill published in the GitHub repository AltimateAI/altimate-code (803 stars, last pushed 3d ago), licensed MIT. It adds 48 tokens to every session and 4,920 once invoked, about $0.0002 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 2026-08-30.
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