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 rules/rajitsaha/100xprism/data-querygit clone --depth 1 https://github.com/rajitsaha/100xprismWrote 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/rules/rajitsaha/100xprism/data-query)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/data-query"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/data-query.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.00407 |
| Opus 5 | $0.00017 | $0.00204 |
| Sonnet 5 | $0.00007 | $0.00081 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
data-query scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Suggested model tier: balanced (moderate reasoning)
Query — Plain-English Database Analytics
Describe what you want to know — Claude writes and runs the query.
How to use
/query <business question>— Claude translates to SQL, runs it, returns analysis
Supported interfaces
- BigQuery:
bqCLI —bq query,bq show,bq ls - PostgreSQL:
psqlCLI - MySQL:
mysqlCLI - SQLite:
sqlite3CLI
Phase 1 — Understand the question
Restate the business question in concrete terms. If the schema is ambiguous, inspect it first:
# BigQuery — show table schema
bq show <dataset>.<table>
# PostgreSQL
PGPASSWORD="$DB_PASS" psql -h "$DB_HOST" -U "$DB_USER" -d "$DB_NAME" \
-c "\d <table_name>"
Phase 2 — Write and run the query
Write the SQL for the business question. Run it using the appropriate CLI. Explain the results in plain English.
Phase 3 — Return results
Return:
- The SQL written
- A plain-English answer to the original question
- A table of key numbers
For recurring queries: offer to save as a named script.
Scope vs /db
/query= analytics in plain English (you describe a business question)/db= execute specific SQL against a named connection (you write the SQL or say "migrate")
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.
- yesterday First seen · 58 lines · 34 tokens per session scan A e8490c9d7f07
data-query is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 4d ago), licensed MIT. It adds 34 tokens to every session and 407 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 2026-09-03.
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