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 debabsah/analytics-office --skill review-my-querygit clone --depth 1 https://github.com/debabsah/analytics-officeWrote 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/debabsah/analytics-office/review-my-query)<a href="https://agentmods.dev/skills/debabsah/analytics-office/review-my-query"><img src="https://agentmods.dev/badge/skills/debabsah/analytics-office/review-my-query/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/debabsah/analytics-office/review-my-query"><img src="https://agentmods.dev/badge/skills/debabsah/analytics-office/review-my-query.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 | $0.00236 | $0.03109 |
| Opus 5 | $0.00118 | $0.01554 |
| Sonnet 5 | $0.00047 | $0.00622 |
| Haiku 4.5 | $0.00024 | $0.00311 |
Grade A, and why
review-my-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 9d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
review-my-query
The colleague who reads your query before it ships: checks it against the definition you locked, hunts the bugs that quietly ship the wrong number, and hands you the findings, not a rewrite.
When to use
Fire when there's a piece of analytics code in hand — a SQL query/view/proc, a dbt or semantic model, a measure, calc group, or RLS rule — and the question is "is this right / will this give the right number" before it ships or gets defended. Triggers: "review my query", "check this SQL/proc/measure", "does this match the definition", "what's wrong with this", "why don't these two numbers match" (with the code in hand).
Do NOT fire to orient on an unfamiliar estate (that's groundwork), to pin a metric's definition (kpi-contract), or to rehearse defending a finished number (defend-my-number). This reviews the code that computes a number; it does not orient, define, or rehearse.
This vs. its neighbors: fire when the code is suspect and the definition is trusted — check the query/model against the locked contract. If the definition itself may be stale or wrong about the world (the code may be flawless), that's audit-my-assumptions.
The trap this exists to beat
Asked to "review this query," a capable assistant does the analytical part well: it spots the bugs and even checks the code against the contract fork by fork. Then it does the wrong thing with what it found. Its instinct is to rewrite the query for you — hand back a corrected CREATE VIEW, sometimes two versions — built on column and table names it guessed because it never saw the schema. That is a different job. It authors your production artifact (over the read-only line), does the work that's yours, and teaches you nothing about where the number was wrong. This skill reviews: it locates the defect, names the failure mode, ties it to the definition it breaks, grades it by whether it ships a wrong number, and points the fix direction — then leaves a committable review. It does not rewrite your query, and it does not invent the schema to do so. A second, quieter failure it avoids: over-blocking — grading every schema-uncertainty as Blocking to look thorough, so a conformant query comes back as a wall of red that trains the room to ignore your Blockings. It grades by what is established, reserves Blocking for a wrong number it can actually show, and lets clean code come back clean.
What ships with it
2 files 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.
- 9d ago First seen · 78 lines · 0 tokens per session scan A d9dea3839e10
review-my-query is a skill published in the GitHub repository debabsah/analytics-office (9 stars, last pushed 3mo ago), licensed MIT. It adds 236 tokens to every session and 3,109 once invoked, about $0.0012 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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