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/sql-reviewnpx skills add AltimateAI/altimate-code --skill sql-reviewgit 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 | $0.00041 | $0.00944 |
| Opus 5 | $0.00020 | $0.00472 |
| Sonnet 5 | $0.00008 | $0.00189 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
sql-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Review
Requirements
Agent: any (read-only analysis) Tools used: altimate_core_check, altimate_core_grade, sql_analyze, read, glob, bash (for git operations)
When to Use This Skill
Use when the user wants to:
- Review SQL quality before merging a PR
- Get a quality grade (A-F) on a query or model
- Run a comprehensive lint + safety + syntax check in one pass
- Audit SQL files in a directory for anti-patterns
Do NOT use for:
- Optimizing query performance -> use
query-optimize - Fixing broken SQL -> use
dbt-troubleshoot - Translating between dialects -> use
sql-translate
Workflow
1. Collect SQL to Review
Either:
- Read SQL from a file path provided by the user
- Accept SQL directly from the conversation
- Auto-detect changed SQL files from git:
git diff --name-only HEAD~1 | grep '\.sql$'
For dbt models, compile first to get the full SQL:
altimate-dbt compile --model <name>
2. Run Comprehensive Check
Call altimate_core_check — this is the single-call code review that composes:
- Syntax validation: Parse errors with line/column positions
- Lint (26 anti-patterns): SELECT *, unused CTEs, implicit casts, NULL comparisons, missing WHERE on DELETE/UPDATE, cartesian joins, non-sargable predicates, missing partition filters, and more
- Injection scan: Tautology attacks, UNION injection, stacked queries, comment injection, Jinja template injection
- PII exposure: Flags queries accessing columns classified as PII
altimate_core_check(sql: <sql>, schema_context: <schema_object>)
3. Grade the SQL
Call altimate_core_grade to get an A-F quality score with per-category breakdown:
altimate_core_grade(sql: <sql>, schema_context: <schema_object>)
Categories scored:
- Readability: Naming, formatting, CTE structure
- Performance: Anti-patterns, index usage, scan efficiency
- Correctness: NULL handling, join logic, type safety
- Best Practices: Explicit columns, proper materialization hints
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 · 119 lines · 41 tokens per session scan A 9de026d5636e
sql-review is a skill published in the GitHub repository AltimateAI/altimate-code (803 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 944 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.
Other skills, from other repositories
review-prs
Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
stale-sweep
Sweep the googleapis/mcp-toolbox repo for issues and PRs with no real activity in N days (default 60), sort each by whose silence it is (the author's, ours, or nobody's), and draft the nudge or close comment. Use whenever a maintainer asks for a stale sweep, backlog cleanup, or an SLO check, e.g. "stale sweep", "find…
triage-issues
Triage GitHub issues in the googleapis/mcp-toolbox repo: propose the correct labels (type / priority / product / status), check for duplicates, verify a bug has enough info to act on, and draft a triage comment. Use whenever a maintainer asks you to triage, label, categorize, prioritize, or "look at" an issue (or a…
data-divergence
Investigate why two datasets that should agree don't — two pipelines writing the same logical table, a rollup vs the detail it aggregates, a dashboard vs its source, one environment vs another. Use when row counts, totals, or date ranges disagree and the question is what happened rather than just what differs. Covers…
sql-diagram
Diagram a SQL query and explain what it shows — either its execution steps (mode=plan) or its column lineage (mode=lineage) — then trace it through small data so the defects the picture cannot show become visible. Use when asked to visualize, diagram, explain or review what a query does, how it joins its tables, or…