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 runxhq/runx --skill sql-analysisgit clone --depth 1 https://github.com/runxhq/runxWrote 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/runxhq/runx/sql-analysis)<a href="https://agentmods.dev/skills/runxhq/runx/sql-analysis"><img src="https://agentmods.dev/badge/skills/runxhq/runx/sql-analysis/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/runxhq/runx/sql-analysis"><img src="https://agentmods.dev/badge/skills/runxhq/runx/sql-analysis.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 | $0.00043 | $0.01329 |
| Opus 5.5 | $0.00017 | $0.00532 |
| Sonnet 5.5 | $0.00009 | $0.00266 |
| Haiku 4.5 | $0.00004 | $0.00133 |
Grade A, and why
sql-analysis 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Analysis
Use SQL Analysis for recurring questions over a configured database: which campaigns produced qualified trials, how support resolution changed, or where activation stalls. It chooses a relevant declared query, checks its parameters, runs that read through the selected SQL connector and answers from the returned rows. The result carries the exact native provider operation, not a model's claim that it queried a database.
The operator owns the database and its useful reads. The optional sql
connector uses installed SQLAlchemy dialects and DBAPI drivers; the skill and
CLI have no fixed database-engine list. Configure another source instance to
change engine, credentials, query definitions or permissions. The connector's
README explains dependency installation and source configuration.
This is not an arbitrary SQL console. Query definitions are operator-owned SQL with named bound parameters and complete JSON Schema parameter contracts. The agent sees descriptions, input contracts and result semantics; it cannot inject a URL, replace SQL, install a driver or broaden permissions. If no declared read answers the question, the skill explains what is missing. An operator can add an appropriate read at the source and run again. Use a separate authorized database session for exploratory SQL, writes or schema changes.
How a run works
The default analyst runner takes question, the exact logical source_ref,
and an optional max_rows ceiling, then:
- Reads the selected source's query catalog without opening a database.
- Chooses the smallest relevant query and supplies values from the question or caller context. Missing dates, definitions or filters are reasons to stop.
- Checks the complete parameter contract using native
data.validate. The connector independently validates before executing. - Executes only that query, within both caller and source row limits.
- Interprets actual rows and retains the native operation evidence, source, query, parameter values, observation time and truncation state.
What ships with it
12 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.
- fixtures/composed-plan.yaml 1.0 KB
- fixtures/default-reads-selected-query.yaml 3.4 KB
- fixtures/default-requires-source-binding.yaml 659 B
- fixtures/default-stops-invalid-parameters.yaml 1.9 KB
- fixtures/default-stops-unsupported-question.yaml 1.8 KB
- fixtures/refuses-unsafe-integer.yaml 830 B
- fixtures/support/composed-plan.graph 862 B
- mark.svg 395 B
- references/catalog.md 132 B
- references/mark-license.md 3.1 KB
- run.yaml 19 KB
- sql-analysis.mjs 2.5 KB runs code
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 · 121 lines · 43 tokens per session scan A adca4f40cab2
sql-analysis is a skill published in the GitHub repository runxhq/runx (103 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 1,329 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-18.
Other skills, from other repositories
dd-report-format
Standard output format for due diligence analysis reports. Use when producing your final analysis to ensure consistency across all specialist agents.
dd-risk-scoring
Calculate weighted risk scores from specialist agent reports. Use when synthesizing multiple due diligence reports into an overall investment risk assessment.
dd-synthesis
Synthesize findings from multiple specialist agents into a coherent investment recommendation. Use when combining reports from 9+ specialist agents into a single decision document.
dd-sourcing
Document sources properly for due diligence reports. Use when citing any external data, ensuring full traceability and auditability of findings.
dd-verification
Verify claims in pitch decks and founder statements using multiple independent sources. Use when analyzing any claim about market size, traction, team background, or competitive positioning.
skill-db
Database audit: schema quality, index coverage, row-level access-control completeness, FK cascades, query patterns. Runs live SQL verification (PostgreSQL instance in PATTERNS.md; other engines verify the equivalent guard). Migration file safety → /migration-audit.