Borrowing it
Nothing to install: this file belongs to NomaDamas/AutoRAG-Research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NomaDamas/AutoRAG-Research/main/.agents/skills/autorag-query/SKILL.mdgit clone --depth 1 https://github.com/NomaDamas/AutoRAG-ResearchWrote 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/nomadamas/autorag-research/autorag-query)<a href="https://agentmods.dev/skills/nomadamas/autorag-research/autorag-query"><img src="https://agentmods.dev/badge/skills/nomadamas/autorag-research/autorag-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/nomadamas/autorag-research/autorag-query"><img src="https://agentmods.dev/badge/skills/nomadamas/autorag-research/autorag-query.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.01699 |
| Opus 5.5 | $0.00023 | $0.00680 |
| Sonnet 5.5 | $0.00012 | $0.00340 |
| Haiku 4.5 | $0.00006 | $0.00170 |
Grade A, and why
autorag-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 2mo 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoRAG-Query: Text2SQL Agent Skill
Query AutoRAG pipeline results with natural language. Converts to SQL, executes safely, returns tables/JSON/CSV.
Quick Example
User: "Which pipeline has the best BLEU score?"
Agent:
- Read
references/schema.sql(understand tables) - Generate SQL:
SELECT p.name, s.metric_result FROM summary s JOIN pipeline p ON s.pipeline_id = p.id JOIN metric m ON s.metric_id = m.id WHERE m.name = 'bleu' ORDER BY s.metric_result DESC LIMIT 1; - Execute:
uv run python .agents/skills/autorag-query/scripts/query_executor.py --query "..." - Present: "hybrid_search_v2 has best BLEU: 0.85"
Workflow
- Parse intent: What data? (metrics/pipelines/queries) What operation? (rank/aggregate/filter)
- Load schema: Read
references/schema.sql- key tables:summary: Aggregated pipeline metrics (best for rankings)evaluation_result: Per-query scores (detailed analysis)executor_result: Generation outputs withtoken_usageJSONBchunk_retrieved_result: Retrieval scores/ranks
- Generate SQL following rules:
- ✅ SELECT-only, ⛔ Never: INSERT/UPDATE/DELETE/DROP/CREATE
- ⛔ Exclude vector columns:
embedding,embeddings,bm25_tokens(cause type errors) - Add
LIMIT 100if not specified - Use JOINs:
query_id → query.id,pipeline_id → pipeline.id,metric_id → metric.id - JSONB:
token_usage->>'field'(text) or(token_usage->>'field')::int(cast)
- Execute:
uv run python .agents/skills/autorag-query/scripts/query_executor.py --query "..." [--format json|csv|table] - Present: Summarize findings, show table, highlight insights
Key Tables
| Table | Purpose | Key Columns |
|---|---|---|
pipeline |
Pipeline definitions | id, name, pipeline_type |
metric |
Metric definitions | id, name, metric_type (retrieval/generation) |
query |
Search queries | id, query, ground_truths, dataset_name |
executor_result |
Generation outputs | query_id, pipeline_id, generation_result, token_usage (JSONB), execution_time |
evaluation_result |
Per-query scores | query_id, pipeline_id, metric_id, metric_result |
summary |
Aggregated metrics | pipeline_id, metric_id, metric_result |
chunk_retrieved_result |
Retrieval outputs | query_id, pipeline_id, chunk_id, score, rank |
What ships with it
3 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.
- 2mo ago First seen · 180 lines · 58 tokens per session scan A 4eadf55a0035
autorag-query is a skill published in the GitHub repository NomaDamas/AutoRAG-Research (151 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 58 tokens to every session and 1,699 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 2026-08-30.
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