Rill is a business intelligence tool for turning data sources into SQL models, metrics, dashboards, and natural-language analysis. It is used by business users and coding agents, with OLAP engines such as ClickHouse and DuckDB supporting its queries. Its catalogue add-ons support agent-driven work on Rill projects.
Borrowing it
Nothing to install: this file belongs to rilldata/rill. 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/rilldata/rill/main/.claude/skills/re-review/SKILL.mdgit clone --depth 1 https://github.com/rilldata/rillWrote 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/rilldata/rill/re-review)<a href="https://agentmods.dev/skills/rilldata/rill/re-review"><img src="https://agentmods.dev/badge/skills/rilldata/rill/re-review/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/rilldata/rill/re-review"><img src="https://agentmods.dev/badge/skills/rilldata/rill/re-review.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 | $0.00015 | $0.00243 |
| Opus 5.5 | $0.00006 | $0.00097 |
| Sonnet 5.5 | $0.00003 | $0.00049 |
| Haiku 4.5 | $0.00002 | $0.00024 |
Grade A, and why
re-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 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.
What it actually says
Re-review a PR that has been updated since a previous review. Unlike a fresh review, focus on whether prior feedback was addressed and flag anything new.
Additional instructions: $ARGUMENTS
Instructions
- Get PR details using
gh pr viewand fetch all previous review comments usinggh api repos/{owner}/{repo}/pulls/{number}/commentsandgh api repos/{owner}/{repo}/pulls/{number}/reviews - For each prior comment, determine if it was addressed, partially addressed, or still open
- Review the current diff, focusing on changes since the last review
- Present findings in two parts:
- Internal analysis (not for posting): table of prior comments and their resolution status
- Draft review (for posting): only unresolved items from prior feedback and newly discovered issues. Do not rehash what the author already addressed.
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 · 19 lines · 15 tokens per session scan A dd5048de6c34
re-review is a skill published in the GitHub repository rilldata/rill (2,970 stars, last pushed yesterday), licensed Apache-2.0. It adds 15 tokens to every session and 243 once invoked, about $0.0001 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.
Other skills, from other repositories
hardwood-cli
Inspect, debug, and convert Apache Parquet files with the hardwood CLI. Use whenever a user is debugging code that reads or writes Parquet (Arrow / Spark / Flink / Java / Python) and the data file itself may be the cause — to check the schema, physical and logical types, repetition, or a row sample. Use it to diagnose…
analytical-databases-expert
Design and query columnar analytical stores: DuckDB, ClickHouse and cloud warehouses, including file formats, partitioning, sort keys and cost control. Use when the user mentions DuckDB, ClickHouse, Parquet, columnar storage, OLAP, a data warehouse or lakehouse, analytical queries over large tables, or when the task…
review-respond
Respond to all pending review comments on the current PR — fetch comments, apply fixes, verify accuracy, test, commit, and reply. Use when addressing Copilot reviews, GitHub PR reviews, or any batch of review feedback.
exasol-import-export
Exasol IMPORT and EXPORT SQL statements: syntax, file formats (CSV, FBV, Parquet), cloud storage (S3, Azure, GCS), connection objects, error handling, and ETL staging patterns.
duckdb
Use when analytical SQL must run in-process with no server: Parquet/CSV/JSON/Arrow queried in place, OLAP embedded in an app or notebook, a slow pandas groupby on multi-GB data, or S3/lakehouse data read without downloading. NOT a multi-user analytics server (that is clickhouse-analytics), NOT an app's transactional…
duckdb
Run embedded OLAP SQL on Parquet/CSV locally; configure memory and thread limits; attach remote files safely; export with COPY; avoid multi-writer locks and treating DuckDB as a production server database.