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/address-feedback/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/address-feedback)<a href="https://agentmods.dev/skills/rilldata/rill/address-feedback"><img src="https://agentmods.dev/badge/skills/rilldata/rill/address-feedback/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/address-feedback"><img src="https://agentmods.dev/badge/skills/rilldata/rill/address-feedback.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 | $0.00009 | $0.00310 |
| Opus 5.5 | $0.00004 | $0.00124 |
| Sonnet 5.5 | $0.00002 | $0.00062 |
| Haiku 4.5 | $0.00001 | $0.00031 |
Grade A, and why
address-feedback 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
Fetch PR review feedback, then walk through each item interactively so the author can decide how to address it.
Input: $ARGUMENTS
Instructions
1. Fetch Feedback
- Get PR details using
gh pr view <pr-number> - Fetch review comments using
gh api repos/{owner}/{repo}/pulls/{number}/comments(inline) andgh api repos/{owner}/{repo}/pulls/{number}/reviews(summaries) - If a reviewer name was provided, filter to only their comments
- Read the relevant code for each comment
2. Assess
Summarize: what are the key themes, which items are quick wins, and which might warrant pushback?
3. Walk Through Each Item
Go through items in order of significance. For each:
- Quote the feedback and show the relevant code
- Offer 2-4 approaches via
AskUserQuestion, including "push back" when appropriate - Execute the chosen approach — make the change, or draft a response
- Move to the next item
4. Wrap Up
Summarize changes made, any drafted responses, and suggest a commit message.
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 · 36 lines · 9 tokens per session scan A e5c3d40261a3
address-feedback is a skill published in the GitHub repository rilldata/rill (2,970 stars, last pushed yesterday), licensed Apache-2.0. It adds 9 tokens to every session and 310 once invoked, about $0.0000 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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