chDB is an in-process OLAP SQL engine powered by ClickHouse, allowing applications to query analytical data without running a separate database server. Python users and data practitioners use it to work with formats such as Parquet, CSV, JSON, Arrow, and ORC through SQL or a pandas-compatible API. Its catalogue add-ons provide agent instructions, rules, skills, and an agent for working with the project.
Borrowing it
Nothing to install: this file belongs to chdb-io/chdb. 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/chdb-io/chdb/main/.agents/skills/pr-description/SKILL.mdgit clone --depth 1 https://github.com/chdb-io/chdbWrote 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/chdb-io/chdb/pr-description)<a href="https://agentmods.dev/skills/chdb-io/chdb/pr-description"><img src="https://agentmods.dev/badge/skills/chdb-io/chdb/pr-description/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/chdb-io/chdb/pr-description"><img src="https://agentmods.dev/badge/skills/chdb-io/chdb/pr-description.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 | $0.00029 | $0.00177 |
| Opus 5.5 | $0.00012 | $0.00071 |
| Sonnet 5.5 | $0.00006 | $0.00035 |
| Haiku 4.5 | $0.00003 | $0.00018 |
Grade A, and why
pr-description 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 1mo 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
Pull Request Description
Inspect the diff against the base branch and the included commits before writing.
Title
- Use a concise, specific, capitalized imperative.
- Describe the outcome rather than the implementation mechanism.
- Do not use Conventional Commit prefixes such as
feat:orfix:.
Body
- Explain what changed, why it changed, and its impact.
- Link related issues when they are known.
- List only validation that was actually run, including the result.
- Mention breaking changes, limitations, or important reviewer context when applicable.
Do not invent behavior, test results, or issue links. Create or edit a remote pull request only when the user explicitly asks.
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.
- 1mo ago First seen · 27 lines · 29 tokens per session scan A 0209ae3e9d71
pr-description is a skill published in the GitHub repository chdb-io/chdb (2,921 stars, last pushed 16d ago), licensed Apache-2.0. It adds 29 tokens to every session and 177 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 2026-09-29.
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Produces canonical review actions from fetched review state and renders action markdown. Use when the user wants only triage/action-planning for the review-framework workflow.
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Skill for searching and evolving the SQLite-backed Knowledge Graph. Use this when you need structured fact/concept/link search across one or more teams, NPCs, or directory scopes. The Knowledge Graph (KG) is stored in the application's database (not YAML). It is scoped by (teamname, npcname, directorypath). Facts and…