Apache ShardingSphere is a database enhancement layer that makes heterogeneous databases easier to access and govern as a unified system, without replacing the underlying databases. It supports distributed database capabilities such as sharding, read-write splitting, SQL federation, encryption, masking, auditing, and traffic control for enterprise data architectures.
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 agentmods add skills/apache/shardingsphere/review-prnpx skills add apache/shardingsphere --skill review-prgit clone --depth 1 https://github.com/apache/shardingsphereWrote 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/apache/shardingsphere/review-pr)<a href="https://agentmods.dev/skills/apache/shardingsphere/review-pr"><img src="https://agentmods.dev/badge/skills/apache/shardingsphere/review-pr.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.04447 |
| Opus 5 | $0.00039 | $0.02224 |
| Sonnet 5 | $0.00015 | $0.00889 |
| Haiku 4.5 | $0.00008 | $0.00445 |
Grade A, and why
review-pr 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 yesterday.
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 — 335 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review PR
Purpose and Modes
Judge the latest reviewed scope from root cause, behavior, contracts, tests, and public or user-authorized repository evidence. Select one output mode:
Formal Review Mode: return one formal result for a public PR review, authorized local-candidate review, code-readiness judgment, mergeability decision, or CI review.PR Discussion Reply Mode: return a copy-ready committer reply only when the user explicitly requests a review-thread response, author or maintainer objection reply, or challenged-finding reply. Do not add a formal verdict unless requested.
Use Formal Review Mode for every complete code review result or recommendation, including pre-handoff review of a local candidate.
Identify a local candidate and its local-only delta in ### Coverage; do not create a separate local-preflight result format or describe local-only work as the public PR state.
Review Focus
Review focus is independent from output mode.
| Focus | Use when | CI behavior |
|---|---|---|
Code Correctness Review |
Default review of code, tests, behavior, scope, or regression risk | Do not query, wait for, or report GitHub Actions, checks, workflow runs, or Actions logs |
Mergeability Review |
The user asks whether the PR can be merged, approved, or landed | Review code and required CI or checks |
CI Review |
The user asks about checks, Actions, logs, or CI failures | Treat CI evidence as the primary target |
Explicit user scope wins. Formal Review of a local candidate uses Code Correctness Review unless the user explicitly requests CI.
In Code Correctness Review, unreviewed CI is not an evidence gap. Runtime facts may still be required from code, official specifications, public reproductions, or local verification. If such a decisive fact is unavailable, identify that fact—not CI—as the incomplete reason.
Canonical Assessment
Resolve one review basis before discovery: the effective candidate, applicable requirements, selected review focus, and admissible evidence. Run the Review Workflow against that basis and produce one mode-independent assessment: confirmed findings consolidated by fix boundary, needs-discussion conditions, incomplete-evidence gaps, and Completion Gate state.
What ships with it
11 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.
- agents/openai.yaml 1.1 KB
- references/evidence-access.md 6.6 KB
- references/high-risk-review.md 6.1 KB
- references/review-corrections.md 4.0 KB
- references/sql-parser-review.md 3.0 KB
- scripts/build_review_inventory.py 7.8 KB runs code
- scripts/review_common.py 5.8 KB runs code
- scripts/review_ledger.py 24 KB runs code
- tests/test_build_review_inventory.py 4.6 KB runs code
- tests/test_review_common.py 2.7 KB runs code
- tests/test_review_ledger.py 12 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.
- yesterday First seen · 335 lines · 77 tokens per session scan A b9a7390e72a6
review-pr is a skill published in the GitHub repository apache/shardingsphere (20,792 stars, last pushed yesterday), licensed Apache-2.0. It adds 77 tokens to every session and 4,447 once invoked, about $0.0004 per session on Opus 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 2026-09-03.
Other skills, from other repositories
drt-analyze
Analyze DRT cluster health for a given time range. Reconstructs the operations timeline, checks CockroachDB metrics (availability, latency, storage, changefeeds, jobs, goroutines, admission control, LSM, KV prober) and logs for anomalies, correlates findings with disruptive operations to distinguish expected…
redux-to-swr
Migrate React components from Redux + Saga to SWR hooks. Use when converting data fetching from Redux store (reducers, sagas, selectors, connect HOC) to SWR-based hooks in CockroachDB DB Console or cluster-ui.
reduce-unoptimized-query-oracle
Reduce an unoptimized-query-oracle test failure log to the simplest possible reproduction case. Use when you have unoptimized-query-oracle.log files from a failed roachtest and need to find the minimal SQL to reproduce the bug.
mma-investigator
Expert system for investigating MMA (Multi-Metric Allocator) behavior on CockroachDB clusters. Helps oncall engineers diagnose load imbalances, understand rebalancing decisions, and identify why MMA did or didn't act.
review-crdb
Review code changes or PRs for quality, correctness, and reviewability. Use when asked to "review", "check", "provide feedback", or "post a review". Dispatches specialized agents in parallel for thorough analysis.
system-table-change
Use when adding, removing, or modifying columns/indexes on system tables. Provides a checklist covering schema definitions, migrations, version gates, golden files, and test hashes.