What is db-partitioning-sharding?

A database-audit module for deciding whether large tables need partitioning or whether data should be split across database shards. Partitioning divides a table inside one database; sharding spreads data across multiple database parts.

Reviewing partition choices for event, log, and time-series tables, detecting skewed keys or hot partitions, and questioning premature application-level sharding.

It highlights both under-scaling, such as an oversized table, and over-scaling, such as adding shards without enough traffic or data to justify the complexity. It also checks for uneven or overloaded partitions.

db-partitioning-sharding — Skill for Claude Code by Hainrixz

Install

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.

Any agent

npx skills add Hainrixz/claude-db --skill db-partitioning-sharding

Clone the repo

git clone --depth 1 https://github.com/Hainrixz/claude-db

Made for: Claude Code.

Or install claude-db, the plugin that ships this one along with the rest of its 36 skills, 6 agents, 1 hook.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/hainrixz/claude-db/db-partitioning-sharding/github.svg)](https://agentmods.dev/skills/hainrixz/claude-db/db-partitioning-sharding)

Your own site

<a href="https://agentmods.dev/skills/hainrixz/claude-db/db-partitioning-sharding"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/db-partitioning-sharding/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/hainrixz/claude-db/db-partitioning-sharding"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/db-partitioning-sharding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,086 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00057	$0.01086
Opus 5	$0.00028	$0.00543
Sonnet 5	$0.00011	$0.00217
Haiku 4.5	$0.00006	$0.00109

Measured 10d ago against content hash a2a3944d57d4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

db-partitioning-sharding 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 10d 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.

skills/db-partitioning-sharding/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

db-partitioning-sharding (M16)

Scaling topology is Performance & Scale (axis performance); feeds relational Escala w12 (shared with M17/M2/M9) and the Shard-key / Partición&hot categories in NoSQL profiles. The two failure directions are symmetric: scaling too late (a monster table that should be partitioned) and scaling too early (sharding a 5 GB database that a single node handles trivially).

What it checks

Partitioning fit — large append-only / time-series tables (events, logs, metrics) that would benefit from Postgres declarative range/list partitioning (cheap pruning, fast retention drops) but are a single heap.
Hot partition / skewed key — a partition or shard key with low cardinality or temporal skew (e.g. partitioning by tenant_id where one tenant is 90% of traffic, or all writes hitting "today's" partition). On wide-column stores an unbounded/hot partition on an event table is severity:5 (perf) with live write-rate evidence — otherwise directional.
Premature sharding — application-level sharding / multiple shards introduced with no size or throughput justification, adding cross-shard-query and rebalancing cost for no benefit.

Score / axis

Feeds performance only (relational Escala w12; Shard-key document / Partición&hot KV+WC / Escala vector+graph).

Tier-0 (static)

Detect partitioning DDL (PARTITION BY, partition children), the chosen partition/shard key and its apparent cardinality, large-table candidates from naming/columns (timestamp + high insert intent), and shard-fanout code. Table size, row counts, and per-partition write rate are runtime → needs_api at Tier-0 (never a silent pass).

Tier-1/2 (verification query, Postgres)

-- partition inventory + sizes (Tier-1):
SELECT inhparent::regclass AS parent, inhrelid::regclass AS partition,
       pg_size_pretty(pg_total_relation_size(inhrelid)) AS sz
FROM pg_inherits ORDER BY pg_total_relation_size(inhrelid) DESC;
-- candidate (unpartitioned) large tables:
SELECT relname, pg_size_pretty(pg_total_relation_size(oid)) AS sz, reltuples::bigint AS est_rows
FROM pg_class WHERE relkind='r' ORDER BY pg_total_relation_size(oid) DESC LIMIT 20;

Method schema_introspect / query_stat. A large unpartitioned table confirms a partitioning-fit finding as established; per-partition skew needs Tier-2 write stats — without them, hot-partition is directional and never caps.

Read the full file on GitHub · 78 lines

Changes

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.

10d ago First seen · 78 lines · 57 tokens per session scan A a2a3944d57d4

Subscribe to this mod's changes

db-partitioning-sharding is a skill published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 1,086 once invoked, about $0.0003 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 2026-08-30.

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