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 skills add Hainrixz/claude-db --skill db-defaults-generatedgit clone --depth 1 https://github.com/Hainrixz/claude-dbWrote 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/hainrixz/claude-db/db-defaults-generated)<a href="https://agentmods.dev/skills/hainrixz/claude-db/db-defaults-generated"><img src="https://agentmods.dev/badge/skills/hainrixz/claude-db/db-defaults-generated.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 5.1 | $0.00073 | $0.01015 |
| Opus 5 | $0.00036 | $0.00508 |
| Sonnet 5 | $0.00015 | $0.00203 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
db-defaults-generated 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
db-defaults-generated (M6)
Defaults and generated columns are correctness placed at the source: a created_at DEFAULT now() is true for every writer, and a GENERATED ALWAYS AS column can never drift from its inputs. Pushing this logic into the app means each writer can get it wrong differently. This module is design-axis (Tipos category). It applies to engines supporting defaults/generated columns.
What it checks
- Timestamp defaults:
created_at/updated_atset only by application code (noDEFAULT now()/CURRENT_TIMESTAMP, noON UPDATE/trigger) — inconsistent across writers, missing on raw SQL inserts. - Derived value should be generated: a stored column computed from siblings (
full_name,total = qty*price,search_vector) kept in sync by app code rather thanGENERATED ALWAYS AS ... STORED— drift-prone (ties to M1 denormalization discipline). - Non-deterministic / wrong default: defaults that bake in a value that should be dynamic, or a default that masks a missing NOT NULL (e.g.
status DEFAULT 'active'hiding required intent), or aDEFAULT ''standing in for NULL. - Identity/sequence hygiene:
serialvsGENERATED ... AS IDENTITY; shared/incorrect sequence ownership. - Boolean/flag defaults missing, forcing three-valued logic where two was intended.
Axis & severity
- Axis: design; magnitude banded, never invented drift rates.
- Derived stored column maintained by app (drift risk): severity 3,
warn,fixable: proposed. - Missing
created_at/updated_atDB default: severity 2–3,warn,fixable: auto(additive default). DEFAULT ''/sentinel masking NULL semantics: severity 2,warn.- M6 holds no sev-5 cap; it shapes the Tipos category value.
Tier-0 static check
Parse DDL/snapshot via scripts/parse-schema.mjs: flag *_at timestamp columns with no DEFAULT; detect stored columns whose name implies derivation (full_name, total, *_count, search_vector) that are plain columns rather than GENERATED; list defaults that are empty-string/sentinel; note serial where identity is preferred. Program-source parses stay directional.
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.
- 7d ago First seen · 53 lines · 73 tokens per session scan A 4c05bc786505
db-defaults-generated is a skill published in the GitHub repository Hainrixz/claude-db (19 stars, last pushed 2mo ago), licensed MIT. It adds 73 tokens to every session and 1,015 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 2026-08-30.
Other skills, from other repositories
dynamodb
Use when modeling or operating a DynamoDB table: deriving partition/sort keys from access patterns, single-table vs table-per-entity, adding a GSI/LSI, on-demand vs provisioned capacity, or diagnosing hot-partition throttling. NOT relational schema/SQL/EXPLAIN (that is postgresdb), NOT aggregation-pipeline document…
malloy-lookml-review
Analyze LookML files as prior art for Malloy modeling. Used during Step 1 (DISCOVER) when .lkml files are present. Coordinates reference files that extract business logic, relationships, and curation decisions. Works with or without a database connection.
convex-migrations
Schema migration strategies for evolving applications including adding new fields, backfilling data, removing deprecated fields, index migrations, and zero-downtime migration patterns.
database-design-patterns
Database schema design patterns and optimization strategies for relational and NoSQL databases. Use when designing database schemas, optimizing query performance, or implementing data persistence layers at scale.
database-sql
Design database schemas, write efficient SQL queries, create migrations, and optimize database performance. Use when working with databases, writing queries, or designing data models.
data-design
Data modeling, schema design, and data architecture.