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 cosmix/loom --skill loom-database-designgit clone --depth 1 https://github.com/cosmix/loomWrote 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/cosmix/loom/loom-database-design)<a href="https://agentmods.dev/skills/cosmix/loom/loom-database-design"><img src="https://agentmods.dev/badge/skills/cosmix/loom/loom-database-design/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/cosmix/loom/loom-database-design"><img src="https://agentmods.dev/badge/skills/cosmix/loom/loom-database-design.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 | $0.00030 | $0.10122 |
| Opus 5.5 | $0.00012 | $0.04049 |
| Sonnet 5.5 | $0.00006 | $0.02024 |
| Haiku 4.5 | $0.00003 | $0.01012 |
Grade A, and why
loom-database-design 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 29d 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 — 745 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Design
Overview
Designing schemas and data models across workloads: OLTP (normalized relational, transactional integrity), OLAP (star/snowflake warehouses), NoSQL (document/KV/wide-column), time-series (TimescaleDB/InfluxDB), event sourcing (append-only stores), and ETL/pipeline staging. Most examples are PostgreSQL; principles generalize. The mechanism-level rules — keys, indexing, concurrency, lock-aware DDL, partitioning — live in Expert Practices below; this section is the design method.
Design Method
1. Requirements → model. Entities, attributes, relationships (1:1 / 1:N / M:N); access patterns (read vs write heavy, hot queries); volume, growth, retention; OLTP vs OLAP. The access pattern, not the entities, drives the physical design.
2. Schema per workload:
- OLTP: normalize to 3NF (one home per fact), then derive read models. Surrogate vs natural PK (see Keys). FK cascade rules. Correct types +
CHECKconstraints. Deliberate NULL semantics. - OLAP: star schema (fact + denormalized dimensions); snowflake only when a dimension's cardinality/reuse justifies normalizing it. Surrogate dimension keys. SCD Type 1 (overwrite) / Type 2 (row-versioned history) / Type 3 (prior-value column). Fact tables = FKs + additive measures + degenerate dims.
- Time-series: time as leading PK component; partition by time range; append-only writes; downsample into rollup/continuous-aggregate tables; retention policy that drops old partitions.
- Event sourcing: immutable append-only events (
aggregate_id,event_type,sequence_number,payload,occurred_at); optimistic concurrency viaUNIQUE(aggregate_id, sequence_number); projections as derived read models; version the payload for schema evolution; snapshots to bound replay cost.
3. Performance & concurrency, migrations, ETL: these are the highest-defect areas — apply the mechanism rules from Expert Practices: index every child FK column; INCLUDE covering indexes; partial-index literal-match limits; declarative partitioning (never inheritance+triggers); READ COMMITTED anomalies vs SERIALIZABLE+40001 retry; lock-aware DDL (lock_timeout, NOT VALID+VALIDATE, CREATE INDEX CONCURRENTLY); idempotent MERGE/ON CONFLICT upserts with staging tables and audit columns.
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.
- 29d ago Changed · +6 tokens per session 8303b773b1ba
- 1mo ago Changed · -33 tokens per session 9cab7a01df65
- 1mo ago First seen · 745 lines · 57 tokens per session scan A a2ba5fb35e68
loom-database-design is a skill published in the GitHub repository cosmix/loom (62 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 10,122 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 2026-08-30.
Other skills, from other repositories
domainmodel-dsl
Read, write, and modify Domain Model DSL files (.dmodel) — datatypes, entities with inheritance and multi-valued features, and nested packages with qualified cross-references. Use when the user wants to create, edit, explain, or debug a domain model in this DSL, including resolving references across packages.
domainmodel-dsl
Work with the Domain Model DSL (.dmodel) for describing data models — datatypes, entities, and their features. Use when the user wants to create or edit a domain model in this DSL.
basin
Build and troubleshoot Cloudflare Basin analytics workflows with Basin Pipelines, Basin Catalog, and Basin SQL. Use for streaming data into R2 Iceberg tables, managing catalogs, or querying those tables; also use for requests using the former Data Platform, Pipelines, R2 Data Catalog, or R2 SQL names.
k2
Build and troubleshoot Cloudflare K2 or K2 Streams durable logs. Use for stream setup, producing from Workers or HTTP, configuring retention and inputs, and consuming through subscriptions.
coverage-tracker
Run a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.
journalist-fit-check
Gate a pitch against one journalist at a time. Runs the pair through proven media-relations checks (last-10-bylines audit, 90-day topic sweep, beat-vs-angle-vs-one-off, the 5 forms of journalism, source-mirror, stated-preferences, database triangulation) and returns fit, soft-fit, no-fit, or unknown with a real recent…