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 LerianStudio/ring --skill designing-data-modelgit clone --depth 1 https://github.com/LerianStudio/ringWrote 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/lerianstudio/ring/designing-data-model)<a href="https://agentmods.dev/skills/lerianstudio/ring/designing-data-model"><img src="https://agentmods.dev/badge/skills/lerianstudio/ring/designing-data-model/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/lerianstudio/ring/designing-data-model"><img src="https://agentmods.dev/badge/skills/lerianstudio/ring/designing-data-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 78 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00120 | $0.01598 |
| Opus 5.5 | $0.00048 | $0.00639 |
| Sonnet 5.5 | $0.00024 | $0.00320 |
| Haiku 4.5 | $0.00012 | $0.00160 |
Grade A, and why
ring:designing-data-model 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Modeling — Producing the Stack-Native Schema
When to use
- OpenAPI spec passed Gate 4 validation (or Gate 4 SKIPPED for features with no API surface)
- System stores persistent data
- Large Track workflow (2+ day features)
Skip when
- Small Track workflow → skip to ring:writing-plans
- No persistent data → skip to Dependency Map
- OpenAPI spec not validated (and Gate 4 not SKIPPED) → complete Gate 4 first
Sequence
Runs before: ring:pinning-dependency-versions Runs after: ring:designing-api-contracts
The deliverable is a REAL, stack-native schema file — DDL that becomes migrations, not markdown tables. The DDL IS the deliverable: CREATE TABLE, indexes, and constraints are required, not forbidden.
Phase 0: Database Field Naming Strategy (MANDATORY)
See shared-patterns/standards-discovery.md for the complete workflow.
Step 1: Check for Gate 4 API standards
If docs/pre-dev/{feature}/api-standards-ref.md exists, auto-detect naming convention.
Step 2: Ask user
If api-standards-ref.md EXISTS: AskUserQuestion: "How should database fields be named?"
- "Convert to snake_case (Recommended)" — API: userId → DB: user_id
- "Keep same as API (camelCase)" — API: userId → DB: userId
- "Different standards — provide DB dictionary"
- "Define manually"
If api-standards-ref.md DOES NOT EXIST: AskUserQuestion: "How should database fields be named?"
- "Use snake_case (Recommended)" — standard for PostgreSQL/MySQL
- "Use camelCase" — standard for MongoDB/document DBs
- "Load from standards document"
- "Define manually"
Step 3: Generate db-standards-ref.md
Option: Convert to snake_case — apply automatic rules:
- userId → user_id, createdAt → created_at, isActive → is_active, phoneNumber → phone_number, userID → user_id
Option: Keep same — copy field names without modification.
Option: Load from doc — WebFetch or read, extract field definitions, save to db-standards-ref.md.
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 · 132 lines · 120 tokens per session scan A f69d263794e0
ring:designing-data-model is a skill published in the GitHub repository LerianStudio/ring (218 stars, last pushed 4d ago), licensed Apache-2.0. It adds 120 tokens to every session and 1,598 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
db-audit
Database performance and safety audit. 70+ checks across 13 dimensions (DB1-DB13): query patterns, indexes, schema design, connections, transactions, migrations, caching, query optimization, ORM anti-patterns, observability, data lifecycle, DB security, and migration deployment safety. Code-level checks for all ORMs.…
performance-audit
Full-stack performance health check across 12 dimensions. Rendering, bundles, assets, API/network, algorithms, memory, database, caching, Web Vitals, backend runtime, concurrency, and framework-specific pathologies. Evidence-based Impact Models with confidence tiers and a prioritized optimization roadmap. Switches…
antidote
Stop AI agents from patching symptoms. Use this skill when coding, reviewing, refactoring, auditing, committing fixes, or adding guards, validation, workarounds, or 20+ lines without deleting code.
review
Structured code review with parallel audit agents, confidence-scored triage, and optional auto-fix. Examines uncommitted changes, staged diffs, commit ranges, or specific paths. Produces a tiered report (MUST-FIX / RECOMMENDED / NIT) backed by evidence, then optionally applies fixes with verification.
code-audit
Batch audit of production files against CQ1-CQ40 quality gates and CAP1-CAP29 anti-patterns. Tiered output (A/B/C/D), critical gate enforcement, evidence-backed scoring, cross-file pattern analysis, and prioritized execution plan. Flags: zuvo:code-audit all | [path] | [file] | --deep | --quick | --services |…
plan
Analyzes architecture, selects patterns, assesses testability, then decomposes work into ordered TDD tasks with exact verification commands and explicit acceptance mapping. Works from an approved spec (zuvo:brainstorm output) or directly from a user-provided description.