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 Aysnc-Labs/angora --skill angora-schemagit clone --depth 1 https://github.com/Aysnc-Labs/angoraWrote 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/aysnc-labs/angora/angora-schema)<a href="https://agentmods.dev/skills/aysnc-labs/angora/angora-schema"><img src="https://agentmods.dev/badge/skills/aysnc-labs/angora/angora-schema/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/aysnc-labs/angora/angora-schema"><img src="https://agentmods.dev/badge/skills/aysnc-labs/angora/angora-schema.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00161 | $0.01090 |
| Opus 5 | $0.00081 | $0.00545 |
| Sonnet 5 | $0.00032 | $0.00218 |
| Haiku 4.5 | $0.00016 | $0.00109 |
Grade A, and why
angora-schema 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 9d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Schema: $ARGUMENTS
Before you start
- Read
src/system.md— understand the site's intent and scope. - Read schema — glob
src/data/schema/tables/*.tsto discover tables, then read the relevant files. One file per table.
Analysis phase (always happens)
Whether the user gives you structured data or a vague description, always analyze first. Ask yourself:
- "Six months from now, what will they wish they had modeled?"
- Arrays of objects → child tables
- Repeated file references → media references (FK to
media) - Many-to-many relationships → junction tables
- Enum-like values → CHECK constraints or lookup tables
- Is this content that drives pages? → needs SEO + status fields
Ask the user probing questions. Don't just accept the first description — refine it.
Schema conventions
Every table gets:
id INTEGER PRIMARY KEY AUTOINCREMENTcreated_at TEXT NOT NULL DEFAULT (datetime('now'))- Foreign keys where appropriate
Page-driving tables
When records will get their own pages (blog posts, case studies, team members, cities), add:
slug TEXT NOT NULL UNIQUE— URL-safe identifiermeta_title TEXT— for<title>tagmeta_description TEXT— for<meta name="description">and OG tagsstatus TEXT NOT NULL DEFAULT 'draft'withCHECK (status IN ('draft', 'published'))— enables content staging, only published records render
Explain to the user why these fields matter (SEO, content staging, clean URLs).
Media references
Use media_id INTEGER REFERENCES media(id) for a primary image/asset. For multiple images, consider a junction table.
Presentation before execution
Before changing any files:
- Show the Drizzle table definition — the TypeScript that will go in its own file under
schema/ - Show example data — 2-3 realistic rows per table
- Show example queries — Drizzle queries page templates will need (e.g.,
db.select().from(testimonials).where(eq(testimonials.status, 'published'))) - Wait for user approval — all schema changes require explicit approval
What ships with it
1 file 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.
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.
- 9d ago First seen · 113 lines · 161 tokens per session scan A c2626658a5c9
angora-schema is a skill published in the GitHub repository Aysnc-Labs/angora (6 stars, last pushed 4mo ago), licensed MIT. It adds 161 tokens to every session and 1,090 once invoked, about $0.0008 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 2026-08-31.
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