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 agentmods add skills/genkovich/sdd/data-modelnpx skills add genkovich/sdd --skill data-modelgit clone --depth 1 https://github.com/genkovich/sddWhat 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 | $0.00233 | $0.03925 |
| Opus 5 | $0.00117 | $0.01962 |
| Sonnet 5 | $0.00047 | $0.00785 |
| Haiku 4.5 | $0.00023 | $0.00392 |
Grade A, and why
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 2d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: data-model
End-to-end runner for the persistence cut: data model + migrations + drift check in one pass. Greenfield-first by default; brownfield delta as --mode brownfield. Output is shippable — full .up.sql + .down.sql, not a plan — but staged under docs/features/<slug>/migrations/, never written into the live migrations/ tree. implement promotes the staged pair into migrations/ (with the real sequence number / timestamp) only when the feature is actually being built. This is deliberate: data-model is a design stage four steps before implement, so a stray migrate up (a teammate's loop, CI, a deploy) must not be able to apply a half-designed schema to a real database. (Same staging discipline the drift fixes already use under _drift/.)
Stack-agnostic by design — it imposes no DB philosophy and writes no rules file. data-model derives the DB + migration conventions from the architecture — architecture-map.md (the migration tool/naming survey recorded) + the sad.md persistence decisions (§4 strategy / §5 building blocks / §8 crosscutting) + the Accepted ADRs — and follows them; the live migrations/ + schema corroborate and fill anything the architecture left implicit. On a greenfield repo with no architecture signal, it confirms each schema choice with the user (Socratic) instead of defaulting to a house style. What it applies regardless of stack is migration safety (staging, reversibility, FK indexes, zero-downtime decomposition, no-PII) — never a stance on updated_at vs not, hard vs soft delete, UUID vs sequence, or whether CHECK constraints are allowed. The size matrix (→ ../_shared/size-matrix.md) governs how much you produce; the aggregate-roots dialogue uses ../_shared/ask-style.md.
data-model.md prose follows artifact_language — SQL, table/column identifiers, headings and frontmatter stay English → ../_shared/artifact-language.md.
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.
- 2d ago First seen · 105 lines · 233 tokens per session scan A 372dee7350bc
data-model is a skill published in the GitHub repository genkovich/sdd (118 stars, last pushed 13d ago), licensed MIT. It adds 233 tokens to every session and 3,925 once invoked, about $0.0012 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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