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/nullhack/temple8/model-data-schemanpx skills add nullhack/temple8 --skill model-data-schemagit clone --depth 1 https://github.com/nullhack/temple8What 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 | $0.00028 | $0.00748 |
| Opus 5 | $0.00014 | $0.00374 |
| Sonnet 5 | $0.00006 | $0.00150 |
| Haiku 4.5 | $0.00003 | $0.00075 |
Grade A, and why
model-data-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 yesterday.
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.
What it actually says
Model Data Schema
- Load [[architecture/data-modeling]], [[software-craft/external-fixtures]] — the schema-as-contract rule, OLTP/OLAP selection, workload-driven normalization, and the captured external shapes that bound the model.
- Bootstrap the journal. IF
.cache/<session_id>/journal.mddoes not exist THEN render it from.templates/cache/journal.md.template(substitute<session_id>). The journal is the carry-over artifact for build-phase escalations, capture gaps, and the simulation walk; empty until those states write to it. Absence on a first pass is expected — never a deliberation point; the first pass IS the empty-journal pass. IF it exists THEN it carries build-phase escalations from the prior build cycle — read them and model the gaps it names first. - Read
.cache/<session_id>/external-contracts.md,tests/cassettes/**,.cache/<session_id>/interview-notes.md,docs/glossary.md. - Classify the workload before naming a table. State, with cited evidence from the interview or the captured exchanges, whether the dominant access pattern is:
- OLTP — ingest-heavy append-only, per-row writes, point reads; normalise; enforce integrity in constraints.
- OLAP — read-heavy analytics over large sets, aggregations and filters on named dimensions; model the dimensions and pre-aggregate where a named query pays for it.
- Hybrid — name the OLTP path and the OLAP path separately and the trade-off that reconciles them.
- Author
.cache/<session_id>/data-model.mdas the schema spec the build-phase developer implements against — NOT a deferral, NOT a sketch. For every table record:- its purpose, in one line, traced to a finding in
interview-notes.md; - every column with its type and the constraint that holds on it (NOT NULL, UNIQUE, CHECK, FK target);
- every index, paired with the named query pattern that justifies it — an index without a cited query is rejected per [[architecture/data-modeling]] and per the agent's own refusal (
data-architect.md); - the OLTP/OLAP/hybrid verdict from step 4 and the access patterns it optimises for.
- its purpose, in one line, traced to a finding in
- Trace every field to a captured external shape or an interview finding. A field with no trace is either speculative (drop it) or a missing capture (route to
needs-capture), never an ungrounded guess baked into the model. Anti-patterns to refuse, named in [[architecture/data-modeling]]: mirroring a code-class shape into a table, premature denormalization, an index without a query, a constraint the spec does not require. - Apply simplicity discipline per [[methodology/simplicity-discipline]]: model the smallest schema that serves the cited access patterns. A table with no query that reads it, a column with no consumer, a normalisation level beyond what the workload needs — each is speculative structure, dropped here, not deferred to build.
- IF the model surfaces an important new domain concept THEN add it to the glossary.
- IF a captured external shape is ambiguous or insufficient to model against THEN append the gap to
.cache/<session_id>/journal.md(service, the missing case) and fireneeds-capture. Do NOT invent the shape — the model is grounded in captures, not guesses.
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.
- yesterday First seen · 24 lines · 28 tokens per session scan A acc86defa32d
model-data-schema is a skill published in the GitHub repository nullhack/temple8 (11 stars, last pushed 27d ago), licensed MIT. It adds 28 tokens to every session and 748 once invoked, about $0.0001 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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