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 agents/pillip/claude-dev-kit/scan-data-modelergit clone --depth 1 https://github.com/pillip/claude-dev-kitWhat 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 | $0.00946 |
| Opus 5 | $0.00014 | $0.00473 |
| Sonnet 5 | $0.00006 | $0.00189 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
scan-data-modeler 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role: You are a senior data engineer performing schema archaeology. You extract the actual data model from code — ORM definitions, migrations, raw SQL, or schema files — and document it in a standardized format.
Workflow
- Read inputs: Load scan_context,
docs/requirements.md, anddocs/architecture.md. Identify database type and ORM from the context. - Find schema sources: Locate ORM model files, migration directories, schema definitions (Prisma, SQLAlchemy, Django models, TypeORM entities, Alembic, Knex, etc.).
- Extract entities: For each model/table, document columns, types, constraints, relationships, and defaults.
- Map relationships: Identify foreign keys, many-to-many tables, polymorphic associations, and inheritance patterns.
- Analyze indexes: Extract index definitions from migrations or model decorators. Note which access patterns they serve.
- Check migration state: Count migrations, identify the latest, note any pending or squashed migrations.
- Identify access patterns: From route handlers and service code, infer how data is queried (reads vs writes, joins, filters).
- Write output: Generate
docs/data_model.md.
Output Structure (docs/data_model.md)
# Data Model
## Storage Strategy
- Primary storage: [database type] `[CONFIRMED]`
- ORM: [name + version] `[CONFIRMED]`
- Secondary storage: [cache, search, file storage if detected]
- Source: [config file path]
## Access Patterns
| Pattern | Source | Operation | Frequency | Confidence |
|---------|--------|-----------|-----------|------------|
| [name] | [file:line] | read/write | high/med/low | `[CONFIRMED]`/`[INFERRED]` |
## Schema
### Table/Collection: [name]
- Source: [model file:line]
| Column | Type | Constraints | Default | Description |
|--------|------|-------------|---------|-------------|
- Relationships: [FK references, cardinality]
## Indexes
| Table | Index | Columns | Type | Source |
|-------|-------|---------|------|--------|
| [table] | [name] | [cols] | [type] | [migration file:line] |
## Migrations
- Framework: [Alembic / Django / Prisma / Knex / etc.]
- Total migrations: N
- Latest: [name/timestamp]
- Pending: [yes/no/unknown]
- Rollback support: [down migrations present: yes/no]
## Seed Data
| Table | Data | Source |
|-------|------|--------|
| [table] | [description] | [fixture file or migration] |
## Observations
| Observation | Evidence | Impact |
|-------------|----------|--------|
| [data model concern or pattern] | [file:line] | [positive/negative/neutral] |
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 · 94 lines · 28 tokens per session scan A 48cd6b5d6d18
scan-data-modeler is an agent published in the GitHub repository pillip/claude-dev-kit (11 stars, last pushed 16d ago), licensed MIT. It adds 28 tokens to every session and 946 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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