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 commands/jonase47/ccpr/p3-data-modelgit clone --depth 1 https://github.com/jonase47/ccprWrote 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/commands/jonase47/ccpr/p3-data-model)<a href="https://agentmods.dev/commands/jonase47/ccpr/p3-data-model"><img src="https://agentmods.dev/badge/commands/jonase47/ccpr/p3-data-model.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.02258 |
| Opus 5 | $0.00000 | $0.01129 |
| Sonnet 5 | $0.00000 | $0.00452 |
| Haiku 4.5 | $0.00000 | $0.00226 |
Grade A, and why
p3-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 today.
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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/p3-data-model – Data Model & API Design
Designs the Data Model with entities and relationships as well as the API design with interface specification. The result is DATA_MODEL.md and API_SPEC.md as the binding foundation for implementation.
Argument: $ARGUMENTS = [Domain/Entity, e.g. "User Management", "Order Process", "Product Catalog"]
If provided: Focus the design on the named domain or entity and its direct relationships. If not provided: Read ARCHITECTURE.md, FEATURES.md and USER_JOURNEYS.md and develop the complete Data Model for the entire system. If any context is missing, ask for the core entities of the system.
Execution
1. Read Context
Read the following files (if available):
- ARCHITECTURE.md (components, data flows – what must the model support?)
- TECH_STACK.md (database choice – influences the model design)
- FEATURES.md (which features impose which data requirements?)
- USER_JOURNEYS.md (what data is generated during usage?)
- DSGVO_INITIAL_ASSESSMENT.md (DSGVO (GDPR) privacy constraints: deletability, anonymization)
2. Delegation to System Architect Agent (Lead)
Delegate the Data Model and API design to the system-architekt agent:
Design the Data Model and API specification. Focus (if provided): $ARGUMENTS Context: [insert key points from ARCHITECTURE.md, FEATURES.md, TECH_STACK.md]
A. Data Model
- Entity-Relationship diagram (Mermaid ERD or ASCII)
- Per entity: fields with data types, required fields, constraints
- Relationships: 1:1, 1:n, n:m with justification
- Indexes: which fields are indexed for performance?
- DSGVO markings: which fields contain personal data?
B. API Design
- API style: REST / GraphQL / tRPC – with justification
- Resources and endpoints (overview of all routes)
- Request/Response schemas for critical endpoints
- Authentication and authorization concept at the API level
- Error handling: standard error codes and formats
- Versioning strategy
C. Database Design Decisions
- Normalization level and justification
- Migration strategy (how are schema changes deployed?)
- Soft Delete vs. Hard Delete – decision and justification (DSGVO-relevant)
- Audit trail: which changes are logged?
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.
- today First seen · 216 lines · 0 tokens per session scan A b9e5a83f3984
p3-data-model is a command published in the GitHub repository jonase47/ccpr (1 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,258 tokens. 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 commands, from other repositories
wiki-tune
Tune wiki schema and conventions interactively.
listen
Show what a given agent should read before coding, plus briefs/approvals mentioning it.
pseo-init
Use when: kullanıcı "yeni proje", "init", "proje kur", "yeni domain ekle", "scaffold" gibi ifadeler kullanır ya da /pseo-init çağırırsa. Also use when: portföye yeni bir SEO projesi alındı, projects/{slug}/ klasörü ve project.config.json dosyasının schemaya uygun ilk hâli üretilecek; brief'te slug + domain + market…
forge-insights
Analyze past sessions for error patterns, file activity, and recommendations.
forge-context-status
Report on current context window usage, cache health, and compaction recommendation. Read-only — does not compact.
CLAUDE
Each subdirectory is one engine verb dispatched by ../cli/cli.ts: / .ts exports a pure handler that resolves the vault, does its work through ../core/ primitives, and returns its own typed report. Commands stay thin — they compose core checks and builders; they do not reimplement them. The canonical output schema is…