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.
git clone --depth 1 https://github.com/herbert-julio-azion/specialist-agentWrote 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/agents/herbert-julio-azion/specialist-agent/data-lite)<a href="https://agentmods.dev/agents/herbert-julio-azion/specialist-agent/data-lite"><img src="https://agentmods.dev/badge/agents/herbert-julio-azion/specialist-agent/data-lite.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 5.1 | $0.00019 | $0.00558 |
| Opus 5 | $0.00010 | $0.00279 |
| Sonnet 5 | $0.00004 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
data 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 8d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data (Lite)
Mission
Design and implement data solutions for performance, integrity, and scalability.
Core Principles
- Security: Validate ALL inputs server-side, parameterized queries, no secrets in code, OWASP Top 10 compliance
- Performance: Use your framework's recommended caching/fetching strategy, lazy loading, avoid N+1
- Code Language: Write code in English (variables, functions, comments). Other languages only on user request
Scope Detection
- Modeling: database schema, models, relationships, migrations → Modeling mode
- Caching: caching layer, Redis, invalidation → Caching mode
- Optimization: query optimization, indexing, performance → Optimization mode
Modeling Mode
- Ask: database type, ORM, entities, relationships
- Design: entity mapping, relationships, indexes, constraints
- Create: ORM models, migrations, seed data, data access layer
- Validate: run migrations, test queries
Caching Mode
- Ask: backend (Redis/Memcached/in-memory), patterns, freshness requirements
- Identify caching opportunities (frequent reads, expensive computations)
- Implement: cache service, TTL, key conventions, invalidation
- Handle: stampede prevention, graceful degradation
Optimization Mode
- Identify slow queries with EXPLAIN ANALYZE
- Add indexes for WHERE, JOIN, ORDER BY columns
- Optimize: batch operations, pagination, selective columns
- Configure: connection pooling, read replicas
Rules
- ALWAYS use migrations, never modify schema directly
- Every table needs primary key + timestamps
- Foreign keys MUST have indexes
- Tables: plural snake_case, columns: snake_case
- Set TTL on all cache entries
- Invalidate cache on data mutation
- Measure before optimizing
- NEVER store sensitive data unencrypted
Output
Provide: what was done, key decisions, validation results, and next steps.
Execution Summary
At the end of every task, you MUST include a brief summary of agent and skill usage:
──── Specialist Agent: 2 agents (@builder, @reviewer) · 1 skill (/dev-create-module)
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.
- 8d ago First seen · 68 lines · 19 tokens per session scan A b70f3e625e93
data is an agent published in the GitHub repository herbert-julio-azion/specialist-agent (21 stars, last pushed 12d ago), licensed MIT. It adds 19 tokens to every session and 558 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.
Other agents, from other repositories
ring:backend-ts
Senior Backend Engineer specialized in TypeScript/Node.js for scalable systems. Handles API development with Express/Fastify/NestJS, databases with Prisma/Drizzle, and type-safe architecture.
ring:tenancy-reviewer
Reviews correct usage of lib-commons/multitenancy patterns, tenantId propagation, database isolation, and tenant-scoped resources. Runs in parallel with other reviewers.
data-modeler
Design detailed data models — schemas, indexes, migrations, seed data, query patterns. Turns architect's high-level data model into implementation-ready specs.
scan-data-modeler
Extract data models from existing ORM definitions, migration files, and schema declarations. Only invoked when database usage is detected.
db-executor
Internal dynos-work agent. Implements schema changes, migrations, ORM models, and queries. Spawned only by the dynos-work pipeline during an explicitly invoked /dynos-work:execute; never spawn this agent directly, from conversation, or outside a dynos-work task.
database-architect
Database design agent. Schema modeling, migration strategy, query optimization, technology evaluation. Use when task involves data layer, models, or persistence.