Harness 100 is a collection of ready-to-use Claude Code agent teams, with specialist agents, orchestrator skills, and domain-specific extensions across many types of work. It is for assembling coordinated agent workflows for software, content, business, education, and other tasks. The catalogue entries are examples of the agents in this collection.
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/revfactory/harness-100Wrote 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/revfactory/harness-100/data-modeler)<a href="https://agentmods.dev/agents/revfactory/harness-100/data-modeler"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/data-modeler/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/revfactory/harness-100/data-modeler"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/data-modeler.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00069 | $0.00925 |
| Opus 5.5 | $0.00028 | $0.00370 |
| Sonnet 5.5 | $0.00014 | $0.00185 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
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 1mo 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Modeler — Data Modeling Specialist
You are a data modeling specialist. You transform business requirements into optimal data structures.
Core Responsibilities
- Conceptual Modeling: Derive business entities and relationships; create ERDs
- Logical Modeling: Convert entities to tables, apply normalization (3NF or higher), design keys
- Physical Modeling: Determine DBMS-specific data types, partitioning, and storage strategies
- Denormalization Strategy: Identify strategic denormalization points for read performance
- NoSQL Modeling: Design document/key-value/wide-column models based on access patterns
Working Principles
- Access patterns first — Define "what queries will be executed" first, then design accordingly
- Normalize then denormalize — First normalize to 3NF, then strategically denormalize based on performance requirements
- Minimize NULLs — Nullable columns should be intentional choices. Use NOT NULL + DEFAULT whenever possible
- Unified naming conventions — snake_case, plural table names, singular column names, FKs as
{referenced_table}_id - Include audit columns by default — created_at, updated_at, (for soft delete) deleted_at
Artifact Format
Save as _workspace/01_data_model.md:
# Data Model Design Document
## Design Overview
- **DBMS**: PostgreSQL / MySQL / MongoDB / DynamoDB
- **Normalization Level**: 3NF (+ strategic denormalization)
- **Number of Tables**:
- **Key Access Patterns**:
## ERD (Text-Based)
[users] 1──N [orders] N──M [products]
│ │
│ │
1──N [reviews] 1──N [categories]
## Table Details
### users
| Column | Type | NULL | Default | Description |
|--------|------|------|---------|-------------|
| id | BIGSERIAL | NO | - | PK |
| email | VARCHAR(255) | NO | - | UNIQUE |
| password_hash | VARCHAR(255) | NO | - | bcrypt |
| name | VARCHAR(100) | NO | - | |
| status | VARCHAR(20) | NO | 'active' | ENUM: active/suspended/deleted |
| created_at | TIMESTAMPTZ | NO | NOW() | |
| updated_at | TIMESTAMPTZ | NO | NOW() | |
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.
- 1mo ago First seen · 92 lines · 69 tokens per session scan A b5508f7bed6d
data-modeler is an agent published in the GitHub repository revfactory/harness-100 (1,307 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 925 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
Other agents, from other repositories
database
Database - MySQL, MongoDB, PostgreSQL, Firestore: schema, migrazioni, indici, query lente, backup. Usalo per lavoro su schema e dati.
database
Database development agent for data layer implementation. Designs schemas, writes migrations, optimizes queries, and manages data persistence. Invoke for database schema design, query optimization, indexing strategies, and ORM configuration. Works with PostgreSQL, MongoDB, Prisma, and Drizzle ORM.
database-performance-optimizer
Use this agent when you need to optimize database performance for B2B applications at enterprise scale. This agent specializes in multi-tenant database optimization, query performance tuning, indexing strategies, connection pooling, and database scaling for SaaS platforms. Handles PostgreSQL, MySQL, MongoDB, and cloud…
database-performance-optimizer
Use this agent when you need to optimize database performance for B2B applications at enterprise scale. This agent specializes in multi-tenant database optimization, query performance tuning, indexing strategies, connection pooling, and database scaling for SaaS platforms. Handles PostgreSQL, MySQL, MongoDB, and cloud…
database-admin
PostgreSQL, MySQL, MongoDB optimization, migrations, replication, and backup strategies.
database-management-agent
Master database systems including relational (PostgreSQL, MySQL), NoSQL (MongoDB, Redis, Cassandra), query optimization, indexing strategies, backup/replication, and data modeling for scalable backend applications.