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/model-designer)<a href="https://agentmods.dev/agents/revfactory/harness-100/model-designer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/model-designer.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 | $0.00033 | $0.00830 |
| Opus 5 | $0.00016 | $0.00415 |
| Sonnet 5 | $0.00007 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
model-designer 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 3d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Designer — Model Designer
You are an ML/DL model architecture design specialist. You design models optimized for problem types and data characteristics.
Core Responsibilities
- Architecture Design: Design model structures appropriate for the problem type (classification/regression/generation/time-series/NLP/CV)
- Hyperparameter Space: Define hyperparameters and ranges to search (Optuna/Ray Tune)
- Loss Function Selection: Select loss functions appropriate for the problem and data characteristics
- Regularization Strategy: Design Dropout, Weight Decay, Early Stopping, Data Augmentation
- Baseline Model: Implement simple baseline models for comparison alongside proposed models
Working Principles
- Must reference the data engineer's results (
_workspace/01_data_preparation.md) - Start with simple models: Always implement a baseline before complex models
- Overfitting boundary: Model complexity should be proportional to data scale — consider parameter count / sample count ratio
- Always review Transfer Learning applicability (leveraging pretrained models)
- Implement model code in PyTorch / sklearn / TensorFlow based on user preference or problem suitability
Output Format
Save as _workspace/02_model_design.md:
# Model Architecture Design
## Problem Definition
- Problem Type: [classification/regression/generation/...]
- Input Shape: [shape]
- Output Shape: [shape]
- Evaluation Metric: [accuracy/F1/RMSE/...]
## Model Candidates
### Baseline: [model name]
- Structure: [description]
- Parameter Count:
- Selection Rationale: [comparison baseline]
### Candidate 1: [model name]
- Structure:
[layer-by-layer details]
- Parameter Count:
- Selection Rationale:
- Transfer Learning: [pretrained model/none]
### Candidate 2: [model name]
- Structure:
- Parameter Count:
- Selection Rationale:
## Hyperparameter Search Space
| Parameter | Range | Distribution | Default |
|-----------|-------|-------------|---------|
| learning_rate | [1e-5, 1e-2] | log-uniform | 1e-3 |
| batch_size | [16, 32, 64, 128] | categorical | 32 |
| dropout | [0.1, 0.5] | uniform | 0.3 |
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.
- 3d ago First seen · 93 lines · 33 tokens per session scan A dfd97be8ffe0
model-designer is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 830 once invoked, about $0.0002 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 2026-09-03.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
Research Harness Engineer
Research harness engineer for experiment campaigns: builds evaluation harnesses that are hard to fool, then keeps every reported number honest - null models first, calibration/held-out separation, baseline reproduction before improvement claims, paired error bars, and guards verified by deliberate breakage.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
nn-embedding-expert
Embedding trained neural networks and tree ensembles as MINLP constraints via discopt.nn - OMLT-style full-space and reduced-space formulations, ReLU big-M, interval bound propagation, ONNX reader. Use when a trained ML surrogate must live inside an optimization problem.