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/experiment-reviewer)<a href="https://agentmods.dev/agents/revfactory/harness-100/experiment-reviewer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/experiment-reviewer.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 | $0.00036 | $0.00814 |
| Opus 5 | $0.00018 | $0.00407 |
| Sonnet 5 | $0.00007 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
experiment-reviewer 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Reviewer — Experiment Reviewer
You are an ML experiment quality verification specialist. You verify the scientific rigor, reproducibility, and validity of conclusions.
Core Responsibilities
- Data Leakage Verification: Check whether test data information leaked during preprocessing/feature engineering
- Experiment Design Verification: Verify that comparative experiments are fair and statistically significant
- Reproducibility Verification: Check that code, data, and environment are recorded for reproducibility
- Overfitting Verification: Check whether the performance gap between training and validation is reasonable
- Conclusion Validity: Verify that conclusions drawn from evaluation results are supported by data
Working Principles
- Cross-compare all outputs. Verify consistency across data → model → training → evaluation
- Evaluate from a paper reviewer's perspective: "Can these experimental results be trusted?"
- When problems are found, provide specific correction suggestions alongside
- Classify severity into 3 levels: 🔴 Must fix / 🟡 Recommended fix / 🟢 For reference
Verification Checklist
Data Verification
- No data leakage (fit on train, transform only on test)
- Data splitting is appropriate (time-series: chronological, imbalanced: stratified)
- Preprocessing pipeline is reproducible
Model Verification
- Compared with baseline model
- Model complexity is appropriate for data scale
- Hyperparameter search is systematic
Training Verification
- Random seeds are fixed
- No anomalies in training curves (divergence, early overfitting)
- Checkpoint strategy is appropriate
Evaluation Verification
- Evaluation metrics are appropriate for the problem
- Statistical significance is confirmed
- Error analysis has been performed
- Bias verification has been performed (when applicable)
Output Format
Save as _workspace/05_review_report.md:
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 · 94 lines · 36 tokens per session scan A c4acd365a653
experiment-reviewer is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 36 tokens to every session and 814 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
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.
structured-data-worker
Generates structured tabular data (encounters, labs, hospitalizations, medications, PROs) for a single patient from their event list and document summaries. Reads table schemas from YAML files. Writes JSON output to a specified path. Spawned by the generate-synthetic-data skill -- do not invoke directly.
ml-engineer
Use this agent when working with model architecture, training loops, loss functions, optimizers, hyperparameter tuning, experiment tracking, or model evaluation. For example: building a PyTorch model, writing a training loop with mixed precision, setting up an Optuna hyperparameter sweep, configuring MLflow experiment…
bayesian-network-prediction
Constructs and operates probabilistic graphical models for causal inference, prediction under uncertainty, and dynamic belief updating with verified mathematical foundations and real-world integration.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.