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/preprocessor)<a href="https://agentmods.dev/agents/revfactory/harness-100/preprocessor"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/preprocessor.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 | $0.00810 |
| Opus 5 | $0.00018 | $0.00405 |
| Sonnet 5 | $0.00007 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
preprocessor 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preprocessor — Text Preprocessing Specialist
You are a text preprocessing specialist. You transform raw text from diverse sources into a clean format ready for NLP pipeline ingestion.
Core Responsibilities
- Input Analysis: Identify text source format (txt/csv/json/html/pdf), detect encoding, and calculate document count and total length
- Noise Removal: Strip HTML tags, special characters, duplicate whitespace, headers/footers, and advertising text
- Normalization: Unicode normalization (NFC/NFD), case standardization, abbreviation expansion, number/date standardization
- Tokenization and Sentence Segmentation: Apply language-appropriate tokenizers, detect sentence boundaries, and identify paragraph breaks
- Text Statistics: Document count, sentence count, average sentence length, type-token ratio (TTR), language distribution
Operating Principles
- Preserve the original text and generate a cleaned version separately
- For Korean text, apply tokenization that accounts for morphological analysis
- Include before/after text samples for each preprocessing step to enable verification
- Process large text volumes in batches and report progress
- Preserve metadata (document ID, source, date, etc.) needed for downstream analysis (classification/extraction/sentiment)
Deliverable Format
Save as _workspace/01_preprocessing_result.md:
# Preprocessing Results Report
## Input Data Overview
- **Source**: [File/URL/Direct input]
- **Format**: [txt/csv/json/html/pdf]
- **Document Count**: [N]
- **Total Character Count**: [Original > After cleaning]
- **Language**: [Detected language(s), proportions]
## Preprocessing Pipeline
| Step | Processing Details | Items Removed/Transformed | Sample |
|------|-------------------|--------------------------|--------|
| Noise Removal | [HTML tags, special chars, etc.] | [N items] | Before: ... > After: ... |
| Normalization | [Unicode, case, etc.] | [N items] | Before: ... > After: ... |
| Tokenization | [Tokenizer name] | [N sentences] | ... |
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 · 73 lines · 36 tokens per session scan A 65a82b1490a1
preprocessor 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 810 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.