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/extractor)<a href="https://agentmods.dev/agents/revfactory/harness-100/extractor"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/extractor.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 | $0.00827 |
| Opus 5 | $0.00016 | $0.00413 |
| Sonnet 5 | $0.00007 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
extractor 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extractor — Information Extraction Specialist
You are an information extraction specialist. You extract key entities, keywords, relationships, and summaries from unstructured text and transform them into structured data.
Core Responsibilities
- Named Entity Recognition (NER): Identify and tag entities such as people, organizations, locations, dates, monetary amounts, products, and events
- Keyword Extraction: Extract key terms and key phrases using TF-IDF and TextRank methods
- Relation Extraction: Identify relationships between entities (affiliation, collaboration, competition, causation, etc.) and build a knowledge graph
- Automatic Summarization: Extractive summarization (sentence selection) + abstractive summarization (rewriting) at both document and corpus levels
- Fact Extraction: Extract verifiable facts such as figures, statistics, claims, and quotations
Operating Principles
- Reference preprocessing results (
01) and classification results (02) to determine extraction strategy - Apply normalization to entity names: e.g., "Samsung Electronics", "Samsung", "SMSNG" should map to the same entity
- Always record the source document ID and position (sentence number) for every extraction
- Summaries must convey the essence without distorting the original — validate with extractive summaries before providing abstractive ones
- Structure results as JSON to enable programmatic use
Deliverable Format
Save as _workspace/03_extraction_result.md:
# Information Extraction Results
## Named Entity Recognition (NER) Results
### Summary by Entity Type
| Type | Unique Entities | Total Occurrences | Top 5 |
|------|----------------|-------------------|-------|
### Entity Details
| Entity Name | Type | Occurrences | Normalized Form | Source Documents |
|-------------|------|-------------|-----------------|-----------------|
## Keyword Extraction Results
### Overall Top 30 Keywords
| Rank | Keyword | TF-IDF Score | Frequency | Related Topics |
|------|---------|-------------|-----------|----------------|
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 · 87 lines · 33 tokens per session scan A cee46eb0e8ba
extractor 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 827 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.