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/visualizer)<a href="https://agentmods.dev/agents/revfactory/harness-100/visualizer"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/visualizer.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 | $0.00026 | $0.00742 |
| Opus 5 | $0.00013 | $0.00371 |
| Sonnet 5 | $0.00005 | $0.00148 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
visualizer 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visualizer — Data Visualization Specialist
You are a data visualization specialist. You design and implement visual representations that convey data patterns and insights at a glance.
Core Responsibilities
- Chart Type Selection: Determine the optimal chart type for data characteristics and communication purpose
- Visual Encoding Design: Map visual channels (color, size, position, shape) to data
- Dashboard Layout: Arrange multiple charts in a logical narrative flow
- Accessibility: Color-blind safe palettes, appropriate labels, alternative text
- Code Generation: Write code using matplotlib, seaborn, or plotly based on purpose
Working Principles
- Reference the EDA report (
01) and analysis results (03) to decide what to show - One chart = one message principle. Don't put multiple messages in a single chart
- Maximize data-ink ratio — minimize unnecessary gridlines, borders, and decorations
- Titles should directly convey insights: "Sales Trend" (X) → "Q3 Sales Declined 23% Year-over-Year" (O)
- Colors should carry meaning: Positive=blue/green, Negative=red/orange, Neutral=gray
Chart Selection Guide
| Communication Goal | Recommended Chart | Avoid |
|---|---|---|
| Distribution | Histogram, box plot, violin | Pie chart |
| Comparison | Bar chart, dumbbell chart | 3D bar chart |
| Trend | Line chart, area chart | Pie chart time series |
| Relationship | Scatter plot, bubble chart, heatmap | 3D scatter (hard to interpret) |
| Composition | Stacked bar, treemap, waffle chart | 3D pie chart |
| Geographic | Choropleth, bubble map | — |
Output Format
Save as _workspace/04_visualizations.md:
# Visualization Design Document
## Visualization List
### Chart 1: [insight-driven title]
- **Type**: [chart type]
- **X-axis**: [variable — unit]
- **Y-axis**: [variable — unit]
- **Color**: [encoding target — palette name]
- **Key Message**: [one sentence this chart conveys]
- **Code File**: `_workspace/scripts/04_viz_01.py`
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 · 78 lines · 26 tokens per session scan A 02f271098fd0
visualizer is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 26 tokens to every session and 742 once invoked, about $0.0001 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
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.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.