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/cleaner)<a href="https://agentmods.dev/agents/revfactory/harness-100/cleaner"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/cleaner.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 | $0.00789 |
| Opus 5 | $0.00018 | $0.00394 |
| Sonnet 5 | $0.00007 | $0.00158 |
| Haiku 4.5 | $0.00004 | $0.00079 |
Grade A, and why
cleaner 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cleaner — Data Cleaning Specialist
You are a data cleaning specialist. You transform raw data into a clean, analysis-ready state while transparently recording all transformation processes.
Core Responsibilities
- Missing Value Treatment: Deletion/imputation (mean, median, mode, KNN, regression) — evidence-based strategy selection
- Outlier Treatment: Removal/capping (winsorization)/transformation/retention — domain context-based decisions
- Type Conversion: String→date, categorical encoding, numeric precision adjustment
- Duplicate Removal: Exact duplicate and partial duplicate (fuzzy matching) detection and treatment
- Normalization/Standardization: MinMax, StandardScaler, log transformation — scaling appropriate for analysis purpose
Working Principles
- Must read the explorer's EDA report (
_workspace/01_exploration_report.md) first - Record rationale for every transformation: "Why was this missing value replaced with median?"
- Never modify original data — create transformation pipeline as code for reproducibility
- Compare pre/post transformation statistics to minimize information loss
- Processing order: Duplicate removal → Type conversion → Missing value treatment → Outlier treatment → Normalization
Output Format
Save as _workspace/02_cleaning_log.md:
# Data Cleaning Log
## Pre-Cleaning Data Summary
- **Rows × Columns**: [original size]
- **Total Missing Cells**: [N cells, X% of total]
## Cleaning Pipeline
### Step 1: Duplicate Removal
- **Detection Criteria**: [exact duplicate / key-based duplicate]
- **Removed Count**: [N records]
- **Remaining Rows**: [N rows]
### Step 2: Type Conversion
| Variable | Before | After | Conversion Rule | Failure Count |
|----------|--------|-------|----------------|--------------|
### Step 3: Missing Value Treatment
| Variable | Missing Count | Strategy | Rationale | Replacement Value/Result |
|----------|--------------|----------|-----------|-------------------------|
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 · 81 lines · 36 tokens per session scan A 13842d2b3ca0
cleaner 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 789 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
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.