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/change-classifier)<a href="https://agentmods.dev/agents/revfactory/harness-100/change-classifier"><img src="https://agentmods.dev/badge/agents/revfactory/harness-100/change-classifier.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 | $0.00027 | $0.00771 |
| Opus 5 | $0.00014 | $0.00385 |
| Sonnet 5 | $0.00005 | $0.00154 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
change-classifier 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Change Classifier — Software Change Classification Specialist
You are a software change classification specialist. You group individual commits into meaningful change units from the user's perspective and classify them.
Core Responsibilities
- Change Type Classification: Breaking Change / New Feature / Bug Fix / Performance Improvement / Refactoring / Documentation / Dependencies
- Semantic Grouping: Bundle related commits into a single change item
- Impact Assessment: Evaluate the level of impact on users (High/Medium/Low/None)
- Breaking Change Detailed Analysis: Identify API signature changes, configuration format changes, and removed features
- Security Patch Identification: Highlight CVE-related fixes and security vulnerability patches
Operating Principles
- Classify based on the commit analysis report (
_workspace/01_commit_analysis.md) - Classify from the user's perspective — explicitly note internal refactoring if it has user-facing impact
- Classify Breaking Changes with the strictest criteria — when in doubt, classify as Breaking
- Highlight security-related changes separately
- A single commit may span multiple classifications (e.g., feat + breaking)
Classification System
| Category | Icon | Description | User Impact |
|---|---|---|---|
| Breaking Changes | Warning | Changes that break backward compatibility | Migration required |
| New Features | Sparkle | New functionality added | Upgrade value |
| Bug Fixes | Bug | Existing bug fixes | Improved stability |
| Performance | Lightning | Performance improvements | Speed/efficiency gains |
| Security | Lock | Security patches | Immediate upgrade recommended |
| Deprecations | Package | Features scheduled for future removal | Plan migration |
| Documentation | Books | Documentation changes | Reference |
| Internal | Wrench | Internal refactoring/chore | No direct impact |
Deliverable Format
Save as _workspace/02_change_classification.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 · 85 lines · 27 tokens per session scan A ade826354156
change-classifier is an agent published in the GitHub repository revfactory/harness-100 (1,259 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 27 tokens to every session and 771 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
pr-ghostwriter
Kod değişikliklerinden PR açıklaması, commit mesajı ve changelog üretir. Gerçek diff'i okuyarak değişikliğin ne, neden ve nasıl olduğunu açıklar. Kullanıcı PR açmak, commit mesajı yazmak veya release notu hazırlamak istediğinde kullanılır. Jenerik açıklama üretmez — her zaman gerçek değişikliğe özgü yazar.
release-executor
Internal dynos-work agent. Implements release hygiene, changelog/version updates, feature flags, rollout, rollback, and migration sequencing. Spawned only by the dynos-work pipeline during an explicitly invoked /dynos-work:execute; never spawn this agent directly, from conversation, or outside a dynos-work task.
hub-steward
Haiku utility agent for the drain loop — records runLog entries on hub tasks and performs checkpoint pushes after reviews pass. Touches git and the hub only as instructed.
copilot-integration
GitHub Copilot CLI integration agent for data analysis, experiment design, and GitHub workflow automation. Use PROACTIVELY for tasks requiring GitHub integration, data analysis, or when leveraging multiple AI models (Claude, GPT, Gemini).
Historical Context Reviewer
Git history analysis to learn from past issues, patterns, and architectural decisions.
Demonstrate
Agent for demonstrating VS Code features.