revfactory/harness

Description: A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.

View on GitHub ↗Jump to charts ↓

Summary Information

Updated 30 minutes ago
Added to GitGenius on June 7th, 2026
Created on March 26th, 2026
Open Issues & Pull Requests: 25 (+0)
Number of forks: 1,157
Total Stargazers: 8,460 (+0)
Total Subscribers: 51 (+0)

Issue Activity (beta)

Open issues: 3
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 2 days
Stale 30+ days: 3
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (4)
  • bug (1)
  • question (1)

Most active issues this week

No issue events were indexed in the last 7 days.

Repository Insights (GitGenius)

Median issue/PR response: 0.0 hours
Mean response time: 5.6 days
90th percentile: 14.3 days
Tracked items: 11

Most active contributors

Detailed Description

Harness is a Claude Code plugin that functions as a team-architecture factory, automatically generating domain-specific agent teams and their associated skills based on natural language descriptions. When a user provides a domain description with prompts like "build a harness for this project," the plugin decomposes the request into coordinated teams of specialized agents and generates the necessary agent definitions and skills tailored to that domain. The plugin is written in HTML and operates at the L3 Meta-Factory layer of the Claude Code ecosystem, specifically within the Team-Architecture Factory sub-layer, distinguishing it from neighboring tools like Archon which focuses on runtime configuration.

The core functionality centers on six pre-defined team-architecture patterns that users can leverage: Pipeline for sequential dependent tasks, Fan-out/Fan-in for parallel independent tasks, Expert Pool for context-dependent selective invocation, Producer-Reviewer for generation followed by quality review, Supervisor for central agent with dynamic task distribution, and Hierarchical Delegation for top-down recursive delegation. The plugin automatically generates agent definitions in the .claude/agents/ directory and skills in the .claude/skills/ directory, implementing Progressive Disclosure for efficient context management. It includes validation features such as trigger verification, dry-run testing, and comparative testing between configurations with and without skills.

According to GitGenius activity tracking, the repository shows strong engagement with a median issue and pull request response latency of 0.0 hours and a mean of 133.6 hours across 11 tracked items. The most active contributor is revfactory with 11 events, followed by dongwookim-ml with 3 events and yijisoo with 2 events. Enhancement requests dominate the issue labels with 4 tracked items, supplemented by 1 bug report and 1 question. The repository is classified across multiple DevOps and infrastructure categories including feature flags, continuous delivery, deployment automation, cloud infrastructure, configuration management, release management, and CI/CD.

The plugin operates in two execution modes: Agent Teams mode as the default, which uses TeamCreate, SendMessage, and TaskCreate for scenarios requiring two or more collaborating agents, and Subagents mode for one-off tasks without inter-agent communication needs. The repository maintains version 1.2.0 under Apache 2.0 licensing and includes internationalization support with README documentation available in English, Korean, and Japanese.

Harness connects to a broader ecosystem of Claude Code tools and maintains relationships with neighboring repositories including coleam00/Archon, SaehwanPark/meta-harness, affaan-m/ECC, and wshobson/agents. The project has generated production applications including revfactory/harness-100, which contains 100 production-ready agent team harnesses across 10 domains in both English and Korean, totaling 200 packages with 1,808 markdown files. Research documented in revfactory/claude-code-harness demonstrates measured improvements in output quality, with reported metrics showing 60 percent average quality improvement, 100 percent win rate across 15 tasks, and 32 percent reduction in output variance, though these figures are explicitly noted as author-measured on a limited sample pending third-party replication.

harness
by
revfactoryrevfactory/harness

Repository Details

Fetching additional details & charts...