shinpr/sub-agents-skills

Cross-LLM sub-agent orchestration as an Agent Skills. Route tasks to Codex, Claude Code, Grok, GLM, Kimi, Cursor, Gemini, OpenCode, or Command Code from any...

View on GitHub ↗Jump to charts ↓

Summary Information

Updated 45 minutes ago
Added to GitGenius on August 31st, 2026
Created on January 16th, 2026
Open Issues & Pull Requests: 1 (+0)
Number of forks: 14
Total Stargazers: 81 (+0)
Total Subscribers: 1 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.8 hours
Mean response time: 12.4 days
90th percentile: 48.5 days
Tracked items: 4

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 0
New in 7 days: 0
Closed in 7 days: 0
Avg open age: N/A days
Stale 30+ days: 0
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

No label distribution available yet.

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

Sub-agents-skills is a Python library that enables cross-LLM sub-agent orchestration by routing tasks to multiple code-generation models from a single compatible tool.

The problem it solves is the fragmentation of specialized code-generation capabilities across different LLM providers. Rather than being locked into one model's strengths, developers need a way to dispatch specific tasks to the best-suited agent for each job. The tool works by exposing sub-agent routing as an Agent Skills interface, allowing any compatible orchestration framework to delegate work to Codex, Claude Code, Grok, GLM, Kimi, Cursor, Gemini, OpenCode, or Command Code based on task requirements.

This approach suits projects that already use an agent framework supporting the Agent Skills pattern and need flexibility across multiple code-generation models. It is particularly valuable when different models excel at different coding tasks—some may be stronger at certain languages, architectural patterns, or problem domains. Teams evaluating adoption should note that the tool requires an existing compatible agent orchestration system; it is not a standalone agent framework but rather an extension that adds multi-model routing capability to one.

The project shows active development with regular commits addressing both new model integrations and refinements to the routing logic. Work spans across multiple sub-agent implementations, indicating ongoing effort to maintain compatibility as new code-generation models emerge. The codebase demonstrates attention to supporting a growing roster of LLM providers, with each integration receiving dedicated implementation work.