REPOGEO REPORT · LITE

ChesterRa/cccc

Default branch main · commit 80c93b01 · scanned 6/9/2026, 4:06:34 PM

GitHub: 885 stars · 78 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ChesterRa/cccc, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README H1 and opening paragraph to explicitly state AI agent orchestration

Why:

CURRENT

# CCCC
### Local-first Multi-agent Collaboration Kernel
**A lightweight multi-agent framework with infrastructure-grade reliability.**

COPY-PASTE FIX

# CCCC: Production-Grade Orchestration for AI Coding Agents
### Local-first Multi-agent Collaboration Kernel for 24/7 Workflows
**A lightweight, production-minded orchestrator for AI coding agents, offering infrastructure-grade reliability and coordination.**

highabout#2

Clarify the repository description to emphasize AI agent orchestration and production readiness

Why:

CURRENT

Coordinate your coding agents like a group chat — read receipts, delivery tracking, and remote ops from your phone. One pip install, zero infrastructure. A production‑minded orchestrator for 24/7 workflow

COPY-PASTE FIX

Production-grade orchestrator for AI coding agents. Coordinate diverse LLM-based agents with group chat features, read receipts, delivery tracking, and remote ops. One pip install, zero infrastructure, built for 24/7 workflows.

mediumreadme#3

Add a section clarifying `cccc`'s role relative to LLM agent frameworks

Why:

COPY-PASTE FIX

## CCCC vs. Agent Frameworks (LangChain, CrewAI, AutoGen)

While frameworks like LangChain, CrewAI, and AutoGen excel at building individual LLM agents and defining their internal logic, CCCC focuses on the *orchestration* layer. CCCC provides the durable coordination, visible delivery semantics, and unified control plane needed to run multiple diverse agents (whether built with these frameworks or standalone) as a reliable, production-grade system. Think of CCCC as the operating system for your agent team, ensuring they work together seamlessly and reliably, even across restarts and remote operations.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface ChesterRa/cccc

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

apache/airflow

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

apache/airflow · recommended 1×
kubernetes/kubernetes · recommended 1×
temporalio/temporal · recommended 1×
PrefectHQ/prefect · recommended 1×
AWS Step Functions · recommended 1×

CATEGORY QUERY
How to reliably orchestrate multiple AI coding agents for continuous 24/7 workflows?
you: not recommended
AI recommended (in order):
1. Apache Airflow (apache/airflow)
2. Kubernetes (kubernetes/kubernetes)
3. Temporal (temporalio/temporal)
4. Prefect (PrefectHQ/prefect)
5. AWS Step Functions
6. Azure Logic Apps
7. Google Cloud Workflows
AI recommended 7 alternatives but never named ChesterRa/cccc. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the best zero-infrastructure tools for coordinating diverse LLM-based coding agents?
you: not recommended
AI recommended (in order):
1. LangChain
2. CrewAI
3. Autogen
4. LlamaIndex
5. Haystack
AI recommended 5 alternatives but never named ChesterRa/cccc. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of ChesterRa/cccc?
pass
AI named ChesterRa/cccc explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ChesterRa/cccc in production, what risks or prerequisites should they evaluate first?
pass
AI named ChesterRa/cccc explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo ChesterRa/cccc solve, and who is the primary audience?
pass
AI named ChesterRa/cccc explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of ChesterRa/cccc. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/ChesterRa/cccc.svg)](https://repogeo.com/en/r/ChesterRa/cccc)

HTML

<a href="https://repogeo.com/en/r/ChesterRa/cccc"><img src="https://repogeo.com/badge/ChesterRa/cccc.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

ChesterRa/cccc — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite