Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for developers who like high-end coding models for architecture and review but want a cheaper way to handle repetitive file reading, transcript extraction, and bulk context digestion.
Who should use it
Who should skip it
Move on from imkunal007219/claude-coworker-model if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
imkunal007219/claude-coworker-model is tracked by RepoRadar as a coding assistant in the Coding Workflows section. It was first seen on 2026-06-28 and last updated on 2026-06-28. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. The standout signals for imkunal007219/claude-coworker-model are workflow potential (9.5) and open-source/build quality (8.4), while maturity (5.8) trails — that balance shapes where it fits best. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned imkunal007219/claude-coworker-model a composite score of 8.0 out of 10, placing it in the Silver tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'conditional' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
It sends codebase context, generated drafts, or transcripts to a secondary model endpoint, so first use should stay on a non-sensitive repo or a local Ollama worker; The reported token savings are maintainer-side numbers and worker-model quality varies sharply by provider, so validate summary accuracy before trusting it on complex refactors.