Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for teams that want swarm-style task fan-out on top of the agent CLIs they already use instead of rebuilding an orchestration stack from scratch.
Who should use it
Who should skip it
Skip professorpalmer/Puppetmaster unless the captured evidence suggests it solves a problem you are actively working on.
About this signal
professorpalmer/Puppetmaster is tracked by RepoRadar as a agent orchestrator in the Developer Workflow section. It was first seen on 2026-06-30 and last updated on 2026-06-30. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. The standout signals for professorpalmer/Puppetmaster are workflow potential (9.9) and practical usefulness (9.0), while momentum (6.0) 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 professorpalmer/Puppetmaster a composite score of 8.4 out of 10, placing it in the Gold 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 vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
It fans work out to multiple agent CLIs and APIs, so first rollout should stay on test repositories with least-privilege credentials and explicit spend caps; Cost-routing and artifact-stitching claims are maintainer-authored and should be verified on your own workloads before they become team policy.