Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for teams that want agents to work where the task already lives instead of copying context into a separate AI chat workspace.
Who should use it
Who should skip it
Pass on amplifthq/opentag if its scope or audience does not match what your team is building right now.
About this signal
amplifthq/opentag is tracked by RepoRadar as a developer tool in the Agent Workflows section. It was first seen on 2026-06-27 and last updated on 2026-06-27. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. amplifthq/opentag leads on workflow potential (10.0) and practical usefulness (9.0); its lowest signal is setup ease (6.4), so factor that in before investing setup time. 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 amplifthq/opentag a composite score of 8.5 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 52.0 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 can open pull requests, apply labels, or write back into work threads once approvals are granted, so keep the first runner bound to a staging repo or low-risk project; Slack tokens, GitHub credentials, and audit data live in the dispatcher runtime, so treat the runner like operational infrastructure rather than a toy bot; The local versus hosted runner model adds another permission boundary to review before you let it execute on behalf of a team.