Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for local AI builders who want a visible orchestration layer above their existing coding-agent CLIs instead of stitching together shell scripts, sidecars, and review checkpoints by hand.
Who should use it
Who should skip it
Pass on fancy1108/Clutch if its scope or audience does not match what your team is building right now.
About this signal
fancy1108/Clutch is tracked by RepoRadar as a developer tool in the Agent Orchestration section. It was first seen on 2026-07-01 and last updated on 2026-07-01. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. Across RepoRadar's eight signals, fancy1108/Clutch is strongest on workflow potential (9.4) and open-source/build quality (8.4) and weakest on maturity (5.8) — a profile worth weighing against your own priorities. 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 fancy1108/Clutch a composite score of 7.9 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 vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
It scans and orchestrates local AI CLIs, MCP services, sidecar processes, and approval-gated workflows, so first evaluation belongs on a disposable workspace rather than a sensitive codebase; Model keys, session logs, file trees, diffs, and workflow state live in the local control layer, so review what the sidecar stores before connecting your normal toolchain.