Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI agent developers, CLI maintainers, security reviewers, platform teams, and dev-tools engineers who need an evidence-backed command index for any CLI their agent harness drives (kubectl-style tools, internal platform CLIs, dev tooling, payment / infra tools) and want to stop agents burning tokens rediscovering the surface with `--help`, trial flags, and backtracking on missing operand
Who should use it
Who should skip it
Consider modiqo/cliare lower priority if you already have a working solution in this category.
About this signal
modiqo/cliare is tracked by RepoRadar as a cli agent-readiness auditor in the AI Agents section. It was first seen on 2026-07-03 and last updated on 2026-07-03. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. The standout signals for modiqo/cliare are workflow potential (9.8) and practical usefulness (9.0), while setup ease (6.4) 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 modiqo/cliare a composite score of 8.3 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
Probes the target CLI binary by running it under bounded controls across five contexts (clean / repository / authenticated / host / fixture-backed / CI) — the authenticated / host contexts intentionally exercise paths that may write files or hit network endpoints; review the side-effect map in the persona report before promoting to a CI gate that runs on every PR, and whitelist only the contexts the target CLI is actually safe to exercise in your environment; The agent-skill output is generated from the runtime evidence; treat it as a starting point, not a finished doc — review the generated SKILL.md against the actual CLI's command surface (especially destructive flags and side-effect-laden paths) before committing it to a production skills directory; Requires Rust toolchain for `cargo install cliare`; on a clean machine the install is a 5-10 minute compile. For CI use, vendor the binary into a Docker image or pin a prebuilt release artifact to keep the gate under the 60-second PR budget.