Item detail
github.com

EclipseElips/recoil

EclipseElips/recoil is a agent memory that RepoRadar is tracking in its AI Coding section, currently rated Silver tier with a 'try now' verdict. Its strongest signal is workflow potential, scored 9.0 out of 10.

Score7.9
Popularity1.0
Riskconditional
TierSilver
Score breakdown
Usefulness8.0
Novelty7.0
Momentum5.0
Maturity5.8
Open-source/build8.4
Evidence7.2
Workflow potential9.0
Setup ease6.4

Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.

Why it matters

Useful for developers who want lightweight, local memory for Claude Code, Codex, and similar agents without standing up a database or accepting repeated avoidable failures.

Who should use it

Developers using Claude Code, Codex, or similar coding agents day to dayTeams that want local memory without a vector databasePower users trying to cut repeated command and patch failuresBuilders evaluating lightweight agent-memory patterns before adopting a bigger stack

Who should skip it

Pass on EclipseElips/recoil if its scope or audience does not match what your team is building right now.

About this signal

EclipseElips/recoil is tracked by RepoRadar as a agent memory in the AI Coding section. It was first seen on 2026-06-30 and last updated on 2026-06-30. The current verdict is 'try now' with a Silver tier and moderate setup difficulty. EclipseElips/recoil leads on workflow potential (9.0) and open-source/build quality (8.4); its lowest signal is momentum (5.0), 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 EclipseElips/recoil 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 installs git hooks and records local failure context, so review the hook behavior before enabling it on sensitive repositories; Automatic lesson capture can surface snippets from commands or errors, so keep first use on codebases without sensitive secrets in terminal output.

Evidence links
Closest alternatives / related signals
coding-agentsmemorygogit-hooksclaude-codemit