Floop

C+ 71 completed
Ai Ml
unknown / go · small
342
Files
64,072
LOC
0
Frameworks
6
Languages

Pipeline State

completed
Run ID
#366544
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
59.67
Framework unique
Isolation
Last stage change
2026-05-10 03:35:10
Deduplication group #48619
Member of a group with 11 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionDocumentation
Repobility analyzer · published findings · https://repobility.com

AI Prompt

Create a command-line tool, similar to what's shown in the quick start, written in Go. This tool, named 'floop', should allow users to capture corrections made to AI agents, extract reusable behaviors, and activate them contextually. Key features to implement include learning from corrections using commands like `floop learn`, listing stored behaviors with `floop list`, and generating an activation prompt for a given file and task using `floop prompt`. It should also support managing the behavior store via commands like `floop stats` and `floop deduplicate`. The CLI should be robust and ideally output JSON for machine consumption.
go cli ai-agent tooling command-line behavior-capture context-aware memory
Generated by gemma4:latest

Catalog Information

floop is a tool that captures corrections made to AI agents, extracts reusable behaviors, and activates them in the right context.

Description

floop is an innovative solution for persistent memory in AI coding agents. It learns from corrections, extracts reusable behaviors, and activates them in context. This approach uses spreading activation, inspired by cognitive science, to build an associative 'blast radius' around current work. The tool provides features such as learning from corrections, context-aware activation, spreading activation, token-optimized storage management, and integration with AI tools via the Model Context Protocol.

الوصف

هو أداة تعلم الذكريات المستمرة في एजENTS الكودية للذكاء الاصطناعي. يتعلم من التصحيحات، ويستخرج السلوكيات المكررة، وينشطها في السياق المناسب. تستخدم هذه الطريقة تفاعل التشتت، ملهمة من العلوم النفسية، لإنشاء منطقة انفجار ارتباطية حول العمل الحالي. توفر الأداة سمات مثل التعلم من التصحيحات، تنشيط السياق المعرفي، تفاعل التشتت، تخزين تلقائي للذاكرة، وإدارة تخزين السلوكيات، ودمج أدوات الذكاء الاصطناعي عبر بروتوكول سياق النموذج.

Novelty

9/10

Tags

persistent-memory ai-coding-agents correction-learning context-aware-activation spreading-activation

Technologies

ent grpc

Claude Models

claude-opus-4.6 claude (unknown version)

Quality Score

C+
70.7/100
Structure
77
Code Quality
61
Documentation
69
Testing
85
Practices
59
Security
82
Dependencies
50

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (82% test-to-source ratio)
  • Code linting configured (golangci-lint)
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • 5 files with critical complexity need refactoring
  • Potential hardcoded secrets in 1 files
  • 3550 duplicate lines detected \u2014 consider DRY refactoring
  • 4 'god files' with >500 LOC need decomposition

Recommendations

  • Move hardcoded secrets to environment variables or a secrets manager

Security & Health

21.6h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility · severity-and-effort ranking · https://repobility.com
Apache-2.0
License
7.1%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

go
92.8%
markdown
4.9%
javascript
1.2%
yaml
0.9%
shell
0.2%
json
0.0%

Frameworks

None detected

Concepts (2)

Open data · scored by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
Repobility analyzer · published findings · https://repobility.com
auto_descriptionProject Description![CI](https://github.com/nvandessel/floop/actions/workflows/ci.yml) ![Release](https://github.com/nvandessel/floop/releases/latest) ![Go 1.25+](https://go.dev/)80%
auto_categoryDocumentationdocs70%

Quality Timeline

1 quality score recorded.

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