Dashboard / Repos / Llm Answer Watcher

Llm Answer Watcher

C+ 74 completed
Web App
cli / python · small
286
Files
64,542
LOC
1
Frameworks
9
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#365057
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
61.00
Framework unique
—
Isolation
—
Last stage change
2026-05-10 03:35:24
Deduplication group #48604
Member of a group with 18 similar repo(s) — canonical #91565 view group →
Top concepts (2)
Project DescriptionTesting
Repobility — same analyzer, your code, free for public repos · /scan/

AI Prompt

Create a command-line tool in Python that monitors how Large Language Models discuss a user's brand versus competitors based on buyer-intent queries. The tool needs to support multiple LLM providers like OpenAI, Anthropic, and Mistral, and should extract structured signals such as brand mentions and rankings from the responses. Key features must include historical tracking using an SQLite database, automatic cost tracking, and the ability to generate HTML reports from the collected data. It should also support web search grounding for Gemini and provide a dual-mode CLI output—beautiful Rich output for humans and structured JSON for AI agents.
python cli llm ai monitoring sqlite openai anthropic web-search automation
Generated by gemma4:latest

Catalog Information

This project monitors how Large Language Models discuss a user's brand and competitors in buyer-intent queries.

Description

The llm-answer-watcher is a tool designed to track how Large Language Models (LLMs) mention a user's brand and its competitors in buyer-intent queries. It provides insights into the conversations happening around your brand, helping you stay on top of market trends and competitor activity. This project uses natural language processing techniques to analyze LLM responses and extract relevant information.

الوصف

هذا المشروع يراقب كيفية إشارة لغات اللغة الكبيرة (LLMs) إلى علامتك التجارية ومتنافستها في الاستفسارات ذات الغرض الشرائي. يوفر هذا الأداة نظرة شاملة على المحادثات التي تحدث حول علامتك التجارية، مما يساعدك على الوقوف على اتجاهات السوق والنشاط التنافسي. يستخدم هذا المشروع تقنيات معالجة اللغة الطبيعية لتحليل الإجابات من LLM و استخراج المعلومات ذات الصلة.

Novelty

7/10

Tags

brand-monitoring competitor-analysis buyer-intent-queries natural-language-processing market-trends conversation-analytics

Technologies

playwright pydantic rich typer

Claude Models

claude (unknown version)

Quality Score

C+
73.5/100
Structure
82
Code Quality
73
Documentation
94
Testing
85
Practices
59
Security
32
Dependencies
60

Strengths

  • Well-documented README with substantial content
  • CI/CD pipeline configured (github_actions)
  • Good test coverage (63% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Properly licensed project

Weaknesses

  • Potential hardcoded secrets in 4 files
  • 2902 duplicate lines detected \u2014 consider DRY refactoring
  • 7 'god files' with >500 LOC need decomposition

Recommendations

  • Move hardcoded secrets to environment variables or a secrets manager

Security & Health

8.6h
Tech Debt (A)
A
OWASP (100%)
FAIL
Quality Gate
A
Risk (12)
Repobility · open methodology · https://repobility.com/research/
MIT
License
12.6%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
60.7%
markdown
32.8%
yaml
5.1%
json
0.8%
toml
0.3%
html
0.2%
css
0.1%
shell
0.0%
text
0.0%

Frameworks

pytest

Concepts (2)

Per-row analysis by Repobility · https://repobility.com
CategoryNameDescriptionConfidence
About: code-quality intelligence by Repobility · https://repobility.com
auto_descriptionProject Description![License: MIT](https://github.com/nibzard/llm-answer-watcher/blob/main/LICENSE) ![Python 3.12+](https://www.python.org/downloads/) ![Tests](https://github.com/nibzard/llm-answer-watcher/actions/workflows/test.yml)80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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