REPOGEO REPORT · LITE
TimeCopilot/timecopilot
Default branch main · commit be1f48f8 · scanned 6/16/2026, 5:27:45 PM
GitHub: 531 stars · 77 forks
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface TimeCopilot/timecopilot, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition the README's opening to clarify identity and counter miscategorization
Why:
CURRENTThe README starts with centered divs and badges, with the core identity statement appearing later in the first paragraph.
COPY-PASTE FIX# TimeCopilot: The GenAI Forecasting Agent for LLM-powered Time Series TimeCopilot is an open-source forecasting agent that combines the power of large language models with state-of-the-art time series foundation models (Amazon Chronos, Salesforce Moirai, Google TimesFM, Nixtla TimeGPT, etc.). It automates and explains complex forecasting workflows, making time series analysis more accessible while maintaining professional-grade accuracy. This project is *not* a time tracking or personal productivity application.
- mediumabout#2Update the 'About' description to explicitly state core function and clarify what it is not
Why:
CURRENTTimeCopilot: the GenAI Forecasting Agent. Built on LLMs and Time Series Foundation Models, it lets you forecast, cross-validate, and detect anomalies using multiple foundation models through a single API. From finance and energy to web analytics, TimeCopilot turns natural-language queries into production-ready forecasts.
COPY-PASTE FIXTimeCopilot: A GenAI Forecasting Agent (not a time tracker). It uses LLMs and Time Series Foundation Models to forecast, cross-validate, and detect anomalies via a single API. Turn natural-language queries into production-ready forecasts for finance, energy, web analytics, and more.
- lowtopics#3Add 'python' and 'python-library' to repository topics
Why:
CURRENTagent-based-modeling, agentic-ai, artificial-intelligence, forecasting, generative-ai, gpt, llms, machine-learning, time-series, time-series-forecasting
COPY-PASTE FIXagent-based-modeling, agentic-ai, artificial-intelligence, forecasting, generative-ai, gpt, llms, machine-learning, python, python-library, time-series, time-series-forecasting
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- Google Cloud Vertex AI · recommended 2×
- ChatGPT with Code Interpreter · recommended 1×
- Microsoft Azure Machine Learning · recommended 1×
- Hugging Face Transformers · recommended 1×
- DataRobot · recommended 1×
- CATEGORY QUERYHow can I generate production-ready time series forecasts using natural language prompts?you: not recommendedAI recommended (in order):
- ChatGPT with Code Interpreter
- Google Cloud Vertex AI
- Microsoft Azure Machine Learning
- Hugging Face Transformers
- DataRobot
AI recommended 5 alternatives but never named TimeCopilot/timecopilot. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat tools automate time series anomaly detection and forecasting across various foundation models?you: not recommendedAI recommended (in order):
- Databricks Lakehouse Platform
- MLflow (mlflow/mlflow)
- Amazon SageMaker
- Google Cloud Vertex AI
- Azure Machine Learning
- Domino Data Lab
- C3 AI Platform
- Cortex (cortexlabs/cortex)
AI recommended 8 alternatives but never named TimeCopilot/timecopilot. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of TimeCopilot/timecopilot?passAI did not name TimeCopilot/timecopilot — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts TimeCopilot/timecopilot in production, what risks or prerequisites should they evaluate first?passAI named TimeCopilot/timecopilot explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo TimeCopilot/timecopilot solve, and who is the primary audience?passAI named TimeCopilot/timecopilot explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of TimeCopilot/timecopilot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/TimeCopilot/timecopilot)<a href="https://repogeo.com/en/r/TimeCopilot/timecopilot"><img src="https://repogeo.com/badge/TimeCopilot/timecopilot.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
TimeCopilot/timecopilot — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite