Winus Financial AI Skills - Be a one-person team of elite financial pros

Been trying this recently and really like the idea of turning financial workflows into reusable AI skills instead of prompting every time. It feels closer to how real analysis work is done. Curious to see how the skill library grows. Great launch!

Interesting concept! Is Winus Skills designed mainly for learning new skills or also for improving existing ones? Curious how the platform structures the learning experience. Looks promising — excited to learn more!

Winus financial AI Skill has completely eliminated the tedious work and validation involved in WACC analysis. It’s incredibly efficient and well done!

Hi PH 👋

Here's what we're seeing from early users.

People come in expecting to automate a data pull. Within the first session, they're building something much more specific — encoding the way their desk reads an earnings call, or the exact logic their risk team uses for a Fed statement.

Once you see what's underneath Skills, the shift makes sense.

Winus Skills run on 20 years of financial domain expertise and a 100B-parameter model, covering 40+ financial roles and data across 100+ countries. Retrieval, modeling, backtesting, validation, and reporting run in parallel. Users who've worked with general-purpose models notice the difference within a few queries.

What makes Winus Skills reusable in practice: hundreds of MCP tools connect each Skill directly to live market data, fundamentals, macro feeds, filings, research reports, and news. So when someone builds an earnings screener, it runs against current filings—not a paste from this morning. Write it once, run it every quarter.

Pairing Skills with Winus Agents takes it further. One team set up its FOMC analysis. Skill to fire the moment Powell's statement drops. Structured output arrives before anyone opens a browser—no manual coordination, and no one is waiting on the senior analyst to have bandwidth.

The personalized part: from raw information to final deliverable, in your format, your logic connected to your actual data sources.

Two things I'd love to hear from the PH community:

1. What's the workflow at your firm that still has someone's name on it—because only they know how to run it correctly?

2. For anyone who's built GPT workflows or internal prompt templates: what caused them to fall apart after the first few weeks?

Financial research usually requires checking multiple databases, reports, and news sources. Can Winus Deep Research + Skills automatically aggregate this information and generate structured investment insights? 📊🤖

The MCP model demonstrates a very strong professional understanding of finance. When asked to perform cross-analysis of complex financial concepts, some other tools tend to provide rather general responses, whereas Winus delivers more precise and in-depth analysis. For example, when asked yesterday about "the valuation differences between upstream and downstream sectors of the new energy industry chain," the answer was well-structured and insightful.

Congrats to the team - interesting launch!

One thing that stood out to me here is the focus on execution rather than just summarization. A lot of AI tools in finance today still stop at “here’s a summary of the data,” which is helpful but doesn’t actually remove much work from the research process.

If I could clone myself - probably monitoring corporate disclosures and filings across multiple markets. A lot of time is still spent manually checking announcements, parsing filings, and identifying the few pieces of information that actually move the investment thesis. Having a “clone” continuously tracking and summarizing those signals would free up a lot of research time.

To the second questions, we definitely also spend a decent amount of time checking sources. Especially for anything that will feed into investment decisions. Even when AI outputs look correct, there’s usually a second step of verifying where the numbers or statements came from. Traceable logic and source transparency are really important for adoption in finance workflows.

Curious for the team: How are users thinking about governance and version control for Skills when multiple analysts or teams are building workflows?

Really interesting direction here. One of the biggest challenges when using AI for finance is that most tools are trained on general web data, but financial analysis often requires structured datasets, domain terminology, and reliable sources.

What caught my attention with Winus Financial AI Skills is the idea of combining AI with dedicated financial data capabilities. If it can truly help users quickly retrieve and interpret things like macro indicators, company fundamentals, or market trends, that could save a lot of time for analysts and investors.

I’m especially curious about how the platform handles financial context and data accuracy when generating answers. Can users trace the sources behind the outputs or drill down into the underlying datasets?

Looking forward to trying it out and seeing how it fits into a typical research workflow for finance professionals. 📊

Congrats on the launch! One thing we see a lot in finance teams is that the real bottleneck isn’t generating insights, but operationalizing repeatable workflows (screening, monitoring, data validation, etc.). Turning those into reusable “skills” could be powerful if the logic remains transparent and auditable.

Curious to see how users end up structuring their own skills over time.