Share Your Ideas for MuleRun Agent Builder + Skills — Let’s Co‑Define the Next‑Gen Way to Monetize Agents

Hi creators :waving_hand:

To help you build AI agents more efficiently and monetize them faster, we’re building MuleRun Agent Builder — a next‑gen agent creation paradigm based on Base Agent + Skills + Knowledge + Runtime.

To make MuleRun Agent Builder actually useful for you, we’d love your input and co‑building on a few core questions:

  1. :white_check_mark:What kind of agents are truly worth paying for?

    Think about a specific, high-value problem it solves. Who are the target users, and why would they pay for it? Are there competitors or similar tools? If yes, what would you want this agent to do differently or better?

  2. :white_check_mark:What skills does this agent need?

    If the skill already exists, please share the link (e.g. skills on https://skillsmp.com/ or any other place).

  3. :white_check_mark:What features or infra in MuleRun Agent Builder would make building these agents easier?

:wrapped_gift: Early access for valuable insights

MuleRun Agent Builder is currently in a private internal preview. We’ll be opening limited early access on January 15, 2026. High‑quality, thoughtful responses here will be at the top of our list for the first wave of invitations.

Your insights won’t just shape a product; they will help define the next paradigm of agent creation and monetiation. Let’s build the future of agents together! :raising_hands:

To help you answer these questions, here’s an example for your reference:


1. Agent name: E‑commerce Dynamic Repricing Bot

Target users:
Small/mid‑size Amazon FBA sellers and brands
Especially those with many SKUs and tight margins

Why someone would pay:
Today they either change prices manually or use rigid rule‑based tools, which lose them the Buy Box or margin
This Agent keeps prices competitive while respecting minimum margin rules, so they stop “racing to the bottom” on price

Competitors:
RepricerExpress, Seller Snap, etc.

Differentiator:
More AI‑driven strategy and explanations, not just static rules


2. Skills needed

Browser / Amazon front‑end price fetch
What it does: scrape competitor prices, stock levels, Buy Box status
Example skill: Browser Agent (e.g. https://skillsmp.com/skills/browser-agent)

Spreadsheet / Excel for margin calculation
What it does: calculate min/target prices from COGS, FBA fees, ad spend, etc.
Example skill: Excel Agent (e.g. https://skillsmp.com/skills/excel-agent)

Amazon SP‑API integration
What it does: read / update listing prices and inventory through SP‑API
Example skill: custom SP‑API price update plugin (self‑hosted or internal)

P.S. Knowledge / data needed:
Per‑SKU COGS
FBA fee tables by category / region
Seller’s own rules: minimum margin, max discount, competitors to ignore


3. Feature / infra needs

Scheduler / cron‑style triggers
Per‑SKU / per‑store configs: run every X minutes / hours

Webhook / notification on price change
Push large price changes or margin‑floor events to Discord / email / webhook

Multi‑env config
Separate API keys and store configs for sandbox vs production

If this tool can save me a lot of time and deliver results that mostly meet my needs, I’d be very willing to pay for it—just like the ad poster generator and KV/product detail page builder previously offered for e-commerce sellers.

That said, I hope MuleRun can polish these tools further and keep documentation up to date, so we don’t have to ask in the group every time. Also, please streamline the review process: if there are issues, kindly list them all at once instead of bouncing submissions back and forth. Otherwise, it really kills our motivation to submit.

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Hi @AFei, Thanks so much for being part of the community!

Just a quick note: since the MuleRun Community is global, we try to keep discussions in English so everyone can follow along, so I’ve translated your comment for visibility.

Going forward, could you please use English in the community? We really appreciate it! :blush:

P.S. The blog has both English and Chinese versions so it’s easier for Chinese creators to read, but for discussion in the community we still prefer English.

A worthwhile Agent = High-value scenarios × Deep professional capabilities × Good experience

Required skills include LLM capabilities, software engineering, domain knowledge, tool integration, data engineering, product design, security and compliance, etc.

Core functional requirements include debugging and observability, deployment and operations, security and permissions, templates and marketplace, etc

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I believe AI agents should exist to make everyday life easier. Stuff like helping people find a place to live, get food, deal with work, make games, manage investments, or take care of their health. Entertainment is cool, but that’s not the point. The real value is when agents step into daily life and take real friction and stress off people’s hands.

Otherwise it’s just another demo, NVM:).

LLMs are already pretty good at reasoning, planning, writing, and coding. But that alone doesn’t solve real life problems. What’s missing is the connection to the real world.

Agents need practical skills so they can actually operate inside systems, access services, and work across domains. These aren’t things humans need to click through. Think of them as apps built for agents, so they can act instead of just talking. LMAO at how many AI products still stop at only chat.

That’s why we need a way to connect agents to the human world the same way apps connect humans to services today. A builder where skills, tools, and interfaces are easy to wire together. The goal is simple. Agents shouldn’t just be interesting. They should be useful, reliable, and something people actually use every day.

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Agent idea: a personal “X posting teammate” that actually thinks

I post on X almost every day.
Writing isn’t the hard part anymore. Deciding what to post and why now is.

After a while you realize posting is a full workflow:
seeing what’s happening → choosing an angle → drafting → deciding when to post → checking what worked → adjusting next time.

Right now this whole decision process lives in my head (or random notes). I want an agent that works like a real content teammate, not just a text generator.


1) What kind of agent is truly worth paying for?

Agent name (working title): X Trend-to-Post Agent

Who it’s for:

  • Builders, indie hackers, founders, open-source maintainers

  • Anyone doing “build in public” or trying to grow authority on X

Why I’d pay for it:
Most tools help me write faster.
Very few help me decide better.

What actually costs time:

  • scanning trends and conversations

  • figuring out which ones fit my audience and voice

  • avoiding repetitive or generic takes

  • remembering what already worked and why

If an agent can save me ~30–60 minutes a day and improve hit rate, that’s an easy yes.

How this is different from existing tools:

  • Not “write a tweet about X”

  • But “given today’s context, here are 3 angles you should talk about, and here’s why”

I want the agent to:

  • explain its reasoning (“this worked before”, “your audience reacted well to this format”)

  • keep track of past decisions instead of starting from scratch every day

  • learn my style over time instead of forcing templates


2) What skills does this agent need?

From a creator POV, these are the minimum skills I’d expect.

Trend sensing

  • Monitor selected topics / accounts / keywords

  • Cluster what people are actually discussing

  • Filter out noise and recycled takes

Pattern extraction

  • Look at high-engagement posts and extract patterns, not copy text

  • Hooks, structures, narrative styles that worked recently

Style-aware drafting

  • Generate multiple drafts with constraints

  • Respect tone, length, emoji usage, CTA style

  • Avoid repeating my own old posts or sounding “LLM-ish”

Originality guardrails

  • Make sure drafts are not close paraphrases of source posts

  • Force meaningful angle shifts when needed

Scheduling & publishing

  • Queue posts or send to a scheduler

  • Support “review before posting” (I still want control)

Learning loop

  • Pull engagement results

  • Summarize what worked / didn’t

  • Update future suggestions based on real performance

Knowledge it should use:

  • My past posts (including what flopped)

  • A short “voice guide” I can edit over time

  • My goals (growth vs product launch vs discussion)


3) What would make this easy to build in Mule Agent Builder?

These are the things that would actually change the game for this kind of agent.

Decision trace as a first-class concept
I want to see and store:

  • what sources it looked at

  • why it picked this topic

  • why it chose this angle
    So over time this becomes a personal content “memory”, not just logs.

Reliable execution for research steps
Trend scanning, clustering, filtering should feel deterministic and fast.
This feels like a perfect use case for programmatic execution instead of multi-step chat reasoning.

Human-in-the-loop by default
Approve / reject drafts
Regenerate with same idea, different angle
Small edits without breaking constraints

Simple scheduling primitives
Daily / weekday schedules
Post spacing rules
Queue management without me thinking about timing

Multiple personas / accounts
Personal account ≠ company account
Different voice, different topics, different boundaries


Why I want to build this with Mule Agent Builder

This kind of agent is hard to build with pure workflows, but also overkill to hand-roll with frameworks if you’re not an infra person.

The “Base Agent + Skills + Knowledge + Runtime” model feels right here:

  • strong reasoning loop

  • reusable skills

  • persistent memory

  • production-grade runtime

If Mule Agent Builder can make this feel like “training a smart teammate” instead of “assembling tools”, I’d love to be part of early testing and give feedback from a real daily-posting workflow.

Happy to share more concrete specs, examples, or even my own posting data if helpful.

1 Like

Product Positioning: “FloraAI” — Your AI Floral Design Mentor


  1. Core Capabilities

A. Intelligent Recognition & Recommendations

  • Photo-based Flower Recognition: Identify flower species, freshness levels, and suitable applications
  • Scene-based Matching: Recommend arrangements based on space (living room, bedroom) and occasions (birthday, anniversary)
  • Smart Color Analysis: Provide harmonious color pairing suggestions based on color theory

B. Step-by-Step Guidance

  • Progressive Tutorials: Complete workflow from flower prep → trimming → arranging → care
  • Real-time Correction: Users upload work photos, AI identifies issues (uneven heights, color imbalance, etc.)
  • AR-Enhanced Assistance (Advanced feature): Camera overlay showing arrangement guidance paths

C. Personalized Growth Journey

  • Skill Level System: Beginner → Intermediate → Master, unlocking progressively complex designs
  • Portfolio Management: Track each creation, generate improvement curves
  • Community Sharing: Showcase works, receive feedback, participate in challenges

D. Practical Tools

  • Flower Shopping Lists: Generate shopping lists based on designs, linked to e-commerce platforms
  • Care Reminders: Push notifications for water changes and trimming based on flower characteristics
  • Seasonal Flower Calendar: Recommend cost-effective seasonal flowers

  1. Target Audience Analysis

Primary Segments:

  1. Novice Enthusiasts (70%)
  • Predominantly female, ages 25-40
  • Seek aesthetic lifestyle but lack professional training
  • Pain Points: Don’t know where to start, fear wasting flowers, lack aesthetic confidence
  1. Light Practitioners (20%)
  • Have attempted floral arrangement 2-3 times
  • Want systematic improvement but reluctant to attend classes (time/cost barriers)
  • Pain Points: Lack continuous guidance, slow progress
  1. Special Occasion Users (10%)
  • Need DIY arrangements for specific events (weddings, store openings)
  • Pain Points: Need to quickly master specific styles

  1. Design Logic

User Journey Design:

Phase 1: Zero-to-One Entry (Lower Psychological Barriers)
User Entry → 5-min Quick Assessment (aesthetic preferences, available time, budget)
→ Recommend 3 Super Simple Designs (3-5 flower types, 10-min completion)
→ First Creation Complete → AI Praise + Improvement Tips → Spark Achievement

Phase 2: Skill Building (Establish Habits)
Weekly New Design → Introduce New Techniques (spiral method, negative space art)
→ Compare with previous week, visualize progress
→ Unlock Badges (e.g., “Color Maestro”)

Phase 3: Deep Engagement (Monetization Conversion)
Free Users: Basic tutorials + 3 AI analyses/month
Premium Users: Unlimited analysis + Advanced courses + 1-on-1 reviews + Purchase discounts

Interaction Logic:

  • Reduce Cognitive Load: Avoid jargon, use “let flowers breathe” instead of “negative space principle”
  • Immediate Feedback: Every step includes encouraging feedback like “Well done!” or “Try this…”
  • Visual-First: Use diagrams and video clips instead of lengthy text

  1. Required Skills (Technical Capabilities)

Essential Skills:

  1. Visual Understanding Skill
  • Flower recognition (CV model trained on floral datasets)
  • Composition analysis (arrangement evaluation, color scoring)
  • Freshness assessment
  1. Knowledge Retrieval Skill
  • Flower language/symbolism queries
  • Flower characteristics database (shelf life, water requirements, incompatible pairings)
  • Occasion/holiday scenario library
  1. Teaching Planning Skill
  • Generate personalized curricula based on user level
  • Difficulty gradient control (Bloom’s Taxonomy approach)
  • Error diagnosis and correction strategies
  1. E-commerce Integration Skill
  • Flower price comparison
  • One-click shopping cart generation (integrate with floral e-commerce APIs)
  1. Social Interaction Skill
  • Personalized work commentary generation (avoid templated responses)
  • Community content moderation

Advanced Skills:

  • AR Rendering Skill: Preview design plans in real space via AR
  • Trend Analysis Skill: Scrape Instagram, Pinterest for trending styles, push trendy designs

  1. Why Users Will Pay

Value Proposition:

  1. Cost Savings
  • Offline floral classes: $30-80 per session + commute time
  • AI Assistant: $1.99/month subscription, learn anytime anywhere
  • Mental Accounting: Savings from avoided classes justify 1-year membership
  1. Tangible Outcomes
  • Every creation is Instagram-worthy → Social currency
  • Fresh flowers at home → Quality of life upgrade → Self-identity reinforcement
  1. Sunk Cost Lock-in
  • Already uploaded 20 works, 3 months of data → Reluctant to abandon
  • Leveling system (2 levels away from “Master”) → Drives renewal

Feature Differentiation:

Free Tier Limitations:

  • Only 3 AI analyses per month
  • Access to basic tutorials only (10 designs)
  • Cannot participate in community challenges

Premium Tier Unlocks:

  • Unlimited AI reviews (core value)
  • 100+ advanced tutorials (including advanced techniques)
  • Exclusive 1-on-1 designer review (1x/month)
  • E-commerce platform exclusive discounts (revenue sharing)
  • Priority access to new features (e.g., AR preview)

Emotional Drivers:

Identity Alignment: Premium members = “People who take life seriously”

Scarcity: Limited-time early bird pricing, member-exclusive flower boxes

Achievement Display: Member-exclusive badges, works tagged with “PRO” badge


  1. Pricing Strategy

Free Tier: Experience core features, build trust

Light Subscription: $1.99/month or $19.99/year (anchor pricing)

Standard Membership: $4.99/month or $49.99/year (recommended)

Premium Membership: $9.99/month (includes offline class vouchers, monthly flower box)

Conversion Funnel:

  • New users get 7-day premium trial → Experience “unlimited AI analysis” delight
  • Day 6 push: Renew at 50% off, only 24 hours
  • Non-converters: Push “We’ve saved you $XXX in class fees” re-engagement

  1. Competitive Moats

  2. Data Flywheel: More user creations → Better AI assessment → More precise recommendations → Higher user dependency

  3. Content Moat: Localized floral knowledge (e.g., Japanese Ikebana, Western contemporary styles, seasonal guides)

  4. Network Effects: User-generated content (UGC) creates community atmosphere, reduces customer acquisition costs


  1. Success Metrics (KPIs)

Acquisition:

  • CAC (Customer Acquisition Cost) < $5
  • Conversion rate from free to paid: >15%

Engagement:

  • Weekly active users (WAU) retention: >40%
  • Average creations per user per month: >2

Monetization:

  • Free-to-paid conversion: >10% within 30 days
  • Annual LTV (Lifetime Value): >$60
  • Churn rate: <5% monthly

  1. Go-to-Market Strategy

Phase 1: MVP Launch (Months 1-3)

  • Target: 10,000 users, validate core value proposition
  • Channels: Instagram influencer partnerships, Pinterest ads
  • Focus: Perfect the “first creation in 10 minutes” experience

Phase 2: Community Building (Months 4-6)

  • Target: 50,000 users, 5% paid conversion
  • Launch: Weekly challenges, user showcase gallery
  • Partnerships: Collaborate with 2-3 flower delivery services for discounts

Phase 3: Premium Expansion (Months 7-12)

  • Target: 100,000 users, 10% paid conversion
  • Launch: AR features, advanced masterclasses
  • Strategy: Referral program (both parties get 1-month free premium)

Summary

FloraAI’s Core Innovation: Use AI to eliminate the intimidation factor of floral design (recognition + teaching), use gamified progression to build habits, use tangible outcomes (creations + social validation) to drive payments, ultimately creating a closed loop of “Learn → Create → Share → Pay.”

The Key Insight: Solve the psychological barrier of “I want to learn but fear it’s too complicated/I’ll fail” by delivering the first success within 10 minutes and building dependency within 1 month.

Why This Wins:

  • Accessibility beats traditional classes (time, cost, location)
  • AI personalization beats YouTube tutorials (generic, no feedback)
  • Community + portfolio beats books (motivation, accountability)
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1.Email Manager Agent

An email manager agent that manages your inbox 24/7 and performs all email-related tasks—sending, drafting, reading, summarizing, replying—based on your input. It notifies you when new emails arrive and can be controlled through third-party chat apps like WeChat, Telegram, and WhatsApp (if Mulerun supports it). A dashboard provides full monitoring and configuration: add system instructions, specify your personal writing style, or set emails to ignore.

Target users: Working professionals, entrepreneurs, and anyone dealing with emails daily.

Why they’d pay: There’s nothing like this in the market. It makes work much easier—no repeatedly checking emails, no worrying about missing important information. Users get full personal configuration control, so they’ll definitely pay for it.

Competitors & why this is better:

  • SaneBox ($7-36/month) - Only filters emails, no AI replies

  • Superhuman ($30/month) - Fast client, but YOU write emails manually

  • Missive ($14-26/month) - Team inbox, no autonomous agent

  • Mailbutler ($9.95-49.95/month) - Templates only, no intelligent auto-responses

  • EmailTree AI (Enterprise) - Customer support only, not personal use

Key differentiator: Competitors are assistants (you work faster). This is an autonomous agent (works FOR you 24/7) with cross-platform control via WhatsApp/Telegram and personalized writing style—none offer this combination.

2.Essential Skills for Email Manager Agent:

1. RAG (Retrieval-Augmented Generation)

2. Email Writing Skills

  • Professional/business tone writing

  • Casual/friendly tone writing

  • Apology and follow-up drafting

  • Polite rejection/decline messages

  • Copying the style of the previous user’s reply.

3. Summarization

  • Condense long emails into 2-3 lines

  • Create daily digest from multiple emails

  • Generate thread summaries

4. Smart Reply Generation

  • Quick responses: “Thanks!”, “Acknowledged”, “Will do”

  • Context-aware options (meeting invites, questions)

5. Calendar Integration

  • Add meetings from emails to calendar

  • Check availability and suggest time slots

  • Handle reschedule requests

6. Task Extraction

  • Detect action items from emails

  • Auto-create to-do lists with deadlines

  • Track pending tasks

7. Contact Management

  • Auto-save new contacts

  • Merge duplicates and maintain VIP list

3.Features & Infrastructure Needed in MuleRun Agent Builder:

1. Third-Party Messaging Integration System

I need a simple way to connect WhatsApp, Telegram, and WeChat without dealing with complex APIs manually. The builder should handle OAuth/webhook setup automatically—just authorize the app and it’s done. A unified messaging hub would be great so I can manage all platforms from one place and test commands before going live.

2. Event-Driven Agent Trigger Infrastructure

This is crucial. The agent shouldn’t need to run 24/7 wasting resources. Instead, implement a push-based architecture where the agent only activates when there’s actual input—new email arrives, user sends a command via WhatsApp, whatever. Think serverless functions: input comes in → system pushes it to agent → agent processes → goes back to sleep. This keeps costs down and makes the system scalable. We need webhook listeners and a queue system that can handle asynchronous triggers reliably.

3. Customizable End-User Dashboard Builder

Since I’m building this for others to use, I need to give them a clean interface to control the agent. The builder should let me create a dashboard where users can:

  • Set email filters and categories

  • Add VIP contacts

  • Configure their writing style preferences

  • Set notification rules

  • Define auto-ignore patterns

It should be drag-and-drop—no coding required. White-label support would be amazing so I can brand it as my own product. Users should see real-time activity logs (what the agent did, when) and have manual override options if they want to handle something themselves.

These three features would make building and deploying this agent actually feasible without needing a full dev team.

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