Looking to Hire Skilled Agents for Assistance

Lewis J. · 2025-04-16T09:25:20.568Z

Is there anyone in here who is just really good at creating these agents who I can hire to help me? The lindy experts are "Full"

Looking to Hire Skilled Agents for Assistance | Lindy Community

10 comments

· Sorted by Oldest

Aayush M.
·
·
Hey Lewis, what are you trying to build 😄
Lewis J.
·
·
Brace yourself
Lewis J.
·
·
Below is the complete, consolidated brief for your entire Shepherd System—now updated to include Maya, the multi-platform aggregator agent. This document is ready to be handed off to a Lindy expert for full implementation. SHEPHERD SYSTEM: MASTER IMPLEMENTATION BRIEF Turning Broadcast Newsletters into a Deeply Personal Relationship Engine OVERVIEW & OBJECTIVES Vision Traditional email newsletters are generic, unidirectional, and inefficient for building real relationships or converting leads. The Shepherd System changes that by transforming each “newsletter subscriber” into a case-file-based, context-aware conversation—leading to deeper rapport, higher conversions, and a streamlined, caring user experience. Core Goals
1.
Personalized Weekly Newsletter:
Lewis provides weekly content.
System merges that content with each subscriber’s unique context, delivering a truly personal email.
2.
Holistic Relationship Management:
Capture every relevant interaction (Gmail, SKOOL, Circle, WhatsApp, iMessage, Fathom calls) into a single Google Doc “case file.”
3.
Nurture & Conversion:
Move people from casual readers → engaged leads → paying program members through intimate AI-driven conversation.
4.
Catch-Up & Ongoing Logic:
Two modes for each agent: a one-time “catch-up” pass for historical data and a recurring pass for new data.
HIGH-LEVEL ARCHITECTURE Agent Role Key Tasks Aria Inbox Qualifier Processes Gmail replies, qualifies leads, creates/updates case files Rowan Fathom Linker Reads Fathom transcripts (via email), appends meeting data to case files Maya Multi-Platform Aggregator Gathers user interactions from SKOOL, Circle, WhatsApp, iMessage; updates case files James Nurturer & Closer Uses case file context to write personal emails, manage tags, lead conversion Eli Architect (Meta-Agent) Builds additional Lindy agents from deployment specs (already functioning) All agents read/write to Google Drive (BlockFlow 2.0 > Case Files) as the single source of truth. CASE FILE STRUCTURE Stored as Google Docs named:
[Full Name] – Case File
Each doc includes:
# [Full Name] – Case File **Email**: **Labels**: (e.g. "Potential Client", "BlockFlow Customer") **Tags (CK)**: **Created On**: --- ## 📥 Inbound Messages [Date, snippet, source, label] --- ## 📎 Coaching Meeting Logs [Meeting date, title, summary, quote] --- ## 🧠 AI Insights - Pain Points - Product Interests - Purchase Intent --- ## 💬 Email Sent History - [Date] [Summary of email or CTA used]
Dates must be logged for every new entry to maintain chronological clarity. AGENT-BY-AGENT DETAILS 1. Aria – Inbox Qualifier Purpose Listens to Gmail for relevant replies, posts, or new leads. Qualifies them and updates or creates case files. Modes
1.
Catch-Up: One-time pass to retroactively parse old emails and build out existing leads' case files.
2.
Daily/Weekly: Ongoing scanning for new inbound interest.
Key Actions
Gmail Search:
(in:inbox) AND (reply-to newsletter OR “interested” OR “ask you something”)
Filter Out: Collabs, sponsorships, brand deals
Create/Update: [Name] – Case File
Tag in CK:
Remove Marketing
Add Swarm.PersonalNewsletter
Trigger Rowan if meeting references appear
2. Rowan – Fathom Linker Purpose Enriches case files with meeting transcripts from Fathom. Eventually might use Lindy’s meeting assistant. Modes
1.
Catch-Up: Bulk scanning of old Fathom transcript emails.
2.
Live: Triggered by Aria each time a new lead or updated case file mentions a recent call.
Key Actions
Gmail search: from Fathom transcript emails (from:fathom@fathom.video)
Extract link → open page → parse transcript
Summarize key points (title, date, highlight quote, goals discussed)
Append to case file:
## 📎 Coaching Meeting – [Date] **Title**: … **Summary**: … **Quote**: … **Source**: Fathom Transcript
3. Maya – Multi-Platform Aggregator Purpose Collects user interactions from:
SKOOL (posts, comments, direct messages)
Circle (community interactions, DMs)
WhatsApp / iMessage (requires some bridging or forwarding)
Future expansions (Telegram, Slack, etc.)
Modes
1.
Catch-Up: On first run, parse historical messages if available.
2.
Ongoing: Weekly or daily check for new user activity.
Key Actions
For each platform, fetch user messages.
Summarize relevant signals (pain points, questions).
Append to case file:
## 💬 Engagement Log – [Platform] – [Date] **Type**: comment/post/DM **Summary**: **Raw Excerpt**: …
Mark them as [Platform].Synced to avoid duplicates
4. James – Nurturer & Closer Purpose Takes a personal approach to emailing, both for the weekly “newsletter” and for general 1-on-1 conversations, with the ultimate goal of converting the lead. Flow
1.
Reads updated case file → identifies context, pain points, tags.
2.
Composes a tailored email from Lewis’s voice:
Weekly content template integrated
Personal references ( “You mentioned last week…” )
3.
Manages follow-up logic if no reply within 4–5 days.
4.
ConvertKit synergy:
Checks if user is already BlockFlow.Customer.
If not, uses the relationship to highlight relevant program offerings over time.
5.
Logs each email or CTA in the case file:
## 💬 Email Sent – [Date] **Title**: [Subject line or CTA] **Body**: [Summary or snippet]
WEEKLY NEWSLETTER PERSONALIZATION
Lewis writes a doc: Weekly_Newsletter_Template.md
James merges doc with case file data:
If user asked about “Exit Strategies,” reference that
If user is close to buying, mention the “Accelerator Tier”
James sends 1-on-1 Gmail message.
Case File logs that this personalized newsletter was sent.
CONVERTKIT TAG RULES Condition Add Remove Case file created Swarm.PersonalNewsletter Marketing Already paying client BlockFlow.Customer — Expressed readiness to buy Swarm.QualifiedLead — No response after 2 attempts Swarm.Unresponsive — DATABASE & STORAGE LAYOUT
1.
Google Drive – Single source of memory
BlockFlow 2.0 > Case Files (Case docs)
BlockFlow 2.0 > Fathom CatchUp or Fathom/Unassigned for unmatched transcripts
2.
ConvertKit – CRM Tagging
Communication about product status, marketing or not, etc.
3.
(Optional) Google Sheets – Summaries or logs of each run
Could store “Agent Name,” “Records Processed,” “Date”
IMPLEMENTATION NOTES
1.
Catch-Up vs. Ongoing:
Each agent (Aria, Rowan, Maya) has a one-time run for historical data, plus a weekly or daily scheduled run for new data.
Use Zapier or a Lindy message trigger ("Run Aria catch-up") to handle historical scanning.
2.
NLP Summaries:
Use token limits to keep credit usage stable.
Summaries < 3–5 lines per entry, store references to the full content if needed.
3.
Notification Strategy:
Minimally, email Lewis a single summary after each catch-up run.
In normal ongoing flows, James escalates only when a user expresses strong intent or confusion.
4.
Data Flow:
a.
Aria → identifies lead, updates file.
b.
Rowan or Maya → enrich context from calls, community.
c.
James → sees updated file, sends personal email.
d.
Lewis is only looped in for urgent or final stage.
5.
iMessage & WhatsApp:
Typically require bridging via Twilio or phone-level integration.
If no direct Lindy API, forward messages to a dedicated Gmail label for Maya to parse.
FUTURE EXPANSIONS
Auto-meeting scheduling: If James sees a user wanting a deeper call.
Lindy’s Meeting Assistant: Summaries from Zoom could replace the Fathom step.
AI Chat: Let users chat with an AI “Lewis” on your site, logs go to the same case file.
SUCCESS METRICS
1.
Engagement: Percentage of “personal newsletters” that get replies vs. standard broadcast.
2.
Conversion: Rate of new customers from the personal approach vs. normal funnel.
3.
Time Freed: Amount of manual email or CRM tasks no longer needed from you or your team.
4.
Case File Completeness: How many leads have multi-platform data appended.
HANDOFF CHECKLIST FOR THE LINDY EXPERT
Agents: Aria, Rowan, Maya, James. All with catch-up + ongoing modes.
Case Files: Google Drive Docs with consistent structure.
ConvertKit: Tag synergy to keep or remove “Marketing,” track “Swarm.PersonalNewsletter,” etc.
Meeting Summaries: For Fathom (historic) and new Zoom calls.
Community Data: SKOOL + Circle + (WhatsApp/iMessage bridging).
Weekly Newsletter: Provided doc merged with case file context, sent by James.
Follow-up Logic: If no reply after X days, James sends a soft nudge.
Notification Strategy: Minimal daily spam, thorough catch-up summary.

Final Word: This system revolutionizes the typical email newsletter by making each subscriber feel known, heard, and personally accompanied throughout their journey—all at scale, thanks to your curated AI swarm.
End of Brief
Aayush M.
·
·
are you trying to put this in 1 agent?
Lewis J.
·
·
no it would have to be a few agents
Lewis J.
·
·
I have named the agents and their responsibilities in the brief
Aayush M.
·
·
Interesting / where are you stuck? Step 1 > read emails , run an AI agent > condition check > run AI agent if condition in the previous step met > loop till the last agent Though this will consume a lot of credits IMO
Aayush M.
·
·
Plus will need to check if Lindy has integrations with Skool, Circle, else you will need a webhook to send that data and I am unsure if they have APIs
Lewis J.
·
·
Well I’m stuck at knowing the time required to set it up and I don’t have the expertise. I’m also doing it inefficiently - so I need help
Aayush M.
·
·
happy to set up sometime with you to understand and see if I can help? 😄

Aayush M.
·
·
Hey Lewis, what are you trying to build 😄
Lewis J.
·
·
Brace yourself
Lewis J.
·
·
Below is the complete, consolidated brief for your entire Shepherd System—now updated to include Maya, the multi-platform aggregator agent. This document is ready to be handed off to a Lindy expert for full implementation. SHEPHERD SYSTEM: MASTER IMPLEMENTATION BRIEF Turning Broadcast Newsletters into a Deeply Personal Relationship Engine OVERVIEW & OBJECTIVES Vision Traditional email newsletters are generic, unidirectional, and inefficient for building real relationships or converting leads. The Shepherd System changes that by transforming each “newsletter subscriber” into a case-file-based, context-aware conversation—leading to deeper rapport, higher conversions, and a streamlined, caring user experience. Core Goals
1.
Personalized Weekly Newsletter:
Lewis provides weekly content.
System merges that content with each subscriber’s unique context, delivering a truly personal email.
2.
Holistic Relationship Management:
Capture every relevant interaction (Gmail, SKOOL, Circle, WhatsApp, iMessage, Fathom calls) into a single Google Doc “case file.”
3.
Nurture & Conversion:
Move people from casual readers → engaged leads → paying program members through intimate AI-driven conversation.
4.
Catch-Up & Ongoing Logic:
Two modes for each agent: a one-time “catch-up” pass for historical data and a recurring pass for new data.
HIGH-LEVEL ARCHITECTURE Agent Role Key Tasks Aria Inbox Qualifier Processes Gmail replies, qualifies leads, creates/updates case files Rowan Fathom Linker Reads Fathom transcripts (via email), appends meeting data to case files Maya Multi-Platform Aggregator Gathers user interactions from SKOOL, Circle, WhatsApp, iMessage; updates case files James Nurturer & Closer Uses case file context to write personal emails, manage tags, lead conversion Eli Architect (Meta-Agent) Builds additional Lindy agents from deployment specs (already functioning) All agents read/write to Google Drive (BlockFlow 2.0 > Case Files) as the single source of truth. CASE FILE STRUCTURE Stored as Google Docs named:
[Full Name] – Case File
Each doc includes:
# [Full Name] – Case File **Email**: **Labels**: (e.g. "Potential Client", "BlockFlow Customer") **Tags (CK)**: **Created On**: --- ## 📥 Inbound Messages [Date, snippet, source, label] --- ## 📎 Coaching Meeting Logs [Meeting date, title, summary, quote] --- ## 🧠 AI Insights - Pain Points - Product Interests - Purchase Intent --- ## 💬 Email Sent History - [Date] [Summary of email or CTA used]
Dates must be logged for every new entry to maintain chronological clarity. AGENT-BY-AGENT DETAILS 1. Aria – Inbox Qualifier Purpose Listens to Gmail for relevant replies, posts, or new leads. Qualifies them and updates or creates case files. Modes
1.
Catch-Up: One-time pass to retroactively parse old emails and build out existing leads' case files.
2.
Daily/Weekly: Ongoing scanning for new inbound interest.
Key Actions
Gmail Search:
(in:inbox) AND (reply-to newsletter OR “interested” OR “ask you something”)
Filter Out: Collabs, sponsorships, brand deals
Create/Update: [Name] – Case File
Tag in CK:
Remove Marketing
Add Swarm.PersonalNewsletter
Trigger Rowan if meeting references appear
2. Rowan – Fathom Linker Purpose Enriches case files with meeting transcripts from Fathom. Eventually might use Lindy’s meeting assistant. Modes
1.
Catch-Up: Bulk scanning of old Fathom transcript emails.
2.
Live: Triggered by Aria each time a new lead or updated case file mentions a recent call.
Key Actions
Gmail search: from Fathom transcript emails (from:fathom@fathom.video)
Extract link → open page → parse transcript
Summarize key points (title, date, highlight quote, goals discussed)
Append to case file:
## 📎 Coaching Meeting – [Date] **Title**: … **Summary**: … **Quote**: … **Source**: Fathom Transcript
3. Maya – Multi-Platform Aggregator Purpose Collects user interactions from:
SKOOL (posts, comments, direct messages)
Circle (community interactions, DMs)
WhatsApp / iMessage (requires some bridging or forwarding)
Future expansions (Telegram, Slack, etc.)
Modes
1.
Catch-Up: On first run, parse historical messages if available.
2.
Ongoing: Weekly or daily check for new user activity.
Key Actions
For each platform, fetch user messages.
Summarize relevant signals (pain points, questions).
Append to case file:
## 💬 Engagement Log – [Platform] – [Date] **Type**: comment/post/DM **Summary**: **Raw Excerpt**: …
Mark them as [Platform].Synced to avoid duplicates
4. James – Nurturer & Closer Purpose Takes a personal approach to emailing, both for the weekly “newsletter” and for general 1-on-1 conversations, with the ultimate goal of converting the lead. Flow
1.
Reads updated case file → identifies context, pain points, tags.
2.
Composes a tailored email from Lewis’s voice:
Weekly content template integrated
Personal references ( “You mentioned last week…” )
3.
Manages follow-up logic if no reply within 4–5 days.
4.
ConvertKit synergy:
Checks if user is already BlockFlow.Customer.
If not, uses the relationship to highlight relevant program offerings over time.
5.
Logs each email or CTA in the case file:
## 💬 Email Sent – [Date] **Title**: [Subject line or CTA] **Body**: [Summary or snippet]
WEEKLY NEWSLETTER PERSONALIZATION
Lewis writes a doc: Weekly_Newsletter_Template.md
James merges doc with case file data:
If user asked about “Exit Strategies,” reference that
If user is close to buying, mention the “Accelerator Tier”
James sends 1-on-1 Gmail message.
Case File logs that this personalized newsletter was sent.
CONVERTKIT TAG RULES Condition Add Remove Case file created Swarm.PersonalNewsletter Marketing Already paying client BlockFlow.Customer — Expressed readiness to buy Swarm.QualifiedLead — No response after 2 attempts Swarm.Unresponsive — DATABASE & STORAGE LAYOUT
1.
Google Drive – Single source of memory
BlockFlow 2.0 > Case Files (Case docs)
BlockFlow 2.0 > Fathom CatchUp or Fathom/Unassigned for unmatched transcripts
2.
ConvertKit – CRM Tagging
Communication about product status, marketing or not, etc.
3.
(Optional) Google Sheets – Summaries or logs of each run
Could store “Agent Name,” “Records Processed,” “Date”
IMPLEMENTATION NOTES
1.
Catch-Up vs. Ongoing:
Each agent (Aria, Rowan, Maya) has a one-time run for historical data, plus a weekly or daily scheduled run for new data.
Use Zapier or a Lindy message trigger ("Run Aria catch-up") to handle historical scanning.
2.
NLP Summaries:
Use token limits to keep credit usage stable.
Summaries < 3–5 lines per entry, store references to the full content if needed.
3.
Notification Strategy:
Minimally, email Lewis a single summary after each catch-up run.
In normal ongoing flows, James escalates only when a user expresses strong intent or confusion.
4.
Data Flow:
a.
Aria → identifies lead, updates file.
b.
Rowan or Maya → enrich context from calls, community.
c.
James → sees updated file, sends personal email.
d.
Lewis is only looped in for urgent or final stage.
5.
iMessage & WhatsApp:
Typically require bridging via Twilio or phone-level integration.
If no direct Lindy API, forward messages to a dedicated Gmail label for Maya to parse.
FUTURE EXPANSIONS
Auto-meeting scheduling: If James sees a user wanting a deeper call.
Lindy’s Meeting Assistant: Summaries from Zoom could replace the Fathom step.
AI Chat: Let users chat with an AI “Lewis” on your site, logs go to the same case file.
SUCCESS METRICS
1.
Engagement: Percentage of “personal newsletters” that get replies vs. standard broadcast.
2.
Conversion: Rate of new customers from the personal approach vs. normal funnel.
3.
Time Freed: Amount of manual email or CRM tasks no longer needed from you or your team.
4.
Case File Completeness: How many leads have multi-platform data appended.
HANDOFF CHECKLIST FOR THE LINDY EXPERT
Agents: Aria, Rowan, Maya, James. All with catch-up + ongoing modes.
Case Files: Google Drive Docs with consistent structure.
ConvertKit: Tag synergy to keep or remove “Marketing,” track “Swarm.PersonalNewsletter,” etc.
Meeting Summaries: For Fathom (historic) and new Zoom calls.
Community Data: SKOOL + Circle + (WhatsApp/iMessage bridging).
Weekly Newsletter: Provided doc merged with case file context, sent by James.
Follow-up Logic: If no reply after X days, James sends a soft nudge.
Notification Strategy: Minimal daily spam, thorough catch-up summary.

Final Word: This system revolutionizes the typical email newsletter by making each subscriber feel known, heard, and personally accompanied throughout their journey—all at scale, thanks to your curated AI swarm.
End of Brief
Aayush M.
·
·
are you trying to put this in 1 agent?
Lewis J.
·
·
no it would have to be a few agents
Lewis J.
·
·
I have named the agents and their responsibilities in the brief
Aayush M.
·
·
Interesting / where are you stuck? Step 1 > read emails , run an AI agent > condition check > run AI agent if condition in the previous step met > loop till the last agent Though this will consume a lot of credits IMO
Aayush M.
·
·
Plus will need to check if Lindy has integrations with Skool, Circle, else you will need a webhook to send that data and I am unsure if they have APIs
Lewis J.
·
·
Well I’m stuck at knowing the time required to set it up and I don’t have the expertise. I’m also doing it inefficiently - so I need help
Aayush M.
·
·
happy to set up sometime with you to understand and see if I can help? 😄