coefficient-portfolio

Reddit & Community Engagement Automation

A three-agent AI system that automates community engagement across Reddit and Salesforce Trailblazer Community. The system collects posts, analyzes product applicability, and generates context-aware responses for human review before posting.

Architecture

Agent 1: Planner-Researcher
    → Collects posts from Reddit/Trailblazer
    → Writes to "Raw Collection" sheet
    ↓
Agent 2: Product Specialist
    → Analyzes each post for product applicability
    → Drafts solution-first responses (3-5 actionable specifics)
    → Writes applicable posts to "Product Analysis" sheet
    → Cleans Raw Collection sheet
    ↓
Agent 3: Analyst & Copywriter
    → Analyzes existing thread comments before refining
    → Classifies thread types and promotional sensitivity
    → Adapts response tone, length, and product positioning
    → Writes context-refined responses to "Final Refined" sheet

Pipeline Stages

Stage 1: Data Collection (Agent 1)

• Collects posts from 12+ subreddits or Trailblazer topics
• Handles two-stage collection: API-first with Browserbase fallback for truncated content
• Preserves exact post titles and full descriptions
• Detects and fixes truncated posts automatically

Stage 2: Applicability Analysis (Agent 2)

• Reads posts from Raw Collection sheet
• Analyzes each for product relevance using connector cheat sheets
• Drafts detailed, solution-first responses
• Removes non-applicable posts (mandatory)
• Generates post-mortem with relevance statistics

Stage 3: Comment Context Intelligence (Agent 3)

• Retrieves and analyzes existing thread comments
• Classifies thread type: ACTIVE_ENGAGED, TECHNICAL_DEEP, CASUAL_BRIEF, PROMOTIONAL_SENSITIVE, SOLUTION_CROWDED, DEAD_THREAD
• Adapts response strategy based on sensitivity:
  • High sensitivity: Help first, product second
  • Medium sensitivity: Acknowledge + alternative positioning
  • Low sensitivity: Direct recommendation
  • Complex native: Simpler workaround approach
• Ensures product is mentioned and hyperlinked in every response

Agent Specs

Agent	Role	Input	Output	Key Tools
Planner-Researcher	Data collection lead	Platform, category, date range	Raw Collection sheet	Reddit MCP, Browserbase, Google Sheets MCP
Product Specialist	Technical product expert	Raw Collection posts	Product Analysis sheet (applicable only)	Connector cheat sheets, Google Sheets MCP
Analyst & Copywriter	Context analysis + refinement	Product Analysis posts + thread comments	Final Refined sheet	Reddit MCP (comments), Google Sheets MCP

Key Innovation: Comment Context Intelligence

The system’s differentiator is analyzing existing thread comments before generating responses:

1. Thread tone detection — casual, professional, technical, humorous
2. Response length pattern matching — adapts to community norms
3. Promotional sensitivity scoring — prevents downvoted/perceived-promotional content
4. Existing solution identification — acknowledges what’s already been suggested
5. OP engagement level assessment — determines if thread is still active

Three-Sheet Persistent Pipeline

• Sheet 1 “Raw Collection” — Temporary staging, cleaned after each run
• Sheet 2 “Product Analysis” — Only applicable posts with drafted responses
• Sheet 3 “Final Refined” — Human-reviewable, context-aware responses ready for posting

Each stage produces auditable data with explicit handoffs and verification counts.

Data Columns (Preserved Across Sheets)

Column	Description
Week	Time period searched
Forum	Subreddit or Trailblazer topic
Connector	Platform name
Topic	Original post title
Description	Full post content (no truncation)
URL	Direct link
Usecase / Integration	Identified use case (Agent 2)
AI Draft	Response (drafted by Agent 2, refined by Agent 3)

Platform Coverage

Reddit: 12+ subreddits across CRM, analytics, accounting, operations, and sales communities

Trailblazer Community: Analytics, Reports & Dashboards, Automation, Integration, Data Management topics

Results

• 100% content completeness (no truncated posts)
• Zero data loss between agents (verified handoffs)
• Thread-appropriate tone matching across all responses
• Promotional sensitivity adaptation based on community patterns
• Human review gate before any content is posted

Stack

• Agents: Claude Code multi-agent (3 agents, sequential pipeline)
• Data collection: Reddit MCP, Browserbase MCP (fallback)
• Data storage: Google Sheets MCP (3-sheet architecture)
• Response quality: Connector cheat sheets (60+ product integrations as reference)

Key Decisions

1. Three-sheet architecture over database — Google Sheets chosen for transparency and human reviewability. Each sheet represents a pipeline stage with explicit handoffs.
2. Comment analysis before response generation — Prevents tone-deaf or overly promotional responses by matching community patterns.
3. Progressive fallback for data collection — Reddit API first, Browserbase only when content is truncated. Minimizes cost while ensuring completeness.
4. Mandatory product mention with adaptive strategy — Product is always mentioned but positioning adapts to thread sensitivity level.

Visual Direction

Diagram: Three-lane swim diagram showing Agent 1 → Agent 2 → Agent 3 flow, with the three Google Sheets as data handoff points between lanes. Side panel shows thread classification types (ACTIVE_ENGAGED through DEAD_THREAD) with corresponding response strategies. Bottom bar shows supported platforms (Reddit subreddits + Trailblazer topics).