LI

lindy-migration-deep-dive

Detailed guide for migrating existing automation platforms and custom scripts to Lindy AI agents.

Install

mkdir -p .claude/skills/lindy-migration-deep-dive && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6892" && unzip -o skill.zip -d .claude/skills/lindy-migration-deep-dive && rm skill.zip

Installs to .claude/skills/lindy-migration-deep-dive

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Advanced migration strategies for moving to Lindy AI from other platforms.
74 charsno explicit “when” trigger
Advanced

Key capabilities

  • Inventory existing automation workflows
  • Classify migration complexity
  • Redesign rule-based logic into AI agent steps
  • Execute phased migration with parallel runs
  • Validate post-migration performance

How it works

It guides users through mapping legacy rule-based automations to AI-driven agent workflows, emphasizing redesign over direct translation.

Inputs & outputs

You give it
Source automation workflow details
You get back
Migrated Lindy agent and validation report

When to use lindy-migration-deep-dive

  • Migrating from Zapier zaps to Lindy agents
  • Consolidating n8n workflows
  • Replacing custom automation scripts
  • Planning migration timelines

About this skill

Lindy Migration Deep Dive

Overview

Migrate existing automation workflows from Zapier, Make (Integromat), n8n, LangChain, or custom code to Lindy AI. Key insight: Lindy replaces rigid rule-based automations with AI agents that can reason, adapt, and handle ambiguity — so migration is a redesign opportunity, not a 1:1 translation.

Prerequisites

  • Inventory of existing automations (source platform)
  • Lindy workspace ready with required integrations authorized
  • Migration timeline approved
  • Rollback plan defined for customer-facing workflows

Migration Source Comparison

Source PlatformLindy EquivalentKey Difference
Zapier ZapLindy AgentAI reasoning replaces rigid if/then
Make ScenarioLindy AgentNo-code builder instead of module chains
n8n WorkflowLindy AgentManaged infra, no self-hosting
LangChain AgentLindy Agent StepNo-code, managed, no Python needed
Custom codeHTTP Request + Run CodeLess code, AI fills gaps

Instructions

Step 1: Inventory Source Automations

For each existing automation, document:

FieldExample
NameSupport Email Triage
TriggerNew email in [email protected]
Steps1. Parse email 2. Classify 3. Route to channel
IntegrationsGmail, Slack, Sheets
Frequency~50 runs/day
ComplexityMedium (3 steps, 1 condition)

Step 2: Classify Migration Complexity

ComplexityCriteriaMigration ApproachTime
Simple1-3 steps, no conditionsBuild from scratch in Lindy30 min
Medium4-8 steps, conditionsNatural language description to Agent Builder1-2 hours
Complex9+ steps, multi-branch, loopsRedesign as multi-agent society1-2 days
Custom codePython/JS logicRun Code action + HTTP Request2-4 hours

Step 3: Migration Strategy by Source

From Zapier:

Zapier Pattern → Lindy Pattern
────────────────────────────────
Trigger (New Email) → Trigger (Email Received)
Filter Step → Trigger Filter (more efficient)
Formatter → AI Prompt field mode (AI does formatting)
Lookup → Knowledge Base search or HTTP Request
Multi-step Zap → Single agent with conditions
Paths → Conditions (natural language branching)

From Make (Integromat):

Make Pattern → Lindy Pattern
────────────────────────────────
Scenario → Agent workflow
Module → Action step
Router → Conditions
Iterator → Loop
Aggregator → Run Code action (consolidation logic)
Error Handler → Agent prompt error instructions

From n8n:

n8n Pattern → Lindy Pattern
────────────────────────────────
Trigger Node → Trigger
Function Node → Run Code (Python/JS)
HTTP Request Node → HTTP Request action
IF Node → Condition
Merge Node → Agent step (AI merges intelligently)

From LangChain/Custom Code:

LangChain Pattern → Lindy Pattern
────────────────────────────────
Agent → Agent Step with skills
Tool → Action or HTTP Request
Memory → Lindy Memory (persistent)
Chain → Workflow steps
Vector Store → Knowledge Base
Retrieval Chain → Knowledge Base + AI Prompt

Step 4: Execute Migration (Phased)

Phase 1: Internal-Only Agents (Days 1-3)

  1. Migrate non-customer-facing automations first
  2. Build in Lindy using natural language description
  3. Test with real data for 48 hours
  4. Compare output quality to source automation
  5. Decommission source automation after verification

Phase 2: Low-Risk Customer-Facing (Days 4-7)

  1. Build Lindy agent alongside existing automation (parallel run)
  2. Route 10% of traffic to Lindy agent
  3. Compare results for 48 hours
  4. Gradually increase to 50%, then 100%
  5. Monitor task success rate and response quality

Phase 3: Critical Workflows (Days 8-14)

  1. Build Lindy agent as exact replacement
  2. Test extensively with staging data
  3. Schedule cutover during low-traffic window
  4. Keep source automation pausable (not deleted) for 7 days
  5. Monitor closely for 48 hours post-cutover

Step 5: Redesign Opportunities

Migration is a chance to improve, not just replicate:

Old PatternLindy Improvement
Rigid if/then classificationAI classifies naturally, handles edge cases
Template-based email responsesAI generates contextual, personalized responses
Multiple automations for variationsSingle agent with conditions handles all
Manual data transformationRun Code action or AI handles transformation
No error handlingAgent prompt includes fallback behavior

Step 6: Validate and Cutover

# Post-migration validation checklist
echo "=== Migration Validation ==="

# 1. Task completion rate
echo "Check: Agent Tasks tab - expect >95% success rate"

# 2. Response quality
echo "Check: Compare 10 agent outputs to old automation outputs"

# 3. Trigger coverage
echo "Check: All events triggering correctly (no missed events)"

# 4. Performance
echo "Check: Task duration within acceptable range"

# 5. Cost
echo "Check: Credit consumption within budget"

Migration Checklist

  • Source system inventory complete
  • Each automation classified by complexity
  • Lindy integrations authorized
  • Phase 1 (internal) agents migrated and verified
  • Phase 2 (low-risk) agents running in parallel
  • Phase 3 (critical) agents tested with staging data
  • Cutover window scheduled
  • Rollback procedure tested
  • Source automations paused (not deleted)
  • 7-day post-cutover monitoring complete
  • Source automations decommissioned

Error Handling

IssueCauseSolution
Output quality lowerAI prompt needs tuningAdd few-shot examples to agent prompt
Missing edge casesSource had specific rulesAdd condition branches or prompt instructions
Higher cost than expectedOveruse of large modelsRight-size models per step
Integration auth failsOAuth not set up in LindyAuthorize integrations before migration
Data format mismatchDifferent field namesMap fields in Run Code action

Resources

Next Steps

This completes the Flagship tier. Review Standard and Pro skills for comprehensive Lindy mastery.

When not to use it

  • When the existing automation is already performing optimally without AI reasoning needs

Prerequisites

Inventory of existing automationsLindy workspace with authorized integrations

Limitations

  • Requires manual validation of agent output quality
  • Complex workflows may require multi-agent societies

How it compares

It treats migration as a redesign opportunity to use AI reasoning instead of performing a 1:1 translation of rigid logic.

Compared to similar skills

lindy-migration-deep-dive side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
lindy-migration-deep-dive (this skill)125dReviewAdvanced
webapp-testing3533moReviewIntermediate
resolve-conflicts818moReviewIntermediate
telegram-bot-builder1066moReviewIntermediate

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