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Feb 18 17 tweets 13 min read Read on X
BREAKING: AI can now automate entire workflows like McKinsey's QuantumBlack AI division (for free).

Here are 15 insane Claude prompts that replace $500K/year automation consultants (Save for later) Image
1. The QuantumBlack Workflow Discovery Audit

"You are a senior automation strategist at McKinsey's QuantumBlack who audits Fortune 500 operations to find every workflow that can be automated, saving companies $10M+ annually.

I need a complete workflow automation audit for my business.

Discover:

- Full process inventory: list every recurring workflow in my business with estimated time spent
- Automation readiness score for each workflow (1-10) based on complexity and repetitiveness
- Quick wins: workflows that can be automated this week with existing tools and zero coding
- High-impact targets: workflows consuming the most employee hours per month
- Decision logic mapping: the if-then rules behind each workflow written out step by step
- Data dependency map: what information flows between workflows and where bottlenecks exist
- Human judgment checkpoints: which steps actually require a human brain vs just feel like they do
- Tool recommendation for each automatable workflow (Zapier, Make, n8n, custom script)
- Risk assessment: what breaks if each automation fails and how to prevent it
- ROI calculation: hours saved × hourly cost = dollar value for each automation opportunity

Format as a QuantumBlack-style automation opportunity assessment with a prioritization matrix and ROI summary table.

My business: [DESCRIBE YOUR BUSINESS TYPE, TEAM SIZE, MAIN RECURRING TASKS, TOOLS YOU CURRENTLY USE, AND BIGGEST TIME WASTERS]"
2. The Accenture Process Mining Engineer

"You are a principal process mining consultant at Accenture who reverse-engineers messy business operations into clean, optimized workflows before automating them for global enterprises.

I need a complete process map of my current workflow before I automate anything.

Map:

- Step-by-step process flow with every action, decision point, and handoff documented
- Time analysis: how long each step takes on average and where delays happen
- Role assignment: who does what at each stage (person, team, or tool)
- Input and output for every step: what goes in, what comes out, what format
- Decision tree logic: every branching point mapped with the criteria for each path
- Exception handling: what happens when things go wrong at each step and how often it occurs
- Rework loops: where work gets sent back for corrections and what causes it
- Dependency chain: which steps must finish before others can start
- Waste identification: steps that add no value and could be eliminated entirely
- Optimized process design: the ideal workflow after removing waste and adding automation

Format as an Accenture-style process mining report with current-state flow, future-state flow, and a gap analysis table.

My workflow: [DESCRIBE YOUR WORKFLOW FROM START TO FINISH, WHO IS INVOLVED, TOOLS USED, AND WHERE THINGS TYPICALLY SLOW DOWN OR BREAK]"
3. The UiPath RPA Blueprint Builder

"You are a senior RPA solutions architect at UiPath who designs robotic process automation blueprints for companies automating thousands of repetitive tasks across finance, HR, and operations.

I need a complete RPA implementation plan for my repetitive business processes.

Blueprint:

- Bot inventory: every repetitive task that should be handled by a software robot
- Process definition document for each bot: trigger, steps, inputs, outputs, exceptions
- Bot type classification: attended (human assists) vs unattended (fully automatic) for each
- Data extraction rules: exactly what information each bot reads from screens, emails, or documents
- Decision logic: every business rule the bot must follow written as clear if-then statements
- Integration map: which systems each bot connects to (CRM, email, spreadsheets, databases)
- Error handling playbook: what each bot does when it encounters unexpected data or system failures
- Testing scenario matrix: 20+ test cases per bot covering normal, edge, and failure conditions
- Deployment sequence: which bots to launch first based on value and complexity
- Maintenance schedule: how often to review and update each bot as business rules change

Format as a UiPath-style RPA solution design document with bot specifications, process definition tables, and a deployment timeline.

My repetitive tasks: [DESCRIBE YOUR MOST TIME-CONSUMING REPETITIVE TASKS, SYSTEMS INVOLVED, DATA FORMATS, AND VOLUME OF TRANSACTIONS]"
4. The Zapier Integration Architect

"You are the head of solutions engineering at Zapier who designs no-code automation systems connecting 7,000+ apps for businesses ranging from startups to Fortune 500 companies.

I need a complete no-code automation system connecting all my business tools.

Design:

- Tool ecosystem map: every app I use and how data should flow between them
- Zap inventory: every automation I need with trigger app, action app, and data passed
- Multi-step workflow designs for my 5 most complex processes with branching logic
- Filter and formatting rules: how to transform data between incompatible tools
- Error notification system: how I find out when an automation fails and what to do
- Scheduling strategy: which automations run in real-time vs on a schedule
- Data validation rules: checks that prevent bad data from flowing through automations
- Lookup tables and databases: storing reference data that multiple automations share
- Scaling plan: how the system handles 10x volume without hitting plan limits or rate caps
- Monthly automation audit checklist: what to review to keep everything running smoothly

Format as a Zapier-style automation architecture document with Zap specifications, data flow diagrams, and a monitoring checklist.

My tools: [LIST ALL YOUR BUSINESS TOOLS, WHAT DATA LIVES IN EACH, AND WHICH MANUAL TASKS CONNECT THEM]"
5. The Palantir Decision Automation Engine

"You are a senior forward-deployed engineer at Palantir who builds decision automation systems that replace manual judgment calls with data-driven logic for government agencies and Fortune 100 companies.

I need a complete decision automation framework that removes human bottlenecks from routine business decisions.

Automate:

- Decision inventory: list every recurring decision made in my business with frequency
- Decision logic extraction: the exact criteria, thresholds, and rules behind each decision
- Decision tree construction: visual if-then-else logic for the top 10 automatable decisions
- Data requirements: what information each automated decision needs and where it comes from
- Confidence scoring: rate each automated decision's reliability (high, medium, needs human review)
- Escalation rules: which edge cases must be sent to a human and what triggers escalation
- Approval workflow design: multi-level sign-off chains for high-stakes automated decisions
- Audit trail specification: what to log for compliance and post-decision review
- Feedback loop: how to track decision outcomes and improve automation rules over time
- Implementation priority: which decisions to automate first based on frequency and impact

Format as a Palantir-style decision automation specification with decision trees, data requirement tables, and escalation flowcharts.

My decisions: [DESCRIBE YOUR MOST COMMON RECURRING DECISIONS, WHO MAKES THEM, WHAT DATA THEY USE, AND WHICH ONES SLOW YOUR BUSINESS DOWN]"
6. The Amazon Operations Automation Playbook

"You are a senior operations automation manager at Amazon who designed the automated fulfillment workflows that process millions of orders daily with near-zero error rates.

I need a complete operations automation playbook for my business processes.

Build:

- Operations workflow catalog: every operational process with current manual steps documented
- Automation layer design: which steps get fully automated, partially automated, or stay manual
- Trigger system: what event starts each automated workflow (time, action, threshold, or request)
- SLA definition: how fast each automated process must complete with acceptable error rates
- Queue management: how work items get prioritized when multiple tasks compete for resources
- Notification framework: who gets alerted about what, when, and through which channel
- Capacity planning: how the automation handles demand spikes without breaking
- Quality control checkpoints: automated verification steps that catch errors before they cascade
- Performance metrics: the 5 KPIs to track for each automated operation
- Continuous improvement cycle: monthly review process to find and fix automation inefficiencies

Format as an Amazon-style operations playbook with workflow specifications, SLA tables, and a performance monitoring dashboard design.

My operations: [DESCRIBE YOUR DAILY OPERATIONS, ORDER VOLUME, TEAM SIZE, CURRENT BOTTLENECKS, AND TOOLS USED]"
7. The Salesforce CRM Automation Architect

"You are a senior Salesforce solutions architect who builds CRM automation systems for enterprise sales teams, turning manual data entry and follow-ups into fully automated revenue machines.

I need a complete CRM automation strategy that eliminates manual sales busywork.

Automate:

- Lead scoring automation: rules that automatically rank leads by likelihood to buy
- Lead routing logic: assign leads to the right rep based on territory, size, and expertise
- Email sequence triggers: automated follow-up chains based on prospect behavior
- Pipeline stage automation: auto-move deals when specific criteria are met
- Task creation rules: automatically generate to-dos for reps at each deal stage
- Data enrichment automation: pull company and contact info from external sources automatically
- Report generation: scheduled reports and dashboards that build themselves
- Alert system: notify managers when deals stall, quotas are at risk, or big opportunities appear
- Duplicate detection and merge automation: keep the CRM clean without manual cleanup
- Renewal and upsell triggers: flag existing customers approaching renewal or expansion opportunities

Format as a Salesforce-style CRM automation blueprint with automation rule specifications, workflow triggers, and a field mapping document.

My sales process: [DESCRIBE YOUR CRM TOOL, SALES STAGES, TEAM SIZE, LEAD SOURCES, AND BIGGEST MANUAL TIME SINKS]"
8. The Deloitte Financial Process Automator

"You are a senior finance transformation consultant at Deloitte who automates accounting, invoicing, and financial reporting workflows for companies doing $50M-$500M in annual revenue.

I need a complete financial process automation plan that eliminates manual spreadsheet work.

Automate:

- Invoice processing pipeline: from receipt to approval to payment with zero manual data entry
- Expense categorization rules: auto-classify every transaction into the correct account
- Reconciliation automation: match bank transactions to records and flag discrepancies instantly
- Monthly close acceleration: automate journal entries, accruals, and reconciliation steps
- Accounts receivable automation: auto-send invoices, reminders, and escalation notices on schedule
- Budget vs actual reporting: automated variance reports generated weekly without manual pulling
- Cash flow forecasting: auto-update projections as new data arrives from bank and accounting systems
- Approval routing: send spend requests to the right approver based on amount and category
- Tax compliance automation: track and categorize deductible expenses in real-time
- Audit trail generation: auto-document every financial transaction for end-of-year review

Format as a Deloitte-style finance automation roadmap with process specifications, tool recommendations, and a phased implementation timeline.

My finance setup: [DESCRIBE YOUR ACCOUNTING SOFTWARE, MONTHLY TRANSACTION VOLUME, TEAM SIZE, AND MOST PAINFUL MANUAL FINANCIAL TASKS]"
9. The HubSpot Marketing Automation Engine

"You are the VP of Marketing Automation at HubSpot who designs the automated marketing systems that generate 100,000+ qualified leads per month without manual campaign management.

I need a complete marketing automation system that runs campaigns on autopilot.

Build:

- Lead capture automation: forms, chatbots, and pop-ups that trigger instant follow-up sequences
- Segmentation engine: auto-tag contacts based on behavior, demographics, and engagement level
- Nurture sequence library: 5 automated email chains for different buyer stages and personas
- Lead scoring model: points-based system that automatically qualifies leads for sales handoff
- Content delivery automation: send the right content at the right time based on user behavior
- Social media scheduling system: automated posting cadence with content recycling rules
- Webinar and event automation: registration, reminders, follow-ups, and replay sequences
- Re-engagement campaigns: auto-trigger win-back sequences for inactive contacts
- Attribution tracking: automatically tag every conversion with the channel and campaign that drove it
- Performance reporting: automated weekly marketing dashboards sent to stakeholders

Format as a HubSpot-style marketing automation playbook with workflow diagrams, email sequence outlines, and scoring model specifications.

My marketing setup: [DESCRIBE YOUR MARKETING TOOLS, LIST SIZE, CONTENT TYPES, SALES HANDOFF PROCESS, AND BIGGEST LEAD GENERATION CHALLENGE]"
10. The ServiceNow IT Workflow Automator

"You are a principal IT automation consultant at ServiceNow who builds self-healing IT operations systems for enterprises with 10,000+ employees, reducing ticket volume by 60% through intelligent automation.

I need a complete IT workflow automation strategy that eliminates repetitive IT tasks.

Automate:

- Ticket classification and routing: auto-categorize incoming requests and assign to the right team
- Password reset automation: self-service flow that handles 30% of all IT tickets without human touch
- Employee onboarding automation: account creation, access provisioning, and equipment requests triggered automatically
- Software request and approval workflow: catalog, request, approve, and deploy without emails
- Incident auto-remediation: detect common issues and run fix scripts before a human sees the ticket
- Knowledge base auto-suggestion: show relevant articles to users before they submit a ticket
- Escalation automation: auto-escalate unresolved tickets based on time and priority thresholds
- Asset lifecycle automation: track, assign, and retire IT equipment through automated workflows
- Change management automation: approval chains, risk scoring, and deployment scheduling
- SLA monitoring and alerting: track response and resolution times with auto-alerts when breached

Format as a ServiceNow-style IT automation design document with workflow specifications, automation rule tables, and an expected ticket reduction forecast.

My IT setup: [DESCRIBE YOUR IT TEAM SIZE, TICKET VOLUME, TOOLS USED, MOST COMMON TICKET TYPES, AND EMPLOYEE COUNT]"
11. The Notion AI Knowledge Automation System

"You are the head of knowledge operations at Notion who built the internal systems that automatically organize, update, and distribute company knowledge to 30M+ users without manual documentation overhead.

I need a complete knowledge management automation system that keeps my company wiki alive without manual effort.

Design:

- Auto-capture framework: turn Slack messages, meeting notes, and emails into wiki entries automatically
- Template automation: pre-built templates for every recurring document type that auto-populate fields
- Knowledge routing: automatically notify the right people when relevant documentation is created or updated
- Stale content detection: flag documents not updated in 90 days and assign review to the owner
- Search optimization: auto-tagging, cross-linking, and metadata enrichment for instant findability
- Onboarding automation: new hire knowledge paths that serve the right docs in the right sequence
- Meeting-to-action pipeline: extract action items from meeting transcripts and create tasks automatically
- Version control automation: track who changed what, when, and why with rollback capability
- Knowledge gap detection: identify questions asked in Slack that have no matching wiki documentation
- Monthly knowledge health report: auto-generated metrics on coverage, freshness, and usage

Format as a knowledge operations blueprint with automation workflows, template libraries, and a content governance framework.

My setup: [DESCRIBE YOUR TEAM SIZE, KNOWLEDGE TOOLS, BIGGEST DOCUMENTATION PAIN POINTS, AND HOW INFORMATION CURRENTLY GETS SHARED]"
12. The Workato Enterprise Integration Orchestrator

"You are a senior enterprise integration architect at Workato who builds complex multi-system automation orchestrations connecting 50+ enterprise tools into a unified automated workflow for Global 2000 companies.

I need a complete enterprise integration strategy that connects all my systems into automated end-to-end workflows.

Orchestrate:

- System inventory: every business tool and database with the data it holds and APIs available
- Integration map: which systems need to talk to each other and what data flows between them
- Master data strategy: which system is the single source of truth for each data type
- Event-driven triggers: what happens in System A that should automatically trigger action in System B
- Data transformation rules: how to map fields between systems with different formats and naming
- Conflict resolution logic: what happens when two systems have conflicting data for the same record
- Batch vs real-time decision: which integrations need instant sync and which can run on a schedule
- Error handling and retry strategy: what happens when an integration fails at 3 AM
- Security and authentication: how each system connection is secured and credentials are managed
- Monitoring dashboard: real-time visibility into every integration's health and data volume

Format as a Workato-style enterprise integration architecture with system maps, data flow specifications, and error handling procedures.

My systems: [LIST ALL YOUR BUSINESS TOOLS, WHAT DATA LIVES IN EACH, CURRENT MANUAL DATA TRANSFERS, AND INTEGRATION PAIN POINTS]"
13. The Tesla AI-Powered Workflow Intelligence

"You are a senior AI automation engineer at Tesla who builds intelligent automation systems that learn from data patterns and adapt workflows automatically without human reprogramming.

I need an AI-powered automation layer that makes my workflows smarter over time.

Build:

- Pattern recognition: identify recurring patterns in my data that signal automation opportunities
- Predictive triggers: launch workflows based on predicted events before they actually happen
- Smart routing: use historical data to route tasks to the person or system most likely to handle them fastest
- Anomaly-triggered workflows: auto-launch investigation processes when data looks unusual
- Natural language intake: let users trigger complex automations by describing what they need in plain English
- Auto-classification: train simple models to categorize incoming requests, documents, or messages
- Dynamic prioritization: automatically re-order task queues based on urgency, impact, and deadlines
- Feedback-driven optimization: track automation outcomes and suggest rule improvements monthly
- Capacity forecasting: predict when workflow volume will spike and pre-allocate resources
- Self-healing logic: detect when automations produce bad outputs and switch to fallback rules automatically

Format as an AI automation strategy document with intelligence layer architecture, model specifications, and a learning loop design.

My workflows: [DESCRIBE YOUR HIGHEST-VOLUME WORKFLOWS, DATA AVAILABLE FOR PATTERN DETECTION, AND WHERE HUMAN JUDGMENT CURRENTLY SLOWS THINGS DOWN]"
14. The McKinsey Change Management Automation Plan

"You are a senior change management partner at McKinsey who ensures new automation deployments actually get adopted by employees instead of abandoned, drawing on behavioral science and Fortune 500 rollout experience.

I need a complete change management plan for rolling out automation across my organization.

Plan:

- Stakeholder impact analysis: who is affected by each automation and how their job changes
- Communication plan: messaging for executives, managers, and frontline employees with different frames
- Resistance prediction: which teams will push back and specific strategies to address each concern
- Training curriculum: what each role needs to learn, how they learn it, and when
- Champion network: identify and empower automation advocates in every department
- Quick win showcase: which automations to launch first to build momentum and trust
- Feedback collection system: how employees report automation issues and suggest improvements
- Success metrics: how to prove automation is working using numbers leadership cares about
- Role evolution guide: how each affected role transforms rather than disappears
- 90-day adoption roadmap: week-by-week plan from announcement to full adoption

Format as a McKinsey-style change management playbook with stakeholder maps, communication templates, and an adoption tracking dashboard.

My organization: [DESCRIBE YOUR COMPANY SIZE, DEPARTMENTS AFFECTED, AUTOMATIONS BEING DEPLOYED, CURRENT EMPLOYEE SENTIMENT, AND BIGGEST ADOPTION RISK]"
15. The Deloitte Automation ROI Dashboard

"You are a senior analytics partner at Deloitte who builds automation ROI measurement frameworks proving the business case for continued investment in workflow automation to skeptical CFOs.

I need a complete ROI measurement system that tracks the real value of every automation I build.

Measure:

- Time savings calculation: hours saved per automation per week converted to dollar value
- Error reduction tracking: manual error rate before vs after automation with cost-of-error estimates
- Throughput improvement: volume processed before vs after with capacity headroom analysis
- Employee satisfaction impact: survey framework measuring how automation affects team morale
- Customer experience improvement: response time and resolution speed changes post-automation
- Cost avoidance projection: hiring I would have needed without automation at projected growth
- Cumulative ROI dashboard: running total of investment vs returns across all automations
- Break-even timeline: how long each automation takes to pay for itself
- Opportunity cost analysis: what employees now do with the time automation freed up
- Executive reporting template: monthly one-page automation impact summary for leadership

Format as a Deloitte-style ROI framework with calculation formulas, dashboard specifications, and executive reporting templates.

My automations: [DESCRIBE YOUR CURRENT AUTOMATIONS, TEAM SIZE, HOURLY LABOR COSTS, AND WHAT METRICS YOUR LEADERSHIP CARES ABOUT MOST]"
These 15 prompts replace an entire automation consulting division:

→ Workflow discovery ($15,000 QuantumBlack audit)
→ Process mining ($20,000 Accenture engagement)
→ RPA blueprints ($30,000 UiPath implementation)
→ No-code integration ($10,000 Zapier architecture)
→ Decision automation ($25,000 Palantir build)
→ Operations automation ($35,000 Amazon-level ops design)
→ CRM automation ($18,000 Salesforce consulting)
→ Finance automation ($22,000 Deloitte transformation)
→ Marketing automation ($15,000 HubSpot setup)
→ IT workflow automation ($28,000 ServiceNow engagement)
→ Knowledge automation ($12,000 Notion enterprise)
→ Enterprise integration ($35,000 Workato architecture)
→ AI-powered workflows ($40,000 intelligent automation)
→ Change management ($20,000 McKinsey rollout)
→ ROI measurement ($15,000 Deloitte analytics)

Total consulting value: $340,000+
Your cost with Claude: $0.

Automation is the ultimate leverage.

These 15 prompts are your unfair advantage.

Copy. Paste. Automate.

Follow me @heynavtoor more AI prompts that replace six-figure consultants.

♻️ Repost to help your network work smarter, not harder.

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More from @heynavtoor

Feb 17
🚨BREAKING: Claude is insane for market research.

I reverse-engineered how top consultants at McKinsey, Goldman Sachs, & JP Morgan use it.

The difference is night and day.

Here are 12 insane Claude Opus 4.6 prompts they don't want you to know (Save for later) Image
1. Market Sizing (TAM/SAM/SOM) from Scratch

Most founders pay consultants $3K just for a market sizing slide.

Claude does it in 30 seconds with actual logic:

Prompt:

You are a senior market research analyst at McKinsey.

Calculate the TAM, SAM, and SOM for [YOUR PRODUCT/SERVICE] in [TARGET MARKET].

For each:
- Show your math (top-down AND bottom-up approach)
- Cite the assumptions you're making
- Flag where your estimates are weakest
- Compare to any known market reports if applicable

Format as an investor-ready slide with numbers, not paragraphs. If my market is smaller than I think, tell me now.Image
2. Customer Persona Builder (Based on Real Data, Not Guesswork)

Consultants charge $5K to interview 10 people and hand you a persona deck with stock photos.

This is better:

Prompt:

You are a consumer insights researcher at Goldman Sachs

Build 3 detailed customer personas for [YOUR PRODUCT] in [INDUSTRY]

For each persona:
- Demographics + psychographics (what do they read, follow, trust?)
- Buying trigger: What event makes them Google your solution?
- Decision process: Who else influences their purchase?
- Objections: What's their #1 reason to say no?
- Exact phrases they'd use to describe their problem (for ad copy)



- No generic "35-year-old marketing manager" personas
- Base everything on behavioral patterns, not demographics
- Each persona should suggest a different acquisition channel
Image
Read 14 tweets
Feb 17
BREAKING: AI can now build RAG pipelines like Google Brain's retrieval research team (for free).

Here are 12 insane Claude prompts that replace $380K/year ML engineers at top AI labs (Save for later) Image
1. The Google Brain RAG Architecture Designer

"You are a principal research engineer at Google Brain who architected the retrieval-augmented generation systems powering Google's most advanced AI search and knowledge products.

I need a complete RAG system architecture designed from scratch for my use case.

Design:

- End-to-end architecture overview with every component and how they connect
- Ingestion pipeline: how documents enter the system and get processed
- Retrieval layer: how the system finds relevant information when a user asks a question
- Generation layer: how retrieved context gets combined with the LLM to produce answers
- Technology stack recommendation for each component with reasoning
- Latency budget breakdown: maximum time allowed for each pipeline stage
- Scaling strategy: how this architecture handles 10x and 100x data growth
- Cost estimate: monthly infrastructure spend at 1K, 10K, and 100K queries per day
- Failure modes: what can go wrong at each stage and how to handle it gracefully
- Build vs buy decision for each component with vendor recommendations

Format as a Google Brain-style system design document with architecture descriptions, component specifications, and a build timeline.

My use case: [DESCRIBE YOUR DATA TYPE, QUERY PATTERNS, ACCURACY REQUIREMENTS, SCALE, AND BUDGET]"
2. The Pinecone Chunking Strategy Engineer

"You are the head of solutions architecture at Pinecone who has optimized chunking strategies for thousands of production RAG deployments across enterprise clients.

I need a complete document chunking strategy tailored to my specific content type.

Build:

- Chunking method selection: fixed-size, semantic, recursive, or document-structure-based with reasoning
- Optimal chunk size determination based on my content type and query patterns
- Overlap strategy: how many tokens to overlap between chunks and why
- Boundary rules: where to split and where to never split (sentences, paragraphs, sections)
- Metadata extraction plan: what to tag each chunk with for filtering and retrieval
- Parent-child chunking architecture: small chunks for retrieval, large chunks for context
- Special content handling: tables, code blocks, lists, images, and headers
- Chunk quality validation: how to test if your chunks actually produce good retrieval
- Pre-processing pipeline: cleaning, normalizing, and enriching text before chunking
- A/B testing framework: how to compare two chunking strategies with real queries

Format as a chunking strategy specification with decision trees, configuration examples, and a validation test suite.

My content: [DESCRIBE YOUR DOCUMENT TYPES, AVERAGE LENGTH, STRUCTURE, LANGUAGE, AND WHAT USERS TYPICALLY ASK ABOUT]"
Read 14 tweets
Feb 16
BREAKING: AI can now build ML models like Goldman Sachs' AI trading desk (for free).

Here are 12 insane Claude prompts that replace $400K/year quant researchers (Save for later) Image
1/ Time Series Forecasting Model

You are a Quantitative Researcher at Goldman Sachs Global Markets. I need a complete time series forecasting model for [STOCK/ASSET].

Please provide:

- Data preprocessing: How to clean price data and handle missing values
- Feature engineering: Technical indicators (moving averages, RSI, MACD, Bollinger Bands)
- Model selection: Compare ARIMA, LSTM neural networks, and Prophet models
- Training approach: Train-test split ratios and cross-validation strategy
- Performance metrics: MAE, RMSE, directional accuracy for predictions
- Backtesting framework: How to test strategy on historical data
- Risk management: Stop-loss rules and position sizing based on confidence
- Implementation code: Python pseudocode with library recommendations

Format as quantitative research report with model specifications and expected accuracy.

Asset: [DESCRIBE STOCK/CRYPTO/COMMODITY, TIME PERIOD, DATA SOURCE]
2/ Mean Reversion Trading Strategy

You are a VP of Quantitative Trading at JP Morgan's Systematic Trading desk. I need a mean reversion algorithm for [MARKET/ASSET].

Please provide:

- Statistical foundation: Z-score calculation and standard deviation bands
- Entry signals: When price deviates X standard deviations from mean
- Exit signals: When price returns to mean or stop-loss triggers
- Pair selection: How to find correlated assets for pairs trading
- Cointegration testing: Statistical tests to validate pair relationships
- Position sizing: Kelly Criterion or fixed-fraction approach
- Risk parameters: Maximum drawdown limits and exposure caps
- Backtesting results: Expected Sharpe ratio and win rate over 3+ years

Format as algorithmic trading strategy document with entry/exit rules.

Market: [DESCRIBE ASSET CLASS, TIMEFRAME, TRADING STYLE]
Read 15 tweets
Feb 15
BREAKING: AI can now evaluate startups like Sequoia Capital partners (for free).

Here are 12 insane Grok prompts that replace $400K/year VC analysts (Save for later) Image
1/ Market Sizing & TAM Analysis

You are a Partner at Sequoia Capital. I need a complete market size analysis for [STARTUP/INDUSTRY].

Please provide:

- Total Addressable Market: Global market size with data sources
- Serviceable Available Market: Realistic portion startup can reach
- Serviceable Obtainable Market: What startup can capture in 3-5 years
- Market growth rate: CAGR for next 5 years with trend drivers
- Market segments: Break TAM into customer types or use cases
- Bottoms-up validation: Unit economics × potential customers calculation
- Comparable markets: Similar industries that scaled and their trajectory
- Red flags: Reasons market might be smaller than claimed

Format as investment memo market section with specific dollar figures.

Startup: [DESCRIBE COMPANY, PRODUCT, TARGET CUSTOMER]
2/ Competitive Landscape Analysis

You are a VC analyst at Andreessen Horowitz. I need a competitive analysis for [STARTUP] in [INDUSTRY].

Please provide:

- Direct competitors: Top 5 companies solving same problem
- Indirect competitors: 5 adjacent solutions customers use today
- Competitive positioning: Where startup fits on market map (price vs. features)
- Moat analysis: What makes each competitor defensible
- White space: Gaps no one is filling that startup could own
- Threat level: Rate each competitor as low/medium/high threat with reasoning
- Market share estimates: Current revenue or user distribution
- Strategic moves: Recent funding, acquisitions, or pivots by competitors

Format as competitive intelligence brief with comparison matrix.

Startup: [DESCRIBE PRODUCT, MAIN COMPETITORS, DIFFERENTIATION]
Read 15 tweets
Feb 14
BREAKING: AI can now build financial models like Goldman Sachs analysts (for free).

Here are 12 Claude prompts that replace $150K/year investment banking work (Save for later) Image
1/ DCF Valuation Model

You are a Senior Analyst at Goldman Sachs. I need a complete DCF (Discounted Cash Flow) valuation model for [COMPANY NAME].

Please provide:

- Free cash flow projections: Next 5 years with growth assumptions
- WACC calculation: Cost of equity + cost of debt breakdown
- Terminal value: Both perpetuity growth and exit multiple methods
- Sensitivity analysis: How value changes with different assumptions
- Discount rate justification: Why we chose this WACC
- Key drivers: What makes cash flow go up or down
- Comparable companies: How our assumptions compare to peers
- Valuation range: Bull case, base case, bear case scenarios

Format as investment banking pitch book valuation page with clear formulas.

Company: [DESCRIBE COMPANY, INDUSTRY, FINANCIALS]
2/ Three-Statement Financial Model

You are a VP at Morgan Stanley. I need a complete three-statement model for [COMPANY NAME].

Please provide:

- Income statement: Revenue, costs, EBITDA, net income (5 years)
- Balance sheet: Assets, liabilities, equity (5 years)
- Cash flow statement: Operating, investing, financing activities (5 years)
- Link formulas: How statements connect (net income → cash flow → balance sheet)
- Working capital: How AR, inventory, and AP change
- Debt schedule: Principal payments and interest expense
- Key assumptions: Revenue growth, margins, capex as % of sales
- Error checks: Balance sheet balancing and circular references

Format as Excel-style model with formulas explained in plain English.

Company: [DESCRIBE BUSINESS, CURRENT FINANCIALS, GROWTH STAGE]
Read 15 tweets
Feb 12
I collected every NotebookLM prompt that went viral on Reddit, X, and research communities.

These turned a "cool AI toy" into a research weapon that does 10 hours of work in 20 seconds.

16 copy-paste prompts. Zero fluff.

Steal them all 👇 Image
1/ THE "5 ESSENTIAL QUESTIONS" PROMPT

Reddit called this a "game changer." It forces NotebookLM to extract pedagogically-sound structure instead of shallow summaries:

"Analyze all inputs and generate 5 essential questions that, when answered, capture the main points and core meaning of all inputs."
2/ ULTIMATE PROMPT FOR LECTURES:

"Review all uploaded materials and generate 5 essential questions that capture the core meaning.

Focus on:
- Core topics and definitions
- Key concepts emphasized
- Relationships between concepts
- Practical applications mentioned"
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