Prompts that brief agents, not chatbots

Research [TOPIC] and summarize the key findings. Include: 3-5 main points, sources with URLs, and 3 actionable recommendations. Use web search. End with a short list of follow-up questions to validate the findings. Best in ChatGPT with Browse or Gemini with Google Search.

I will paste data below. Analyze it in 3 steps: 1) List observed patterns only (no guesses). 2) List hypotheses separately. 3) Produce a one-page exec summary with: what changed, impact, and 3 actions. Format as bullets under 15 words each. Do not invent numbers. Paste data: [PASTE HERE]

Create a mini campaign for [PRODUCT] targeting [AUDIENCE]. Step 1: One-line offer, 3 persona angles (pain, outcome, objection). Step 2: 4 ad hooks, 3 email subject lines, 1 landing hero. Step 3: 2-week A/B test plan with hypothesis, KPI, sample target. Keep claims specific and compliant. Best in ChatGPT or Claude with iterative follow-ups.

Convert this feature request into a sprint-ready spec. Request: [PASTE REQUEST]. Output: A) One-sentence problem. B) In-scope / out-of-scope. C) 5 acceptance criteria in Given/When/Then. D) Top 5 edge cases. E) Telemetry events to track. Then create two implementation options: Lean (2 weeks) vs Robust (6 weeks) with tradeoffs. Best in Claude or ChatGPT.

Audit this analysis for errors. Check: denominator changes, seasonality, outliers, small sample bias, channel mix shifts. For each risk, mark High/Medium/Low and suggest one validation query. Do not guess at causes — list what to verify. Paste analysis: [PASTE HERE]

Research [COMPANY/INDUSTRY] and produce a competitive brief. Use web search. Include: positioning, recent news, pricing signals, product updates, marketing channels, and 3 opportunities or threats for [OUR COMPANY]. Cite sources. End with 5 questions a sales or product team should ask to validate. Best in ChatGPT with Browse or Perplexity.

Product: [PRODUCT]. Audience: [AUDIENCE]. Goal: [LEADS/DEMO/TRIAL]. Channel: [LINKEDIN/EMAIL/LANDING]. Step 1: Return a message map with 1 core promise, 3 proof points, 3 objections + responses. Step 2: Using that map, generate 5 ad hooks, 3 email subject lines, 2 CTA variants. Step 3: Create a 2-week A/B test plan with hypothesis, KPI, and stop/scale rules. No hype, max character limits included.

For [PRODUCT] targeting [AUDIENCE], give 3 persona angles: Pain, Desired Outcome, Objection. Then create 4 LinkedIn ads, 3 email subject lines, 1 landing hero section. Keep claims compliant and specific. Avoid "revolutionary" and "game-changing." Use grade-8 reading level. CTA must start with an action verb.

Act as a senior marketing strategist. Create a comprehensive campaign brief for [PRODUCT/SERVICE] targeting [AUDIENCE]. Include: campaign objectives, key messages, channel strategy, timeline, KPIs, and budget allocation recommendations. The campaign should differentiate us from [COMPETITORS] and leverage our unique value proposition of [UVP].

Analyze the competitive landscape for [INDUSTRY/MARKET]. Create a framework comparing: positioning, pricing strategies, target audiences, marketing channels used, content strategies, and unique differentiators. Identify gaps and opportunities for a new entrant or existing player to capture market share.

Create a detailed buyer persona for [PRODUCT/SERVICE]. Include: demographic details, psychographic profile, pain points, goals, objections to purchase, preferred content formats, media consumption habits, buying triggers, and a day-in-the-life narrative. End with 5 marketing messages that would resonate with this persona.

Develop comprehensive brand voice guidelines for [BRAND]. Include: voice attributes (3-5 adjectives), tone variations by context (social media, customer service, formal communications), words to use vs. avoid, example phrases, and do's/don'ts. Provide before/after examples of copy transformation.

Create a 30-day social media content calendar for [BRAND] on [PLATFORMS]. Include: post themes tied to our goals of [GOALS], optimal posting times, content mix (educational, promotional, engagement, UGC), hashtag strategies, and 2-3 specific post ideas per day with captions. Consider [UPCOMING EVENTS/HOLIDAYS].

Act as a CMO. Given a marketing budget of [AMOUNT] and goals of [GOALS], create a channel allocation framework. Include: recommended spend per channel, expected ROI ranges, testing budget allocation, seasonality adjustments, and KPIs to track. Consider our current performance baselines of [BASELINES].

Develop a content pillar strategy for [BRAND] in [INDUSTRY]. Create 5-7 content pillars that align with our expertise and audience interests. For each pillar, provide: description, example topics (10 each), content formats, SEO keyword clusters, and how it supports business goals. Include a content repurposing framework.

Generate 10 high-impact A/B test hypotheses for [WEBPAGE/CAMPAIGN]. For each test, provide: hypothesis statement (If we change X, then Y will happen because Z), control vs. variation description, primary metric, expected impact range, and sample size recommendation. Prioritize by potential impact and ease of implementation.

Generate 20 headline variations for [PRODUCT/TOPIC] targeting [AUDIENCE]. Include: 5 benefit-driven headlines, 5 curiosity-driven headlines, 5 urgency/scarcity headlines, and 5 social proof headlines. Each headline should be under 60 characters. Explain the psychological trigger behind each.

Write complete landing page copy for [PRODUCT/SERVICE] using the AIDA framework. Include: attention-grabbing headline and subheadline, problem agitation section, solution presentation, 5 key benefits with supporting copy, social proof section, FAQ addressing top objections, and a compelling CTA. Target conversion goal: [GOAL].

Create 10 Facebook ad copy variations for [PRODUCT]. Include: 3 short-form (under 125 chars), 4 medium (125-250 chars), 3 long-form (250-500 chars). Each should have primary text, headline, and CTA. Test different angles: pain point, transformation, social proof, curiosity, and direct offer.

Write a product description for [PRODUCT] that converts browsers to buyers. Include: sensory language that helps readers imagine using it, unique selling points, specifications presented as benefits, ideal use cases, and a mini-story of transformation. Length: [WORD COUNT]. Tone: [TONE]. Include SEO keywords: [KEYWORDS].

Create a brand story for [BRAND] using the StoryBrand framework. Define: the customer as hero, their problem (external, internal, philosophical), position our brand as guide, the plan we offer, the call to action, the stakes of failure, and the transformation/success. Write a 500-word founding story that brings this to life.

Create a value proposition for [PRODUCT/SERVICE]. Define customer jobs (functional, social, emotional), pains (frustrations, obstacles, risks), and gains (desired outcomes). Then map our product's: pain relievers, gain creators, and features. End with 5 value proposition statement variations following the format: "We help [WHO] do [WHAT] by [HOW] so they can [BENEFIT]."

Generate 15 CTA button variations for [ACTION/GOAL]. For each, provide: button text, supporting microcopy (anxiety reducer), and the psychological principle it leverages. Include a mix of: action-oriented, benefit-focused, urgency-based, and conversational CTAs. All should be mobile-friendly (under 25 characters for button text).

Analyze and improve this copy: [PASTE COPY]. Provide: specific issues identified (clarity, persuasion, structure), an improved version with tracked changes explained, readability score improvement, and 3 alternative approaches. Maintain the same word count but increase conversion potential. Keep brand voice of [TONE].

I will paste weekly funnel data. Output in this exact format: 1) What changed (3 bullets, numbers only). 2) Why it likely changed (max 3 hypotheses). 3) What to do this week (3 actions with owner and expected impact). 4) What data is missing to confirm. Keep under 180 words. Do not invent numbers. Paste data: [PASTE HERE]

Turn this analysis into 4 bullets for a slide. Each bullet: start with what happened, end with what we're doing about it. Keep under 15 words per bullet. Then rewrite the same 4 bullets for a non-technical [MARKETING/SALES/EXEC] audience. Paste analysis: [PASTE HERE]

Analyze this marketing performance data: [PASTE DATA]. Provide: executive summary, key trends identified, anomalies or concerns, performance vs. benchmarks, root cause hypotheses for underperformance, top 5 actionable recommendations with expected impact, and suggested next analyses to conduct.

Write a SQL query to [DESCRIBE ANALYSIS]. The database has these tables: [LIST TABLES AND KEY COLUMNS]. Include: the query with comments explaining each part, sample output structure, edge cases handled, performance optimization notes, and 2 variations for related analyses.

Design a [TYPE] dashboard for [AUDIENCE]. Include: dashboard objective, 8-12 KPIs with definitions and formulas, recommended visualizations for each metric, filter/drill-down capabilities needed, data refresh frequency, alert thresholds, and a wireframe layout description. Consider the decisions this dashboard should enable.

Write an executive summary for this data analysis: [PASTE FINDINGS]. Structure it for [C-SUITE/MANAGER] audience. Include: headline insight (1 sentence), key metrics with context, what's working, what's concerning, recommended actions with business impact, and next steps. Keep it under 300 words. Use confident, decisive language.

Analyze trends in this time-series data: [PASTE DATA]. Identify: overall direction (growth/decline rate), seasonality patterns, cyclical patterns, notable inflection points with potential causes, leading indicators, and forecast for next [TIME PERIOD] with confidence intervals. Recommend actions based on trend trajectory.

Create a KPI framework for [BUSINESS FUNCTION/GOAL]. Define 10-15 KPIs organized by: leading vs. lagging indicators, strategic vs. operational metrics. For each KPI include: definition, formula, data source, measurement frequency, benchmark/target, owner, and actions triggered by different performance levels.

Design a cohort analysis for [BUSINESS QUESTION]. Define: cohort segmentation criteria (time-based, behavior-based), metrics to track per cohort, analysis time frame, visualization approach, statistical significance considerations, and expected insights. Provide SQL pseudocode for the analysis structure.

I noticed [ANOMALY] in our data. Help me investigate: create a hypothesis tree of possible causes (technical, external, behavioral), suggest data points to examine for each hypothesis, recommend validation steps, provide a template for documenting findings, and outline the communication plan if the anomaly is significant.

Write a Python data cleaning pipeline for a dataset with: [DESCRIBE DATA]. Include: loading data, handling missing values (with multiple strategies), outlier detection and treatment, data type conversions, feature engineering suggestions, validation checks, and logging. Use pandas best practices and include docstrings.

I need to predict [TARGET] using [FEATURES] with [DATA SIZE] records. Help me: compare 4-5 suitable algorithms with pros/cons, recommend preprocessing steps for each, suggest hyperparameter tuning approaches, define evaluation metrics and why, outline a validation strategy, and identify potential pitfalls.

Create Python visualization code to show [INSIGHT] from this data structure: [DESCRIBE]. Use matplotlib/seaborn. Include: publication-quality formatting, clear labels and titles, color palette for accessibility, multiple subplot layout if needed, annotations for key points, and export settings for presentations.

I want to test if [HYPOTHESIS]. My data: [DESCRIBE DATA TYPE, SAMPLE SIZE, DISTRIBUTION]. Recommend: the appropriate statistical test with justification, assumptions to verify, Python code using scipy/statsmodels, how to interpret results, effect size calculation, and how to report findings in a business context.

Given these raw features for predicting [TARGET]: [LIST FEATURES], suggest 20+ engineered features. For each: name, calculation logic, intuition for why it might be predictive, Python implementation, and feature importance testing approach. Group by feature type (interaction, aggregation, time-based, etc.).

Explain this [MODEL TYPE] model results to non-technical stakeholders. Model predicts [TARGET] with [ACCURACY]. Top features: [LIST]. Create: an executive summary, analogy-based explanation, visual description suggestions, business implications, limitations and caveats, and recommended next steps. Avoid jargon.

Generate 20 subject line variations for an email about [TOPIC] to [AUDIENCE]. Create 4 variations each of: curiosity-driven, benefit-focused, urgency/scarcity, personalization-based, and question format. Each under 50 characters. Include preview text suggestions. Rate each for spam risk (low/medium/high).

Create a 5-email welcome sequence for new [PRODUCT/SERVICE] subscribers. For each email: subject line + preview text, send timing (day/hour), goal of the email, full copy, CTA, and success metrics. Progress from welcome → value delivery → social proof → engagement → conversion. Include personalization tokens.

Create a win-back email sequence for customers who haven't [ENGAGED/PURCHASED] in [TIME PERIOD]. Design 4 emails with: emotional hook (we miss you), value reminder, special offer, and final attempt. Include subject lines, timing, copy, and criteria for suppressing non-responders. Consider why they may have disengaged.

Create personalized email content blocks for [EMAIL TYPE] based on these segments: [LIST SEGMENTS]. For each segment, provide: personalized headline, body copy variation, product recommendations logic, CTA variation, and image suggestions. Show the default version and 3+ segment-specific versions.

Design a 3-email abandoned cart sequence for [PRODUCT TYPE]. Email 1: Reminder (1 hour), Email 2: Overcome objections (24 hours), Email 3: Incentive (48 hours). For each: subject line, copy, product block suggestions, trust elements to include, and A/B test recommendations. Include SMS follow-up option.

Create a newsletter template and 4 weeks of content for [BRAND/TOPIC]. Include: recurring sections (define 4-5), content mix ratio, optimal length, subject line formulas, send time recommendations, engagement hooks, and growth tactics. For each week, provide specific content ideas with headlines.

Convert this request into a spec. Request: [PASTE]. Output: A) One-sentence problem. B) In-scope / out-of-scope. C) 5 user stories with Given/When/Then acceptance criteria. D) Top 5 edge cases. E) Telemetry events to track. Then create two options: Lean (2 weeks) vs Robust (6 weeks) with tradeoffs. End with: What could fail in onboarding, permissions, data accuracy, or support? Mitigation for each.

For feature [FEATURE] launching to [USERS], pretend you are Support, QA, and Compliance. What will break first? Output a table: Risk | Severity | Mitigation | Owner. Then define events, properties, and success threshold for the first 14 days post-launch.

Create a PRD for [FEATURE]. Include: problem statement with data, user personas affected, jobs to be done, success metrics, requirements (must have, should have, nice to have), user flows, edge cases, technical considerations, dependencies, risks, and launch checklist. Format for engineering handoff.

Generate user stories for [FEATURE/EPIC]. Create 10-15 stories following: "As a [user type], I want [action] so that [benefit]." For each story include: acceptance criteria (3-5 specific conditions), story points estimate, dependencies, and potential edge cases. Group by user type and prioritize by value/effort.

Analyze these user interview notes: [PASTE NOTES]. Extract: key themes (group related insights), user pain points ranked by frequency and severity, desired outcomes, surprising findings, quotes that capture sentiment, and recommended actions. Create an affinity map structure and suggest follow-up questions.

Help plan Q[X] roadmap for [PRODUCT]. Given these inputs: company goals [GOALS], current metrics [METRICS], user feedback themes [THEMES], and tech debt items [ITEMS]. Create: theme-based roadmap, prioritization rationale (RICE scores), resource allocation suggestions, dependencies map, and risk assessment. Include "not doing" list with reasoning.

Apply RICE scoring to these feature ideas: [LIST FEATURES]. For each feature, estimate: Reach (users/quarter), Impact (0.25-3x scale), Confidence (%), Effort (person-weeks). Calculate RICE scores, rank features, and provide reasoning for estimates. Identify any features that need more data before scoring.

Create a competitive analysis for [PRODUCT] vs [COMPETITORS]. Compare: feature parity matrix, pricing models, target user segments, UX strengths/weaknesses, market positioning, recent product updates, and user review sentiment. Identify: competitive moats, vulnerability areas, and differentiation opportunities.

Create a detailed customer journey map for [PERSONA] using [PRODUCT/SERVICE]. Map stages: Awareness → Consideration → Purchase → Onboarding → Usage → Advocacy. For each stage include: user goals, touchpoints, actions, thoughts, emotions (high/low), pain points, and opportunities for improvement. Add moments of truth.

Audit customer touchpoints for [BUSINESS]. List all touchpoints across channels (digital, physical, human). For each: rate current experience (1-5), identify friction points, benchmark against expectations, suggest quick wins and longer-term improvements. Prioritize by impact on satisfaction and feasibility.

Design an onboarding flow for [PRODUCT] targeting [USER TYPE]. Define: the "aha moment" to reach, steps to get there (max 5), information to collect, features to highlight, gamification elements, progress indicators, and re-engagement triggers for drop-offs. Include copy for each step and success metrics.

Create a churn prevention framework for [PRODUCT/SERVICE]. Define: early warning signals (behavioral triggers), health score components, intervention points, retention plays for each risk level, win-back strategies, and exit interview questions. Include automation rules for triggering interventions.

Build an RFM segmentation model for [BUSINESS/DATABASE]. Given: customer purchase history with recency, frequency, and monetary value fields. Output: 5–7 meaningful segments with labels (e.g., Champions, At-Risk, Hibernating), thresholds for each dimension, recommended actions per segment, and expected response rates. Include SQL pseudocode for segment assignment. Best in ChatGPT or Claude.

Create a brief for building a propensity-to-buy model for [PRODUCT/OFFER]. Include: input features to consider (demographic, behavioral, transactional), model type recommendations with rationale, validation approach, score banding strategy (deciles), business rules for campaign suppression, and a test vs. control design to measure lift. Best in Claude or ChatGPT Thinking for complex reasoning.

Audit this customer database extract for campaign readiness. Paste data summary or column list: [PASTE]. Check: completeness of key fields (email, postal, opt-in status), duplicate logic, data currency (last updated), segment distribution, and compliance flags. Output a table: Field | Completeness % | Issue | Fix Required. Then recommend top 3 data quality actions before any campaign send.

Build a suppression logic document for [CAMPAIGN TYPE] targeting [AUDIENCE]. Define: hard suppression rules (opt-outs, unsubscribes, legal blocks), soft suppression rules (recent buyers, complaint history, fatigue thresholds), frequency capping logic, reachable vs contactable definitions, and GDPR/CCPA considerations. Format as a decision tree for campaign ops to follow.

Design a lookalike audience strategy for [PRODUCT/BRAND] using our best customers as seed. Step 1: Define seed audience criteria (top 20% by LTV). Step 2: Identify key characteristics for matching (demographics, behavioral signals, purchase patterns). Step 3: Recommend platforms and match rates. Step 4: Define audience size tiers vs. precision tradeoff. Step 5: Measurement plan to validate lookalike quality vs. broad targeting.

Analyze customer lifetime value for [BUSINESS]. I will paste cohort or purchase data: [PASTE DATA]. Output: average LTV by segment, top 10% vs. bottom 10% profile differences, payback period by acquisition channel, LTV:CAC ratios by channel, and 3 recommendations for shifting budget toward highest LTV segments. Do not invent numbers. Flag any data gaps.

I am considering this decision: [DESCRIBE DECISION]. Before giving a recommendation, work through: 1) What assumptions is this decision built on? 2) Which assumptions are most likely to be wrong? 3) What is the second-order consequence if the top assumption fails? 4) What would a confident critic say? 5) What information, if available, would change the recommendation? Then give a verdict with confidence level (High / Medium / Low) and a one-sentence rationale. Best in ChatGPT Thinking or Claude extended thinking.

Analyze this trade-off: [DESCRIBE OPTIONS AND CONTEXT]. Work step by step. First: define what we are optimizing for. Second: list criteria for evaluation with weights. Third: score each option against each criterion — show your work, don't jump to totals. Fourth: identify any criteria where the scoring is highly uncertain. Fifth: give a final recommendation with a one-paragraph rationale. Do not skip steps. Best in ChatGPT Thinking or Claude extended thinking.

We observed this problem: [DESCRIBE METRIC DROP OR ISSUE]. Work through a structured root cause analysis. Step 1: List possible causes grouped by category (technical, behavioral, external, data quality). Step 2: For each cause, state what evidence would confirm or rule it out. Step 3: Rank causes by probability given what we know. Step 4: Recommend the top 3 validation queries or checks. Do not guess at causes without stating what confirms each. Best in ChatGPT Thinking or Claude extended thinking.

For [BUSINESS DECISION / INITIATIVE], build three scenarios: Base (most likely), Bull (best reasonable case), Bear (realistic downside). For each scenario: state the key assumption that drives it, estimate the financial or operational impact with ranges, name the earliest leading indicator you would see if that scenario is unfolding, and define a trigger point for each scenario where strategy should change. Be specific with numbers; note where estimates are uncertain. Best in ChatGPT Thinking or Claude extended thinking.

Review this code/logic for correctness: [PASTE CODE OR LOGIC]. Do not just check syntax. Work through: 1) Edge cases that could cause wrong output. 2) Off-by-one or boundary errors. 3) Assumptions in the logic that might not hold in production data. 4) Performance issues at scale. 5) Security or data exposure risks. For each issue: severity (Critical / Medium / Low), explanation, and suggested fix. Best in ChatGPT Thinking — takes longer but catches more.

My role is [JOB TITLE] at a [COMPANY TYPE]. My key responsibilities are: [LIST 5–7 TASKS]. Reason through: 1) Which of these tasks can current AI (2026) fully automate? 2) Which can AI significantly accelerate but still require human judgment? 3) Which remain human-only for now? 4) What new skills should I build to stay ahead of automation in 12–24 months? Be honest, not reassuring. Use current capabilities, not speculative future AI. Best in ChatGPT Thinking or Claude for nuanced judgment.

I am applying for [ROLE TITLE] in [INDUSTRY]. Below is my work history in rough notes: [PASTE BULLETS OR PARAGRAPH]. Rewrite as 5–7 resume bullets. Use strong verbs, quantify impact where I gave numbers, and do not invent metrics. If a claim needs a number I did not provide, phrase it qualitatively or mark [VERIFY]. Tailor emphasis to keywords from this job description: [PASTE JD OR KEY PHRASES].

Write a LinkedIn About section for me. Current title: [TITLE]. Target audience: [e.g. hiring managers in SaaS marketing]. Tone: [professional but conversational / sharp / warm]. Include: what I do today, one concrete win, what I am looking for next, and a single line on how I use AI at work (honest, not buzzwordy). Max 2600 characters. Here is background: [PASTE SHORT BIO OR RESUME SUMMARY].

Job description: [PASTE JD]. My background: [2–3 sentences or paste resume summary]. Write a cover letter under 350 words. Open with why this company or role specifically (use public facts only, no flattery). Map three of my strengths to three requirements in the JD. Close with one sentence on fit. Avoid clichés like "passionate self-starter." Sound like a human.

Role: [TITLE] at [TYPE OF COMPANY]. Job description summary: [PASTE OR SUMMARIZE]. Based on this, list 12 likely interview questions (mix of behavioral and role-specific). For each question, give a 4–6 sentence outline of a strong answer using the STAR method where it fits. Pull themes from my background: [PASTE 5 BULLETS]. Flag any answer that would need a real metric I should look up before the interview.

Current role: [TITLE]. I want to move toward [TARGET ROLE, e.g. AI product analyst / marketing ops with AI / data + GenAI]. Here are my skills and projects: [LIST]. Rewrite my positioning in three parts: (1) One paragraph "elevator" story for networking. (2) Five resume-ready bullets that emphasize tooling, data, and judgment, not hype. (3) Three gaps I should close in the next 90 days with concrete actions. Be direct if the pivot is a stretch.

I received an offer (or expect one) for [ROLE] in [LOCATION OR REMOTE]. Base range I have in mind: [RANGE if known]. Benefits that matter to me: [LIST]. Help me draft: (1) A short script to ask for time to review. (2) 4 bullet points to justify asking at the top of the range without sounding entitled. (3) One paragraph if they say the budget is fixed. (4) Questions to ask about equity, bonus, and title. This is for planning only, not legal advice.

I want to message [ROLE, e.g. a product lead] at [COMPANY OR TYPE OF COMPANY] on LinkedIn. Goal: [informational chat / referral / advice on breaking in]. My background in one line: [LINE]. Draft a message under 90 words. No "I hope this finds you well." One specific reason I picked them (their post, talk, or company move). One clear ask. Humble tone.

I built [WHAT IT DOES] using [STACK OR TOOLS]. Audience: recruiters and hiring managers who are not deeply technical. Write a README.md with: project name and one-line description, problem solved, what I built (3 bullets), how to run it or view it (or "demo link: ..."), what I learned, and "Possible next steps." Keep jargon low. I will paste rough notes: [PASTE].

Help me script a 60-second "tell me about yourself" for interviews. Current situation: [WHERE I AM NOW]. Target: [ROLE OR INDUSTRY]. Strengths I want highlighted: [3 THINGS]. One honest reason for the move. Structure: past (10 sec), present (25 sec), future (25 sec). Conversational, not memorized-sounding. I will practice aloud, so avoid tongue-twisters.

I work as [ROLE] and want to understand what transfers to [TARGET FIELD OR ROLE]. List my likely transferable skills from this job description I wrote: [PASTE]. For each skill, rate relevance High/Medium/Low for the target, and give one sentence I could use on LinkedIn or in an interview. Then list three skills that are real gaps and the cheapest way to demonstrate progress on each (course, small project, volunteer task).

Help me prepare for a meeting about [TOPIC] with [ATTENDEES]. Create: agenda suggestion, key talking points, potential questions I'll face with suggested answers, data/examples I should bring, decisions to drive, and follow-up items to propose. Meeting goal: [GOAL].

Create a presentation outline for [TOPIC] targeting [AUDIENCE]. Include: hook opening (story/statistic), problem framing, solution/recommendation, supporting evidence (3 pillars), objection handling, call to action, and closing. Suggest visuals for each slide. Time: [DURATION]. Goal: [ACTION YOU WANT].

Help me solve this problem: [DESCRIBE PROBLEM]. Walk me through: problem definition (5 Whys), stakeholder map, constraint identification, solution brainstorm (10+ ideas), evaluation criteria, top 3 solutions with pros/cons, recommended approach, implementation plan, and success metrics.

Help me prepare for a difficult conversation about [SITUATION] with [PERSON/ROLE]. Provide: opening script that's direct but empathetic, key points to make, anticipated responses and how to handle each, de-escalation phrases, desired outcome, and follow-up steps. Tone should be professional and constructive.