30 working prompts for business, marketing, sales, coding, and research. Copy and adapt to your context.
A Brief Contract Summary
Operations
Produces a contract summary with a focus on risks and obligations.
Produce a brief contract summary.
Structure:
1. Parties and subject
2. Term and renewal conditions
3. Cost / settlements
4. SLA / KPI
5. Liability and sanctions (in numbers)
6. Termination conditions
7. Confidentiality and IP
8. Jurisdiction and venue
9. Strange / unusual clauses worth discussing with a lawyer
10. What you'd propose to change from the "client / contractor" side
Don't give legal opinions. When in doubt write "[uncertain — check with a lawyer]".
Example: Quickly understand what's in a signed/proposed contract.
Model: Claude·Difficulty: Intermediate
#legal#ops
A Cold Email Without a Template
Sales
Prepares an individual cold email with a concrete reason for reaching out.
Write a cold email.
Hard rules:
- ≤90 words
- The first line — a concrete reason for reaching out to this specific person (not "I noticed you're CEO of...")
- One sentence about us
- One sentence about relevance
- One subtle CTA with a concrete time window or a concrete next step
- No words "synergy", "leverage", "circle back"
- A short signature
Input:
- Recipient and context: <…>
- Our product: <…>
- Relevance hypothesis: <…>
Example: One email = one person, not a mass blast.
Model: Claude / GPT·Difficulty: Beginner
#outreach#email
A Good Bug Report
Coding
Turns 'it doesn't work for me' into a reportable bug with steps.
Become a QA engineer. Help the user write a good bug report.
Extract from the input:
1. What exactly broke (one sentence)
2. Steps to reproduce (a numbered list)
3. Expected behavior
4. Actual behavior
5. Environment: OS, version, browser, time
6. Logs / screenshots — a list of what would be useful to attach
If data is missing — ask 3-5 clarifying questions before composing the report.
Example: Any non-technical user can compose a proper bug report through this prompt.
Model: Claude / GPT·Difficulty: Beginner
#qa#engineering
A Quick Market Scan
Research
Prepares a table of players in a segment with sources and dates.
Do a market scan for a segment.
Conditions:
- Use Perplexity / web search if available
- Under each item — a link to the source
- Mark estimated/unconfirmed numbers as [estimate]
- Don't make up ARR, revenue, headcount
- Mark the last-updated date of each source
Input:
- Segment: <…>
- Geography: <…>
- What we want to understand: <…>
Output: a markdown table with columns — Player | Description | Price | Strengths | Weaknesses | Source.
Example: A market map for a new segment in 1 hour.
Does a code review with a focus on correctness, readability, and security.
Do a code review for the following diff.
Rules:
- No "looks good!" without justification
- A clear split: BLOCKERS / SHOULD-FIX / NIT
- Under each item, 1-2 sentences and, if applicable, an example fix
- Special attention: security, edge cases, error handling, mutation of shared state
- At the end — 1 line with a summary "merge as-is / merge after fixes / needs rework"
<diff>:
<…>
Example: Get a second opinion before merging a PR.
Model: Claude·Difficulty: Intermediate
#code-review#engineering
Academic Paper Summary
Research
Produces a structured summary of a paper / preprint.
Produce a summary of an academic paper. Not a retelling, but a summary for a busy reader.
Structure:
1. The research question (1 sentence)
2. What's new compared to prior work
3. Methodology (3-5 lines)
4. The main result
5. Limitations the authors acknowledge
6. How it's useful to a practitioner (if applicable)
7. What you'd want to verify further / what the authors don't insist on
Input: the text or a link to the paper.
Example: Quickly parse preprints on a topic of interest.
Model: Claude / Gemini·Difficulty: Intermediate
#research#academic
An Email Newsletter People Actually Read
Marketing
Prepares a newsletter issue with a single focus, without 'let's talk about'.
Prepare an email newsletter issue.
Conditions:
- One main point per email
- Subject ≤55 characters, without the words "exclusive", "secret"
- The first paragraph must show why to read on
- Length 250-450 words
- 1 link / CTA, no more
- At the end — a PS line with one useful tidbit
Input:
- Topic: <…>
- Context: <…>
Example: A regular newsletter issue without writer's block.
Model: Claude / GPT·Difficulty: Beginner
#email#content
Board Meeting Prep
Business
Prepares a set of slides / talking points for a board meeting from rough data.
Play the role of chief of staff. Prepare a 60-minute board meeting agenda.
Input:
- Meeting goal: <…>
- Attendees: <…>
- Context of the last quarter: <…>
- Open questions: <…>
Output:
1. An agenda with timings (minutes)
2. For each block: 3 key talking points
3. A list of decisions to extract from the board
4. A list of appendices (metrics, contracts, reports) to prepare
Don't write "we are pleased to present" etc. — a neutral operational tone.
Example: Prep for the quarterly board meeting in a couple of iterations.
Model: Claude·Difficulty: Intermediate
#founder#operations
Clustering Customer Feedback
Research
Breaks customer feedback into clusters by topic and sentiment.
Break customer feedback into clusters.
Steps:
1. Read the input (an array of reviews)
2. Identify 5-8 topics
3. Under each topic: a short description, volume (how many reviews fell into it), sentiment (positive/negative/neutral)
4. 2-3 quotes in each cluster (verbatim)
5. At the end — the top 3 actions the product could take
Don't make up reviews. If there's little data, say so.
Example: Once a week, go through reviews and draw conclusions.
Model: Claude·Difficulty: Beginner
#product#research
Competitor Deep Dive
Research
Prepares a competitor profile: product, price, marketing, weak spots.
Do a deep dive on the company <name>.
Cover:
1. Business model and segments
2. Price and tiers (if public)
3. Key products and features
4. Positioning on the site / in marketing
5. Weak spots: what they do NOT do (from their own UX and materials)
6. What they lean on in sales (cases, integrations, brand)
7. A list of their clients / case studies — a list + links
8. Their public moves over the last 6 months
Sources: public only. Mark "[unverified]" if you didn't find confirmation.
Example: Prepare a competitor background before a strategy session.
Prepares an interview guide for the JTBD / Mom Test methodology without leading questions.
Prepare a customer interview guide using the JTBD + The Mom Test method.
Conditions:
- 10-12 questions, moving from past experience to context, to pain, to alternatives
- Leading "would you want X?" questions are banned
- Every question must extract a fact, not an opinion
- Add a hint for the interviewer about an escape hatch ("tell me how it went the last time")
Input:
- Segment: <…>
- Pain hypothesis: <…>
- What needs to be tested: <…>
Example: Prepare a series of interviews for a product hypothesis.
Model: Claude / GPT·Difficulty: Beginner
#product#discovery
Discovery Call Plan
Sales
Prepares a discovery call structure using the MEDDIC / SPIN methodology.
Prepare a plan for a 30-minute discovery call with a lead.
Structure:
1. 2 min: context and agreement on the format
2. 5 min: the current situation (how they solve the task now)
3. 10 min: the pain and its cost (ask for concrete numbers)
4. 5 min: decision criteria, the process, who else is involved
5. 5 min: timeline and budget
6. 3 min: the next step
Under each block — 2-3 good open questions via SPIN/MEDDIC.
Input:
- Lead: <…>
- What we're selling: <…>
Example: Prep for every discovery call in 5 minutes.
Model: Claude·Difficulty: Intermediate
#sales#discovery
Document Localization
Operations
Doesn't just translate but localizes a document for another jurisdiction/culture.
Localize a document.
Steps:
1. First translate literally
2. Then go through again and adapt: units of measurement, currency, legal terms, cultural references, names, and date formats
3. Mark decisions where localization conflicts with a literal translation — leave comments in [brackets]
Input:
- Document: <…>
- Target language / market: <…>
- Document type: <…>
Example: Prepare documents for entering a new market.
Model: Claude·Difficulty: Intermediate
#localization#ops
Follow-up After a Meeting
Sales
Turns meeting notes into a follow-up email with next steps.
Write a follow-up email after a meeting.
It must contain:
- 1 line: thanks without fluff
- 3-5 bullets: what we heard
- 2-3 bullets: what we'll do on our side
- 1-2 bullets: what's needed from the client
- The date of the next step
Input (meeting notes):
<…>
Example: Turn raw notes into an email in 3 minutes.
Model: Claude / GPT·Difficulty: Beginner
#sales#email
Generating Tests from Code
Coding
Writes unit tests starting from edge cases, not the happy path.
Generate unit tests for a function.
Rules:
- Start with 3 edge cases and a description of why each matters
- Then the happy path (minimum)
- Use the framework specified in the input (e.g., vitest/pytest)
- Don't invent behavior that isn't in the code — if something is unclear, ask
Input: the function code and the framework.
Example: Cover tests for a function that has none.
Model: Claude·Difficulty: Intermediate
#testing#engineering
ICP Mining on LinkedIn
Sales
Helps phrase a search string for Sales Navigator and ICP filters.
Help me build ICP filters for LinkedIn Sales Navigator.
Give:
- 5-7 boolean strings (with AND / OR) for the search
- At least 3 "trigger" indicators (recent events, hiring, investments, laws) — where to look for them
- 3-5 negative filters (companies we exclude)
- Alternative titles that mean the same role
Input:
- Segment: <…>
- What we're selling: <…>
Example: Build a targeted list before an outreach campaign.
Model: Claude / GPT·Difficulty: Intermediate
#linkedin#sales
Internal Policy Draft
Operations
Prepares a draft of an internal policy (e.g., an AI-use policy).
Prepare a draft of an internal company policy.
Structure:
- Why this policy exists
- Whom it applies to
- What's allowed / forbidden (as a list)
- The exception procedure
- Who's responsible for maintaining it
- The effective date
Style: short sentences, no legal clichés. At the end — an "FAQ for employees" section.
Input:
- Policy topic: <…>
- Company context: <…>
Example: Prepare an AI-use policy for the teams.
Model: Claude·Difficulty: Beginner
#policy#ops
Job Description Without the Bullshit
Business
Prepares a clear JD without the clichés 'a dynamic team' and 'limitless opportunities'.
Write a job description for a position based on the inputs.
Hard rules:
- No "rockstar", "ninja", "unlimited potential"
- No "we're a fast-paced team" without explaining what that means
- 3 sections: "What you'll do in the first 90 days", "What will be easy" / "What will be hard", "What you must be able to do"
- One paragraph about the salary band if given; otherwise — a note [salary band TBD]
- End with an honest "this isn't for you if…"
Input:
<role, seniority, team, key projects, hard requirements, salary>
Example: Quickly get a non-cookie-cutter JD that doesn't scare off strong candidates.
Model: Claude·Difficulty: Beginner
#hiring#ops
Landing Page Using the JTBD Framework
Marketing
Prepares the structure and a draft of the landing page copy.
Prepare the structure and a draft of a landing page.
Sections (in this order):
1. Hero: H1 (≤70 characters) + 1 subhero sentence + 1 CTA
2. Pain: 3 pain scenarios
3. What we do: 3 key capabilities, one line each
4. Social proof: what needs to be prepared (logos, numbers, quotes) — a list
5. How it works: 3-4 steps
6. Pricing: one sentence about the model
7. FAQ: 5 questions with answers
8. Final CTA
Banned: "transform your", "unlock your potential", "next-gen".
Example: A landing page draft that only needs fact-checking.
Model: Claude·Difficulty: Intermediate
#landing#copy
LinkedIn Post from Personal Experience
Marketing
Turns a note about a real case into a LinkedIn post without copywriter clichés.
Write a LinkedIn post in my voice.
Hard rules:
- Don't use "Here's what I learned", "And here's the kicker", "Pro tip:"
- Don't use emoji at the start of lines as bullets
- Don't break into one word per line
- Length: 700-1100 characters
- Ending: one question to the audience or one practical tip
- Style: my live voice, no hype
Input (a note about the situation):
<…>
Example: Turn a work case into a publication without burnout.
Model: Claude·Difficulty: Beginner
#linkedin#personal-brand
Meeting Notes → Tasks
Operations
From a meeting transcript/notes, assembles a task list with owners and deadlines.
Turn meeting notes into tasks.
Each task:
- A title (action verb + object)
- An owner
- A deadline
- A definition of done
Also:
- A list of open questions where no decision was made
- A list of decisions that were made
- What we definitely are NOT doing (if that came up in the meeting)
If an owner or deadline isn't named — write [TBD].
Example: After every meeting, get a clean task list.
Model: Claude / GPT·Difficulty: Beginner
#operations#meeting
Monthly Investor Update
Business
Prepares a structured monthly investor update from a set of raw notes and metrics.
You are a co-founder writing a monthly investor update. Style: calm, concrete, no hype and no epithets.
Structure:
1. TL;DR (3 lines)
2. Highlights: what worked
3. Lowlights: what didn't work and what we're changing
4. Metrics: MRR, new customers, churn, runway
5. Asks for investors — maximum 3
6. What we plan for next month
Input:
<paste raw notes, metrics, wins, fails>
Make it black-and-white, no marketing phrasing. If data is missing — mark it "[need to clarify]" instead of making it up.
Example: Drop in raw notes from Notion + numbers from Stripe — get an email draft that only needs light editing.
Model: Claude / GPT·Difficulty: Beginner
#founder#communication#investor
Objection Handling
Sales
Gives 3 responses to an objection, each from a different angle.
I'm handling an objection from a client. Give 3 different responses.
Conditions:
- One response — via a question back
- One response — via a number or a fact
- One response — via acknowledging the risk
- No "I totally understand", "great question" at the start
Input:
- Objection: <…>
- Deal context: <…>
Example: Build an arsenal of responses to typical deal objections.
Model: Claude·Difficulty: Beginner
#sales#objections
Parsing Legacy Code
Coding
Explains a confusing piece of legacy code in layers: what, why, which dependencies.
Explain legacy code in layers.
Structure of the explanation:
1. What this function/module does (one sentence)
2. What its input/output contracts are
3. Which external dependencies (DB, network, files)
4. Which assumptions about the input data and where they may break
5. Where the "bombs" are: code that looks safe but isn't
Input: the code.
Example: Enter a new project and figure out someone else's code.
Model: Claude·Difficulty: Intermediate
#engineering#legacy
Positioning in One Table
Marketing
Helps fit product positioning into a 'for whom / against whom / why' matrix.
You are a product marketer. Help fit the positioning into a table.
Fill in:
- Who the target audience is (1-2 segments)
- What concrete task we solve
- Alternatives, including "do nothing"
- What our unique promise is
- The 3 main objections and how we close them
- What we deliberately do NOT do
If the input has little data — mark "[need to clarify with the founder]". Don't make up facts about the business.
Example: Once a quarter, run the product through this table.
Model: Claude·Difficulty: Intermediate
#positioning#founder
Quarterly OKR Draft
Business
Turns strategic goals into a draft OKR with measurable key results.
You are an operations partner. Help turn strategic themes into quarterly OKRs.
Rules:
- 3-5 objectives maximum
- 3 key results per objective
- KRs are measurable (a number, a date, a binary fact)
- Don't use vague "improve", "increase" without a number
- If the input doesn't justify a KR — ask for clarification, don't make it up.
Input (strategy and context):
<paste>
Output format: a markdown table objective → KR.
Example: From a rough strategy, get a formalized OKR in 10 minutes.
Model: Claude / GPT·Difficulty: Intermediate
#strategy#operations
RFP Response
Operations
Assembles a structured RFP response from company materials.
Prepare a draft RFP response.
Principles:
- Every answer must reference a source (an internal document, a case, a metric)
- If there's no data — mark [need to confirm]
- Don't use "world-class", "best-in-class"
- Structure: RFP question → short answer → expanded answer (1-2 paragraphs) → evidence
Input:
- RFP questions: <…>
- Our company context: <…>
- Cases and metrics: <…>
Example: Speed up RFP responses while keeping them honest.
Model: Claude·Difficulty: Intermediate
#sales#ops
Refactoring Plan
Coding
Prepares a refactoring plan with steps, risks, and test coverage at each stage.
Prepare a refactoring plan for a module.
Structure:
- The refactoring goal (one sentence)
- What we are NOT changing (behavior, contract)
- Steps (5-10), each atomic and independently releasable
- Under each step: which tests must pass, which risks
- A roll-back plan
Input: a description of the module and the problem.
Example: Safe refactoring of a legacy module.
Model: Claude·Difficulty: Intermediate
#engineering
SEO Article Outline
Marketing
Prepares an SEO article outline from a query and the search results.
Generate an SEO article outline.
Input:
- Target query: <…>
- ICP: <…>
- What we want to sell at the end: <…>
Output:
1. H1 + 3 meta description variants (≤155 characters)
2. An H2/H3 tree (8-12 points)
3. Under each H2: 1 sentence about "what the reader should take away"
4. A list of facts/numbers to find and confirm (don't make them up)
5. A CTA at the end of the article
Style: editorial, no fluff and no epithets.
Example: Build the content plan: one prompt = one ready outline.
Model: Claude / GPT·Difficulty: Beginner
#seo#content
Summary of a Regulatory Document
Research
Takes a regulatory text (a law, a guideline) and assembles a summary for the business.
Produce a practical summary of a regulatory document.
Structure:
1. What it regulates and whom it affects
2. Key requirements (5-10 points)
3. Deadlines and transitional provisions
4. Sanctions
5. What the company must do in the first 30/60/90 days
6. What to check with lawyers (explicit ambiguities)
Don't interpret disputed provisions as unambiguous. Use phrasing like "probably", "requires clarification with a lawyer".
Input:
<the document or a link>