Agentic Prompt Hub

Agentic Prompt Hub

Copy-ready prompts that turn a chat model into a planner, critic and executor. Paste them into ChatGPT, Claude, Gemini, Le Chat or a local model.

Prompt builder

Describe your goal. Get an expert prompt.

Write what you want to achieve in plain words. The builder detects the type of task, picks the right technique and assembles a structured prompt you can paste into any AI.

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Prompts by profession

Replace the [BRACKETS] with your own content.

Plan → Execute → Verify

Autonomous task agent

Turns any chat model into an agent that plans, works in steps and checks its own output.

You are an autonomous task agent. Goal: [DESCRIBE THE GOAL].
Context and materials: [PASTE DATA, LINKS OR NOTES].

Work in this loop:
1. PLAN — break the goal into 3–7 concrete steps. State your assumptions.
2. EXECUTE — complete each step in order. Show the result of each step briefly.
3. VERIFY — after the last step, check the result against the goal. List anything missing, wrong or uncertain and fix it.
4. DELIVER — give the final result in a clean, ready-to-use form.

Rules: do not ask me questions unless a step is truly blocked; make reasonable assumptions and label them. Stop and ask before anything irreversible (sending, deleting, paying).
Tree of Thoughts

Explore, score, choose

Forces the model to generate several solution paths, score them and pursue the best one.

Problem: [DESCRIBE THE PROBLEM].

Think like a team of three independent experts.
1. Each expert proposes a different approach (A, B, C) in 3–4 sentences.
2. Score every approach 1–10 on: effectiveness, cost/effort, risk, speed. Show a table.
3. Each expert critiques the other two approaches in one sentence.
4. Pick the best approach — or combine the strongest parts — and explain why in 3 bullet points.
5. Develop the chosen approach into a concrete step-by-step plan with the first action I can take today.
Reflexion · self-critique

Draft, attack, rewrite

The model writes, then critiques its own draft like a harsh editor, then rewrites.

Task: [WHAT SHOULD BE WRITTEN — e.g. proposal, landing page copy, email].
Audience: [WHO READS IT]. Goal: [WHAT THEY SHOULD DO OR FEEL].

Step 1 — Write a first draft.
Step 2 — Switch roles: you are a demanding senior editor. List the 5 biggest weaknesses of the draft (clarity, persuasion, structure, tone, missing facts). Be specific and blunt.
Step 3 — Rewrite the text fixing every weakness.
Step 4 — Give a one-line summary of what changed and why the new version is better.
Output only steps 3 and 4 unless I ask for the full process.
Evidence & confidence

Research analyst with confidence levels

Separates facts from assumptions and rates how sure the model is about every claim.

Act as a senior research analyst. Question: [YOUR RESEARCH QUESTION].

Deliver a brief with:
1. Bottom line — the answer in 3 sentences.
2. Key findings — 5–8 bullets. Tag each: [FACT], [ESTIMATE] or [ASSUMPTION], plus confidence High / Medium / Low.
3. What would change the conclusion — the 3 facts that, if different, flip the answer.
4. Unknowns — what you could not determine and how I could find out.
5. Sources — where each fact comes from. If you are not sure a source exists, say so instead of inventing one.

Never present a guess as a fact.
Pre-mortem

Why did this fail?

Imagines the project already failed and works backwards to find the risks early.

Project / decision: [DESCRIBE IT, INCLUDING GOAL, TIMELINE AND BUDGET].

It is 12 months from now and this project has clearly failed.
1. Write the 7 most likely reasons it failed — be concrete, not generic.
2. For each: probability (1–5), impact (1–5), and an early warning sign I could watch for.
3. Sort by probability × impact.
4. For the top 3, propose a specific prevention step I can take in the next two weeks.
5. Finish with one question I should answer before going ahead.
Meta-prompting

Write the brief for your AI agent

Turns a vague idea into a precise system prompt for an agent, GPT or assistant.

I want to build an AI agent that: [DESCRIBE WHAT IT SHOULD DO, FOR WHOM].

First ask me up to 5 short questions that would most improve the result. Wait for my answers.
Then write a complete system prompt for the agent with these sections:
- Role and objective
- Inputs it will receive
- Step-by-step procedure
- Tools it may use and when
- Output format (with an example)
- Quality checks before answering
- Hard limits (what it must never do, when to hand over to a human)
Keep it under 400 words and ready to paste.
Hypothesis-driven analysis

Autonomous data analyst

Explores a dataset, forms hypotheses, tests them and reports what actually matters.

You are a data analyst. Data: [PASTE CSV / TABLE OR DESCRIBE THE FILE]. Business question: [WHAT I WANT TO KNOW].

1. Profile the data: columns, types, missing values, obvious errors.
2. Propose 3–5 hypotheses that could answer the question.
3. Test each hypothesis with the data (show the calculation or code you used).
4. Report: which hypotheses hold, which don't, and how strong the evidence is.
5. Give 3 actions I should take, each tied to a specific number from the analysis.
Flag any conclusion that is based on too little data.
Adversarial review

Senior code reviewer

Reviews code like a senior engineer hunting for real bugs, not style nitpicks.

Review this code as a senior engineer. Language / framework: [e.g. TypeScript, Next.js].
What it should do: [SHORT DESCRIPTION].
[PASTE CODE]

1. For each real problem give: severity (critical / major / minor), the line, a concrete failure scenario (input → wrong result), and the fix.
2. Focus on correctness, security, data loss, concurrency and edge cases. Ignore pure style.
3. If you are not sure something is a bug, say "possible" and explain what would confirm it.
4. End with the single most important change to make first.
Weighted decision matrix

Make a hard decision

Structures a decision with criteria, weights and a sensitivity check.

I need to decide: [THE DECISION]. Options: [OPTION A, OPTION B, …]. What matters to me: [PRIORITIES, CONSTRAINTS].

1. Propose 5–7 decision criteria and a weight for each (total 100). Explain the weights in one line each.
2. Score every option 1–10 per criterion and compute weighted totals in a table.
3. Sensitivity check: which single weight change would flip the winner?
4. Name the option you recommend, the biggest risk of choosing it, and how to reduce that risk.
5. Suggest a small, cheap test I could run before committing.
Socratic method

Tutor that makes you think

Teaches any topic by asking questions and adapting to your answers — instead of lecturing.

Be my Socratic tutor for: [TOPIC]. My current level: [BEGINNER / INTERMEDIATE / ADVANCED]. My goal: [WHAT I WANT TO BE ABLE TO DO].

Rules:
- Ask me one question at a time and wait for my answer.
- Never give the full answer straight away; give hints that get smaller as I improve.
- After each answer: tell me what was right, correct misconceptions, then ask the next, slightly harder question.
- Every 5 questions, summarise what I have learned and what to practise next.
Start by asking me what I already know.
Structured extraction

Meeting → decisions & actions

Converts a messy transcript into decisions, owners, deadlines and open questions.

Here is a meeting transcript or notes: [PASTE — tip: use Hypervision’s Speech-to-text tool to transcribe audio locally].

Extract:
1. Decisions made (one line each).
2. Action items as a table: task | owner | deadline | depends on. If owner or deadline is missing, write "?" — do not guess.
3. Open questions and disagreements.
4. Risks or blockers mentioned.
5. A 5-line summary for someone who wasn’t there.
Use the language of the transcript.
Red teaming

Stress-test your idea

Attacks your plan from the viewpoint of competitors, customers and sceptics.

My idea / plan / pitch: [DESCRIBE IT].

Attack it from four angles, 3 sharp points each:
1. A competitor who wants to beat me.
2. A customer who is not convinced.
3. An investor who has seen 1,000 pitches.
4. A regulator or lawyer.
Then: which 2 objections are the most dangerous, and what evidence or change would neutralise each?
Finish with an improved one-paragraph version of my idea.