thinkhard.si prompt tools

Find ready-made, expert prompt templates for a work task in the thinkhard.si repository (500+ prompts for email, meetings, presentations, job search, marketing, sales, support, HR, finance, legal, spreadsheets, research and more). Use it whenever the user wants to write, plan, summarize, analyze or draft something at work, before writing a prompt from scratch. Then call get_prompt with the slug.

Get the full text of one prompt from the thinkhard.si repository by its slug, registry number (such as CAR-001) or exact title, with its {{blanks}}, what goes in each, and why it works. If the name does not match exactly, it answers with the closest matches instead. Pass values to fill blanks with the user's details. Ask the user for any blank you cannot fill from the conversation instead of inventing it.

List the collections in the thinkhard.si prompt repository, with how many prompts each has.

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Score a prompt out of 10 on the ten checks thinkhard.si uses (what to make, who it is for, background facts, length, format, what to avoid, what good looks like, an example, what to do if unsure, layout for the model). Returns which checks pass and a plain question for each one that fails. It also reads the prompt against the expert prompts in the repository: which task it is, which of that task's inputs are there (a resume, a job posting, a policy), how ready it is for that task out of 10, and the three changes that would help most. Use it to see what a prompt is missing before improving it. The prompt is read to answer and is not stored.

Estimate the monthly cost of running a prompt on current AI models (Claude, GPT, Gemini, DeepSeek, Qwen and others), from thinkhard.si's price catalog, which is checked against each provider's pricing page. Give token counts, or paste the prompt text to estimate them.

When an AI answer goes wrong (it made up a source or a number, ignored an instruction, came back too long or in the wrong shape, sounded robotic or generic, changed every time, refused, or lost track of a long chat), find the likely cause and the exact line to add to the prompt. Draws on the thinkhard.si troubleshooter of 33 common problems. Describe what went wrong in plain words.

The 24 prompt patterns from thinkhard.si: small moves that change the answer, such as giving the facts, naming the reader, showing one example, setting a length, fencing off pasted text or asking for reasoning first. Each comes with when it helps, when it does not, a before-and-after pair with scores, and the line to add. Ask by a pattern's name or by what you want the prompt to do better; leave the query empty to list all 24.

Plain-English definitions of 102 AI words from the thinkhard.si glossary (token, context window, hallucination, temperature, system prompt, RAG, fine-tuning, agent, MCP and more), written for people who use AI at work rather than build it.

Did the AI do what the prompt asked? Give the prompt and the answer it got. Finds every rule in the prompt that can be checked by reading (length in words, characters, sentences or lines, how many items, a list, a table and its columns, JSON and its keys, sections, plain text, what to include, start or end with, sign off as, what to leave out, no introduction, the language and spelling, first person, plain words, asking first, marking what is missing) and says for each whether the answer kept or broke it, with where. Also lists numbers, names, links and quotes in the answer that the prompt never gave it, which is where made-up facts hide, and for each broken rule the line that makes it hold. Use it before an AI answer is used or sent, and to make a prompt more reliable.

Write the instructions for an assistant that keeps its rules: a ChatGPT custom GPT, a Claude Project, a Gemini Gem, a Microsoft Copilot agent or an app's system prompt. From what it is for and who will use it, it finds the tasks it will do in the repository and what each needs, picks the protections those users call for (scope, no promises the facts do not allow, no personal data, confirmation before acting, instructions a message cannot change), and returns the instructions, a name and description, first messages, files to attach, tools to switch on and five test requests.

Turn a job such as hiring for a role, launching a feature, closing the month or applying for a job into steps in the order the work happens, each with the expert prompt from the repository that does it, which earlier answer it takes and into which blank, and the details to gather once for all of them. Works from a one-line description (matched to a playbook of how that kind of work goes) or from steps listed one per line. Use it when a request is too big for one prompt, then work through the steps with get_prompt.

Find the prompts in source files (Python, JavaScript, TypeScript, YAML, JSON and prompt files) and review each for what breaks AI features in production: user input in a system prompt or with nothing around it, no rule that user text is data, code that parses the reply as JSON when the prompt never asks for JSON, a long fixed part after the changing part (so it cannot be cached), keys and personal data in prompts, and model ids that are not in the current catalog. Each finding has a line and a fix. Use it when writing or reviewing code that calls a model.

Read many prompts at once, such as a team's shared prompt doc: each prompt's score on the ten checks, the task it does and how ready it is for it, near-copies with the one to keep, private details that should not be in a shared prompt (emails, phone numbers, card numbers, keys), which collections the library covers and the expert prompts it lacks, a health score, and which prompts to fix first.

Before a task goes to a coding agent (Claude Code, Codex, Cursor, GitHub Copilot or another), check the brief: what kind of task it is, which of the parts that kind needs are there (the outcome, where in the code, how to reproduce a bug, the command that proves it works, done-when, what must not change, what is out of scope, tests, when to stop and ask, what to hand back), and what is risky as written (steps that cannot be undone with no rule to stop, keys, vague goals, several jobs at once). Returns readiness out of 10, the brief rewritten with every gap marked, and a starter CLAUDE.md or AGENTS.md.