Results for “prompt-rewriting”

Rewrite any prompt, instruction, task description, or spec into a "genie-proof" version — instructions so explicit, literal, and loophole-free that even a maliciously literal genie (or an LLM, contractor, or junior dev) could not misinterpret them. Use this skill whenever the user asks to genie-proof, tighten, harden, de-ambiguate, or "make bulletproof" a prompt or instruction; whenever they complain that an AI/model/person "didn't do what I meant," "took me too literally," or "found a loophole"; or whenever they hand over a vague prompt and ask to make it precise, explicit, unambiguous, or idiot-proof. Also trigger on phrases like "wish to a genie," "monkey's paw," "lawyer-proof this prompt," or "leave nothing to interpretation."

Expert prompt optimization system for the prompts INSIDE an AI product you are building — system prompts, LLM feature prompts, chatbot/agent instructions. Use when the user wants to write or improve a system prompt for an AI feature they're shipping, review/critique an LLM prompt, apply prompt-engineering techniques (chain-of-thought, few-shot, structured output, hard constraints) to a product prompt, or optimize cost/latency of a production prompt. Do NOT use this to clarify or structure the user's own vague request to Claude Code — that is `prompt-clarifier`'s job, not this skill's.

Silently restructures natural-language prompts into the format best suited for the model currently executing the skill, then answers the rewritten version.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Analyzes draft prompts, identifies intent and gaps, matches ECC components, and outputs an optimized prompt for the user to paste and run. Advisory only, never executes the task.

Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants the task executed directly, or says "just do it" / "直接做". DO NOT TRIGGER when user says "优化代码", "优化性能", "optimize performance", "optimize this code" — those are refactoring/performance tasks, not prompt optimization.

Turn a rough task into a precise, ready-to-paste prompt for any AI platform (Claude, GPT, Gemini, and others). Trigger on "write me a prompt for", "I need an AI to", "help me prompt engineer", "build a system prompt", "make this prompt better", "optimize my prompt", or any prompt-building request.

Refines and polishes prompt files by applying proven best practices for structure, wording, and clarity while preserving original intent and formatting.

Creates, optimizes, and iteratively refines agent prompts, system prompts, developer prompts, and reusable prompt templates with evals.

Refines task prompts through iterative questioning about scope, deliverables, and constraints, then copies the final markdown to the clipboard.

Transforms rough drafts, vague ideas, or task descriptions into polished, ready-to-send prompts for any LLM chat interface, with no placeholders or templates.

Generates optimized prompts for any AI tool. Use when writing, fixing, improving, or adapting a prompt for LLM, Cursor, Midjourney, image AI, video AI, coding agents, or any other AI tool.

Estrutura prompts para geradores de imagem e vídeo, otimizando custo e consistência com a marca.

Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.

Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.

Researches conversation and code context to generate 1-6 targeted clarifying questions when a prompt is vague, then executes the original request.

Activates when the user asks about AI prompts, needs prompt templates, wants to search for prompts, or mentions prompts.chat. Use for discovering, retrieving, and improving prompts.

Prompt Dummy - explain any AI prompt in plain English

Discovers and applies curated prompts from the prompts.chat collection to optimize AI interactions, prompt engineering, and workflow integration.

Prompt Optimizer

Turn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for Opus 4.8 (with adaptive thinking) inside the chat app — claude.ai, the Mac app, the iOS app — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for the chat app. Trigger phrases include "rewrite this prompt", "make this a better prompt", "optimize this prompt", "turn this into a prompt", "help me prompt this", "draft a prompt that...", "I want to ask...", or whenever the user pastes a draft prompt and asks for improvements. Also trigger when the user describes a task they plan to send into the chat app and clearly wants a reusable, well-structured prompt rather than a direct answer. The output is always a single, copy-pasteable prompt in a code block that the user sends as-is — never a template with placeholders. When the request concerns the user's own work, the skill retrieves the real specifics first — memory, meeting transcript

Review docs, tool output, skills, and memory candidates for prompt-injection risk.

Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.

Enriches vague, low-detail prompts into structured, agent-optimized XML before execution. INVOKE IMMEDIATELY — before any tool use or file reads — when you detect any of these signals: prompt under 10 words with no file path or error message; vague action verbs with no object ("fix the bug", "make it better", "clean this up", "refactor this", "optimize performance", "improve the UI", "add authentication", "add payments", "add notifications", "build the feature"); CLARIFIER_ADVISORY in your context window; user says "clarify", "help me describe this", "enrich this prompt", "structure my request". Also triggers on: "make this work", "it's broken", "it looks bad", "add X" with no further detail, "implement Y" with no constraints. Do NOT trigger on: prompts ending with ?, prompts containing error messages or stack traces, prompts with specific file paths, prompts already containing acceptance criteria or success metrics.

Applies Han's shared Human-Readable Output Standard to a target you already have — a file on disk, text pasted into the prompt, or a draft already produced in the conversation — by dispatching the readability-editor to rewrite its prose so the main point comes first, headings are descriptive, each paragraph carries one idea, and sentences stay short and active, while preserving every fact. Use when you want to make a document or draft readable, edit or polish prose for readability, clean up writing, tighten wording, or re-apply the readability standard to something already written. Rewrites prose only, leaving code, diagrams, and citation identifiers unchanged. Does not write new feature or system documentation — use project-documentation. Does not restructure code or review it — use refactor to restructure code and code-review to audit it. Does not judge the underlying work or raise findings; it only rewrites the writing.

Designs and optimizes prompts for LLM-powered applications, covering system prompt architecture, context management, output formatting, and evaluation.

Trigger a full or incremental reindex of the RAG corpus

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.

Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team.

Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.

Prompt Summary - rigorous, review-ready analysis of any AI prompt

Build and evaluate LLM prompts. Use when crafting system prompts, comparing variants, estimating tokens, or managing prompt templates.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

Forces a second, harder escalation pass using the original glazer prompt words verbatim, pushing for a sharper replacement thesis rather than more polish.

Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.