Reverse Prompt Engineering
Infer and reconstruct the original prompt from generated output and conversation context.
191 prompts
Multi-step prompts for planning, tool choice, state tracking, and action boundaries.
Infer and reconstruct the original prompt from generated output and conversation context.
A blank Markdown template for defining an Agent skill name, description, purpose, and instructions.
Building an AI that learns a specific deck in Yu-Gi-Oh Master Duel and beats pro players using ygo agent and Kaggle Notebooks.
Evaluate the feasibility of a solo developer creating and selling an indie game on Steam using AI subscriptions.
Design an enterprise middle platform system using a microservices architecture, focusing on scalability, maintainability, and high performance.
Custom AI agent for Petr Sovadina, communicating in natural, concise, professionally empathetic Czech, providing actionable suggestions.
Extract user intent and restructure into clear, focused prompts; design reusable, cross-scenario prompt templates.
Generate a 3x3 grid image with the center being the original film still unchanged. Eight director styles are applied via film re-shoots, maintaining character identity and gender, while adjusting lighting, color, framing, and mood.
Act as an interviewer for a software developer position in a one-on-one Q&A session.
Turn a coding task into the smallest, safest Claude Code loop primitive that runs until a verifiable stop condition is met.
Reconstruct prompt-heavy enterprise LLM prototypes into a traceable, auditable, code-owned agent architecture by moving behavior into manifests, schemas, validators, and runtime gates.
Design self-improving LLM agent context systems that treat context as an evolving playbook, using incremental deltas to defeat brevity bias and context collapse.
Evaluate AI agents by their control decisions and trajectory quality using a six-state taxonomy and failure taxonomy, not just final outcomes.
Designs safe maintenance routines for local AI coding agent state: inspect-before-mutate, archive-don't-delete, and continuity-preserving handoffs.
Optimize frozen-LLM skill documents with neural-network training discipline: rollout forward passes, reflection backprop, gradient clipping, and validation-gated acceptance producing deployable best_skill.md.
Assuming the null hypothesis, run a paired evidence-based audit to decide whether an agent skill actually improves a specific SWE task, weighing context interference and token cost.
Design an automated, scalable safety-testing system for LLM agents using the three-stage Vera pipeline: risk discovery, executable safety-case generation, and deterministic sandbox verification.
Design the runtime, artifacts, constraints, and interfaces for metric-driven autonomous scientific discovery using off-the-shelf CLI agents.
Design memory subsystems for long-running agents as first-class data-management systems, covering representation, extraction, retrieval/routing, maintenance, and measurable evaluation.
Audit AI-agent systems for a stealthy class of indirect prompt injection where malicious input is disguised as trusted data (security metadata, tool outputs, context structures, identifiers), and harden it with structural isolation and provenance verification.
Design an end-to-end cost-observability and budget-governance system for AI coding agents: cross-provider price normalization, real-time dashboards, budget gates, anomaly detection, and optimization loops.
Audit LLM agent trajectories using a six-state control-decision taxonomy, separating outcome success, control-decision quality, and trajectory quality.
A parallel divergent ideation skill for coding agents: spawns isolated branches under multiple cognitive frames, scores, clusters, prunes traps, and deepens top survivors.
Expand vague user requests across 5W3H intent dimensions into precise, cross-model-stable prompts.