1328 hand-tested AI prompts across image, video, and music — each with full context, variables, and a sample output. Content is authored in Traditional Chinese; the UI here is English.
When you're assigned to run training or onboard new hires, the time-consuming part isn't knowing the content — it's arranging it into an order you can actually teach. This prompt uses the basic instructional-design framework (objectives, motivation hook, explanation, practice, assessment) to produce a complete lesson plan, including time allocation, interaction design, and points where learners tend to get stuck. Works for corporate training, new-hire onboarding, and club/community teaching.
A social media manager's daily grind is finding five different ways to talk about the same product. This prompt first has you define your brand voice and no-go topics, then generates a full batch of posts from five distinct angles — pain point, scenario, data, interactive question, and behind-the-scenes — each with hashtags and a suggested posting time, plus built-in rules to strip out that "obviously AI" tone so every post doesn't read like the same canned copy.
The biggest problem with handover documents is that everything you consider obvious never gets written down. This prompt has the AI act as a handover reviewer, first using structured questions to draw out the tacit knowledge in your head (who to contact, where passwords live, what has to happen every month, where the landmines are), then organizing it into a handover manual the successor can actually follow. Useful before resigning, transferring roles, or taking extended leave.
A full Suno support thread from one user: wrote their own lyrics, generated with Suno, distributed via Amuse, published through Musixmatch — then tried to make an acoustic version, and Suno blocked it entirely with a copyright error. Suno officially confirmed this is a platform-level limitation.
The RefineAnything LoRA + ComfyUI-RefineNode plugin automate the painful "fix one area, leave the rest untouched" task. A lifesaver for product-image flaws, logos, text, and label repair.
Installs a focused 'security auditor' subagent into Claude Code that reviews code and architecture file-by-file through an OWASP Top 10 lens as you develop features, classifying vulnerabilities by severity with directly-applicable remediation suggestions.
Installs a senior 'backend architect' subagent into Claude Code that uses contract-first design and domain-driven design to draw service boundaries, design REST/GraphQL/gRPC APIs, and build resilience and observability into the architecture from day one.
Installs a dedicated 'performance engineer' subagent into Claude Code that profiles code you've just written, finds bottlenecks like N+1 queries, memory leaks, and slow APIs, classifies them by impact, and provides before/after optimization recommendations with benchmark estimates.
A PostToolUse hook that makes Claude Code automatically run a formatter (Prettier/Black/gofmt) plus a linter every time it edits or writes a file—permanently removing 'inconsistent formatting' as a code review chore.
A PreToolUse Bash hook that intercepts destructive commands like rm -rf, fork bombs, and curl|sh before the AI executes them—a last line of defense that holds even against --dangerously-skip-permissions.
A Stop hook that fires a desktop notification and plays a sound the moment Claude Code finishes running. You can freely switch to other windows—the AI will call you back when it's done, no need to keep staring at the terminal.
A single npx command generates an information-rich terminal statusline for Claude Code, showing the current directory, Git branch, model, a context-usage progress bar, real-time spend burn rate, and a countdown to usage reset—hooked into live ccusage stats.
After Claude Code removed its built-in output style feature, this uses a SessionStart hook to automatically inject a custom response style (tone / format / rules) into every session, with a local marketplace for managing, activating, and applying multiple styles.
Brings obra/superpowers' "spec-first + true red-green TDD" development discipline into your agent: brainstorm to clarify requirements first, break the work into verifiable 2-5 minute tasks, then strictly follow RED-GREEN-REFACTOR, with a two-stage subagent review.
Borrowing the three-layer architecture from buildermethods/agent-os (Standards / Product / Specs): have AI first scan your codebase to extract existing conventions into documented standards, then auto-inject them every time it writes code — so you never have to re-explain the rules.
Two AGENTS.md templates drawn from Ischca/awesome-agents-md (CC0): "minimal" is a five-line checklist you can run with right away, and "advanced" covers five sections — environment, testing, conventions, safety, and example tasks. They let an AI coding agent know the rules the moment it enters your project.
Rewrites awesome-cursorrules' Next.js App Router standard into an AGENTS.md/CLAUDE.md you can drop straight into your project root, so Claude Code, Codex, and Cursor automatically follow Server-Components-first, file-based routing, TypeScript, and performance conventions when writing Next.js.
Rewrites awesome-cursorrules' FastAPI production architecture standard into an AGENTS.md/CLAUDE.md that enforces Router→Service→Repository layering, thin handlers, an anti-corruption layer, and idempotency — so AI automatically rejects anti-patterns when writing Python backends.
Rewrites awesome-cursorrules' Go backend scalability standard into an AGENTS.md/CLAUDE.md covering idiomatic error handling, context propagation, interface abstraction, concurrency, and observability — so AI follows standard conventions and trade-off analysis when writing Go services.
A ready-to-paste ~/.codex/config.toml template that sets up OpenAI Codex CLI's approval policy, sandbox isolation, reasoning effort, and multiple profiles in one go — balancing automation efficiency with safety boundaries.
Uses cc-sdd's --codex-skills install to load reusable skills (SKILL.md) into Codex CLI on demand, and replicates its /kiro-impl autonomous execution loop — a fresh implementer per task, an independent reviewer, and automatic root-cause debugging on failure — so Codex advances steadily through a task list one task at a time.
Uses codex exec's non-interactive execution plus a restricted sandbox to plug Codex CLI into CI / scripted workflows for unattended tasks like lint fixes, batch refactors, and auto-repair, guarding the safety boundary with approval_policy=never paired with a workspace-write sandbox.
Anthropic's official skill-creator: a meta-skill that "asks you clarifying questions, produces a SKILL.md, runs evals, refines the trigger description, and packages everything into a .skill file." It teaches you to build reusable Claude skills using a standardized directory structure and progressive disclosure.
Anthropic's official mcp-builder: a four-phase workflow (research & planning → implementation → review & testing → writing evals) for wrapping your API or service into a high-quality MCP server. Covers both Python (FastMCP) and TypeScript (MCP SDK), with emphasis on tool naming, input/output schemas, error messages that point toward a fix, and evaluating with real-world tasks.