PROMPTS
1346 original and curated AI prompts across image, video, music, and tutorials. Browse by tool, use case, and difficulty; each entry shows its available notes and examples.
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Claude Life OS Prime: Turn the Chat Window Into a Productivity Dashboard
Not a chatbot — think Notion AI + Motion + Atomic Habits in one. Tracks habits, goals, energy, and deep work; auto-detects behavioral patterns; flags burnout risk.
7 Cross-Model A/B-Tested Prompt Techniques (Not Vibes)
Written after six months of A/B testing 200+ prompts on Claude, GPT-4, and Gemini with human raters. Covers persona + constraint stacking, anti-examples, role-reversal QA, cognitive scaffolds, emotional priming, uncertainty CoT, and steelman-first.
Interview Transcript to Story Synthesis
Turns a 3-hour interview recording into a 2,000-word brand story, profile piece, or documentary script. Claude selects the key passages, preserves the original voice, and stitches fragmented moments into a narrative with a real arc.
Diagnose a generation failure from observable differences
Separate intent, observed output and evidence before making one reversible revision.
Project Breakdown Prompt: One-Line Requirement to WBS Timeline + Risk Alerts
Paste in a one-line project requirement, the deadline, and available headcount, and the AI produces a three-level WBS breakdown, a milestone timeline worked backward from the deadline, the critical path, and the top three risk alerts; if the deadline isn't feasible, it tells you straight whether to cut scope or add people.
Claude Code Backend Architect Subagent: Contract-First API Design
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.
Claude Code Output Style Recovery: SessionStart Hook + Marketplace
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.
Superpowers: Brainstorm, Plan, and True TDD for Claude Code
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.
A Production-Grade Layered-Architecture AGENTS.md / CLAUDE.md Template for FastAPI
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.
Build an MCP Server LLMs Can Actually Use, with Anthropic's mcp-builder
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.
Automated Design Review: UI/UX/Accessibility Audits via Playwright MCP
A curated, ready-to-use version of OneRedOak's design-review workflow: using Playwright MCP in a real browser environment, it automatically audits frontend changes across 7 stages (interaction, responsiveness, visuals, accessibility, robustness, code health) and reports findings graded Blocker/High/Medium/Nitpick.
Claude Code Hooks Builder: Auto Format/Lint/Tests After Every Edit
Uses hooks in .claude/settings.json to automatically run formatting, linting, type-checking, or tests at specific Claude Code lifecycle events (PostToolUse after an edit, PreToolUse before running a command, Stop when wrapping up)—producing correct JSON config plus an executable hook script, with exit code 2 hard-blocking dangerous actions.
Claude Code Subagent Fan-Out: Splitting Big Tasks Into Parallel Subagents
Has Claude Code break a large task into several independent, parallel subagents (each with its own context, restricted tools, and an assignable model), planning how to fan the work out, defining each subagent's responsibility boundaries and reporting format, and producing reusable .claude/agents subagent definition files when needed.
Safe Multi-File Refactoring: Map Blast Radius, Then Batch and Verify
A large-refactor playbook for agents: first do a read-only survey of the 'blast radius' — which files, call sites, and tests are affected — then split the change into small batches, each preserving behavior, each run through its own tests, each its own atomic commit. Puts an end to 'change 40 files at once, then the build breaks and no one knows where to start fixing it.'
AEO Content: Write Passages ChatGPT / Perplexity Will Actually Cite
The new traffic battleground in 2026 is getting cited by AI. Write your topic in the format AI models love to pull from: direct answers, clear structure, and authority signals.
AI Agent Task Spec: Turn Vague Requests into Executable Instructions
2026 has entered the agent era — this template turns 'I want X' into an executable spec for an agent: goal, steps, available tools, definition of done, and boundaries.
Context Engineering: What Separates You in 2026 Isn't the Incantation, It's the Ingredients
Feed the AI structured background, what's already been tried, constraints, and available tools/data structures before giving it the task — in 2026 the real leverage point has shifted from 'prompt tricks' to 'context.'
Prompt Chaining: Split a Big Task into Three Steps for Better Quality
Don't feed an entire complex task into a single prompt — split it into three separate steps ('decompose → produce each part → integrate'), and the output is noticeably more stable and complete.
XML Tag Structuring: 28% Fewer Parsing Errors Than Markdown
Wraps instructions, data, rules, and schemas in XML tags when feeding them to an AI — tested to produce roughly 28% fewer errors on structured-extraction tasks than using Markdown headers.
A Red Teamer's View: 3 High-Capability Prompt Patterns That Work Across Models
Three patterns shared by the HackAPrompt 2.0 champion: frame the task as an audit, make the abstraction level explicit, and stage the context like production. They work on Claude, GPT, and Gemini alike.
AI Agent Harness Engineering: Separating the LLM from the Deterministic Orchestration Layer
Mitchell Hashimoto's "Agent = Model + Harness" framework. The reason 88% of enterprise AI agents never reach prod isn't the prompt — it's the missing deterministic orchestration layer. Four CS primitives you can't skip: state machine, idempotency, DAG, DLQ.
reClaim: Turn a Frontier Model Into a Research Agent With Confidence Scores
Scores every claim on three axes (A: sources / B: counterevidence / C: completeness), plus a 4-tier source hierarchy, adversarial self-poking, and 4 output modes.
Flip Your AI Dev Workflow: Generate the Prompt First, Then the Code (4 Phases)
Instead of telling AI to write code directly, have it translate a vague goal into an executable prompt structure first, then use that structure to drive code generation. Phase 1 Intent → Phase 2 Structuring → Phase 3 Tool-Level → Phase 4 Implementation.
Run an Architecture Review on Your Whole Codebase with Claude
Upload the repo and Claude returns an architectural map, the top 5 fixes to prioritize, and the file paths to back them up.