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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.'
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.
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.
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.'
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.
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.
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.
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.
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.
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.
Upload the repo and Claude returns an architectural map, the top 5 fixes to prioritize, and the file paths to back them up.
A one-page decision memo for a CEO, board, or VC. Compresses 30 pages of analysis into a single clear document readable in 5 minutes before a meeting, in the style of Amazon's PRFAQ or McKinsey's SCQA.