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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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.
System vuln scanner: analyze your architecture, find attack chains, estimate time-to-compromise
More than "list the CVEs." It finds how individual weaknesses chain into an attack and estimates how long an AI-augmented attacker would need to break in. A take on the OpenAI Daybreak concept, built for small teams. Feed in a stack description (Next.js + Supabase + Clerk, etc.) and get back critical paths plus a patch-priority order.
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.
Grading essays as a teacher: a 3-step chain (lens → diagnose → tone)
A method dreamed up by a teacher 19 essays into a 30-essay all-nighter. Where typical AI prompts "act as an English teacher" and devolve into a wall of rubric noise, this chain solves the real grading workflow: don't comment on everything.
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.
Read 10 papers at once and synthesize them with Gemini
Upload 10 PDFs and Gemini automatically maps the consensus, disagreements, and gaps, then drafts a literature review.
Auto-Brief a 10-K with Gemini
Upload a public company's annual or quarterly report PDF and auto-generate a management-quality + risk read plus an investment thesis
Prioritize your product feature list with Gemini
Upload customer feedback, NPS data, or support tickets alongside your feature backlog, and Gemini runs a RICE or Kano model analysis.
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.
Deep Document Analysis with Gemini's 1M Context
Put Gemini's 1M-token context to work — feed in an entire PDF or cross-reference multiple documents at once
Whole-Codebase Overview with Gemini
Drop the entire repo into Gemini → get an architectural overview + improvement suggestions
1-Page Decision Memo for the CEO
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.
PRO15Customer Interview Synthesizer: 10 Interviews Into 3 Insights
For product managers and founders. Strips the noise out of multiple interviews and extracts the shared patterns and pain points.