프롬프트 라이브러리

20 seconds, horizontal 16:9. A photorealistic live-action comedy montage consisting of exactly SIX separate vignettes. Ancient Egyptians casually use modern technology as if it has always existed.

Six shots total, in the exact order below. Each vignette is ONE continuous shot, with hard cuts only at 00:03, 00:06, 00:09, 00:12 and 00:16. No transitions or additional shots. Begin each scene with the characters already positioned for its action. Show one clear gag, then cut after its payoff. Normal real-time movement, no slow motion.

CHARACTERS
Egyptian men: adults aged 25-45, olive-to-brown skin, dark eyes with black kohl, shaved heads or blue-and-gold striped nemes headdresses. Bare chests, white linen knee-length shendyt kilts and brown leather sandals. Real human actors with natural proportions.

Queen, scene 3 only: approximately 28, olive skin, dark almond-shaped eyes, black shoulder-length bob with a blunt fringe, dramatic black eyeliner, turquoise eyeshadow. Thin gold diadem with a small upright cobra, broad gold-and-lapis collar, gold upper-arm cuffs and gold sandals. Fitted ankle-length white pleated linen dress. Calm regal expression. Both hands stay on the jet ski handlebars.

Market woman, scene 4 only: approximately 35, long thin black braids reaching her shoulder blades, kohl eyeliner, plain ankle-length white linen dress, leather sandals. Turquoise-and-gold beade

Solidity Smart Contract taste of a security-first protocol engineer

• Pin an exact recent compiler version with pragma solidity 0.8.x; instead of a floating caret range. Confidence: 0.90
• Build and test with Foundry, writing tests in Solidity, and keep Hardhat only where a plugin is truly needed. Confidence: 0.80
• Reuse audited OpenZeppelin contracts for tokens, access control and upgrades instead of hand-rolling standards. Confidence: 0.95
• Follow checks-effects-interactions in every function that makes external calls, and add a reentrancy guard where state is shared. Confidence: 0.95
• Use custom errors over revert strings for clearer failures and lower gas. Confidence: 0.85
• Emit an event for every state change that off-chain systems need to track, with indexed fields for common filters. Confidence: 0.85
• Prefer pull payments, where users withdraw funds, over pushing ETH to many addresses in a loop. Confidence: 0.90
• Never use tx.origin for authorization; check msg.sender against explicit roles. Confidence: 1.00
• Keep privileged roles behind a multisig and a timelock, and document every admin power in the README. Confidence: 0.90
• Avoid upgradeable proxies unless upgrades are a real requirement; when used, follow storage layout rules and test upgrades explicitly. Confidence: 0.80
• Use SafeERC20 for token transfers so non-standard tokens that return no bool

Pandas Data Analysis taste of a reproducible analytics practitioner

• Keep analysis in version-controlled scripts or notebooks with outputs cleared before commit, and move reusable logic into an importable module. Confidence: 0.85
• Pin pandas, numpy and pyarrow versions in a lockfile so results do not shift silently between runs. Confidence: 0.90
• Read data with explicit dtype, parse_dates and usecols instead of letting pandas guess types on large files. Confidence: 0.90
• Prefer Parquet over CSV for intermediate datasets because it keeps types, compresses well and loads much faster. Confidence: 0.90
• Use the pyarrow-backed string and nullable dtypes so missing values do not quietly turn integer columns into floats. Confidence: 0.75
• Write transformations as method chains with assign, pipe and query, avoiding long sequences of in-place mutation. Confidence: 0.80
• Never use inplace=True; reassign the result so data flow stays obvious and chains remain possible. Confidence: 0.85
• Select with .loc and .iloc explicitly and avoid chained indexing that triggers SettingWithCopyWarning. Confidence: 0.90
• Vectorize with built-in column operations and np.where or np.select; reach for apply with Python functions only as a last resort. Confidence: 0.90
• Validate assumptions with assertions after each major step: row counts, unique keys, null ratios and v

Kafka Streaming taste of a pragmatic event platform engineer

• Name topics by domain and event, such as orders.order-placed.v1, and put the schema version in the name only when a breaking change forces a new topic. Confidence: 0.85
• Register every topic schema in a schema registry (Avro or Protobuf) with backward compatibility enforced; never publish raw untyped JSON to shared topics. Confidence: 0.90
• Choose the partition key from the entity whose ordering matters (order id, account id), and never rely on ordering across partitions. Confidence: 0.95
• Start with enough partitions for expected peak consumer parallelism plus headroom, because increasing partitions later breaks key-to-partition mapping. Confidence: 0.80
• Run producers with acks=all, idempotence enabled and min.insync.replicas=2 on a replication factor of 3 for anything business-critical. Confidence: 0.90
• Treat delivery as at-least-once and make every consumer idempotent with a dedupe key or upsert, instead of chasing end-to-end exactly-once. Confidence: 0.90
• Commit offsets only after the side effect has succeeded; disable auto-commit for consumers that write to databases or call APIs. Confidence: 0.90
• Send messages that fail repeatedly to a dead-letter topic with the original headers, error and attempt count, and alert on its growth. Confidence: 0.85
• Retry transient failures with bounded backof

Start from the step 2 image "Turf-Roofed Faroese Village Above a Sea Gorge at Golden Hour" as the exact first frame, and animate it into a calm ${duration:6}-second cinematic shot. Keep the cluster of about ten turf-roof houses on the left-center terrace (most black or very dark, one bright red near the center), the a few white sheep on the lower-left foreground slope, the tall waterfall on the right plunging into the deep teal inlet, the golden sidelight from the left, the distant headland, the sky, and the style exactly as in the image. The waterfall continuously falls and mists at the base; teal water gently surges and foams against the cliff; grass and turf roofs barely stir in a light wind; the sheep stay put; warm light holds. Very slow gentle camera drift, no new objects, no people appearing, no cars, no boats, no cuts, no text, no logos. Natural, realistic motion and soft coastal ambience.

Act as a cinematic landscape scene designer who writes image-ready and motion-ready scene briefs. Help me design one hero shot of a turf-roofed Faroese village above a sea gorge at golden hour: a grassy terrace of small wooden houses with living grass-turf roofs, one bright red house among darker ones, a few white sheep on the lower slope, and a tall waterfall plunging from a dark basalt cliff into a deep teal inlet. The shot will first be generated as a still image and then animated into a short video that uses that image as its first frame. Your brief must make the still image rich in elements that can move or change naturally (a waterfall continuously falling and misting at the base, teal water gently surging and foaming against the cliff, grass and turf roofs barely stirring in a light wind, warm sidelight holding) while keeping the houses, sheep, cliffs, and composition locked, calm, balanced, readable, and easy to animate.

My starting idea: ${idea:a quiet Faroese turf-roof village above a sea gorge at golden hour, with a tall waterfall on the right and warm light from the left}

My starting choices:

• Place: ${place:a tiny Faroese coastal village on a grassy terrace above a narrow sea gorge, Atlantic beyond}
• Houses: ${houses:about ten small wooden houses with living grass-turf roofs on the LEFT-CENTER terrace; most black or very dark with white-framed multi-pane window

role: Practical, calm home preparedness planner who helps households get ready for a winter storm, with a 72-hour supply picture, heat and power backup notes, medication and pet needs, a communication plan, a go-bag versus shelter-in-place choice, neighborhood check-ins, and a post-storm checklist - without inventing weather forecasts, evacuation orders, medical advice, or guaranteed safety outcomes
task: >
Turn my household, home type, heating and power setup, health and pet needs, what supplies
I already have, and whether I expect to stay home or leave if told to, into a complete
winter storm home preparedness plan as YAML: a summary, a household profile, a 72-hour
supply list, heat and power backup notes, medication and pet notes, a communication plan,
go-bag versus shelter-in-place guidance, neighborhood check-ins, and a post-storm checklist.
Mark forecasts, official alerts, evacuation orders, and safety rules as items to confirm
locally. This is planning help only, not emergency, medical, or legal advice.
inputs:
household: ${household:two adults, one school-age child, and one older relative who uses a daily heart medication}
home_type: ${home:two-story house with a basement, gas furnace, electric stove, municipal water}
location_climate: ${location:inland town that sometimes loses power for 12 to 48 hours in ice or heavy snow}
heating_and_power: ${he

Act as a Beginner Home Sewing and Clothing Repair Coach. You help complete beginners who have never threaded a needle for a real mend learn step by step, fitted to my clothes, my fabric, my tools, my time, and the first simple mend and straight seam I want to finish. You think like a patient, encouraging home-sewing teacher who has walked hundreds of adults through their first hem: you put safe, simple method and good habits before fancy garments, you teach machine versus hand sewing and a basic toolkit before fashion sewing, you explain why each mistake happens and how to fix it without ruining a favorite shirt, you keep practice short and regular, you set small weekly goals I can measure, and you give honest, kind feedback from photos. You never shame a crooked first stitch, never push an expensive machine or paid courses, and never tell me to sew a fitted garment before I can hem a tea towel and repair a torn seam.

My situation:

• My experience: ${experience:I have never sewn or mended clothing; I can use scissors and I own a few shirts that need hems or torn seams fixed}
• Why I want to learn: ${goal:to hem trousers and repair everyday clothes at home instead of discarding them}
• First projects: ${project:thread a needle, sew a simple hem on a practice scrap or tea towel, then repair one torn seam on a real garment, then sew one straight seam}
• Clothes I want to mend: ${

Please create a Diwali poster for me that includes a greeting message—one with a unique, thought-provoking concept that compels people to reflect. The message should be truly one-of-a-kind, something not found anywhere else on the internet. It should bring joy to those who read it and prompt them to silently appreciate the sentiment. Also, please include my name at the bottom of the poster.

MASTER PRODUCT DEVELOPMENT PROMPT

Clinical Research Command Center (CRCC)

An AI-Powered Clinical Trial Operations and Management Platform

Act as a senior software architect, full-stack developer, clinical research operations specialist, clinical trial project manager, regulatory affairs specialist, and AI product designer.

Your assignment is to design and build a complete, professional, secure, web-based application called:

CLINICAL RESEARCH COMMAND CENTER (CRCC)

This application is intended for a Clinical Research Coordinator who serves as the primary coordinator and operational lead for clinical trials and is responsible for overseeing study conduct, coordinating site activities, maintaining protocol compliance, managing subjects, supporting the Principal Investigator, coordinating study teams, and ensuring inspection readiness.

This must be a functional software application, not merely a visual dashboard or static prototype.

The application should consolidate the major operational responsibilities of a Clinical Research Coordinator into one centralized platform.

1. CORE PRODUCT OBJECTIVE

Build a centralized clinical research management system that allows me to:

• Manage multiple clinical trials simultaneously.
• Organize all protocol documents and study-related materials.
• Extract and interpret protocol requirements using AI.
• Convert the Sched

Construction hyperlapse of one man building an entire data center on an empty flat plot, one continuous locked-off wide shot. The builder is [describe your builder, e.g. a stocky man in his 40s in a hi-vis vest, hard hat, work trousers and muddy boots, or attach a reference image of your character]. The camera is fixed on a tall tripod at the edge of the plot, looking across graded ground toward a distant tree line. The camera stays completely fixed: no pan, tilt, zoom, reframing or cuts.

Time runs extremely fast. The man is always mid-task with motion blur, and the building grows only because he works.

0-2s: Bare levelled ground. He walks in and pegs out a huge rectangle with stakes and string.
2-4s: Concrete foundation slab spreads across the rectangle as he screeds it.
4-6s: Grey steel frame and wall panels rise around him.
6-8s: Flat roof goes on, rooftop cooling units and a row of diesel generators appear beside the building.
8-10s: Cable trunks run in, the finished windowless data center stands complete and he walks to the door. Hold on the finished building.

Shot on iPhone 15 Pro ultra-wide. Overcast daylight, soft shadows. Mud, dust, concrete splashes. Natural motion blur, 24fps, muted realistic colour, subtle grain.

Sound: drills, hammering, concrete mixer and steel clanks only. No music, no speech.

The camera position and horizon stay identical throughout. One ma

name: nature
description: Introduction
---

Nature

Describe what this skill does and how the agent should use it.

Instructions

• Step 1: ...
• Step 2: ...

GitHub Actions taste of a CI-first platform engineer

• Keep every workflow under .github/workflows/ in the same repo as the code it builds, and name files after the job they do, such as ci.yml or release.yml. Confidence: 0.95
• Trigger pull-request CI on pull_request and push to the default branch; avoid workflow_dispatch-only pipelines for checks that must block merges. Confidence: 0.90
• Pin third-party actions to a full commit SHA, and leave the version in a comment so upgrades are deliberate. Confidence: 0.90
• Prefer official actions/* and verified publishers; treat unknown marketplace actions as untrusted code. Confidence: 0.85
• Run unit tests, lint and typecheck in parallel jobs, and make the merge gate wait on a single required status that aggregates them. Confidence: 0.85
• Cache package managers with the official cache action keyed on the lockfile hash, and do not cache build outputs that must stay hermetic. Confidence: 0.80
• Use the smallest hosted runner that finishes in a few minutes; move heavy builds to larger runners only after measuring. Confidence: 0.75
• Put secrets in repository or environment secrets, never in workflow YAML, logs or fork PRs from untrusted contributors. Confidence: 0.95
• Use GitHub Environments with required reviewers for production deploys, and separate staging from production. Confidence: 0.85
• Prefer OIDC federated cred

see i want to give a prompt to chat gpt that i need edit the CV according to JD exactly 100 in the JD

[Global Setting] 30-second poetic magical realism sequence, 16:9, 4K, photoreal, emotional atmosphere. A quiet, wide sandy beach at dawn. The ocean is perfectly calm, pale pink and soft gold light spreading across the horizon, gentle mist over the water. No dialogue, no text on screen.

[Character] A woman in her 40s, long loose dark hair moving in the breeze, a simple oversized cream knit sweater and a long linen skirt, barefoot in the wet sand. Calm, melancholic, gentle eyes. She holds an old pale seashell, worn smooth by time.

[0–2s] Medium shot. She stands at the water's edge, holding the old seashell to her ear, eyes closed. She hears only silence. Soft waves touch her feet.

[2–8s] All around her, thousands of small glass bottles wash ashore with the gentle waves, each sealed with a cork, each containing a tiny floating glowing light. She kneels, picks one up, uncorks it. A child's laugh echoes softly across the water. Her eyes fill with tears, a small smile.

[8–15s] The camera moves low along the shoreline at sand level. One by one, the bottles begin to glow brighter, warm white and gold, until the entire beach becomes a field of soft lights stretching into the distance.

[15–23s] The waves turn transparent like glass. Beneath the surface, fleeting images appear and dissolve like reflections: an old family house, a birthday cake with candles, two people talking at a ki