프롬프트 라이브러리

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

Cloudflare Worker taste of an edge-first platform engineer

• Prefer Wrangler for local dev, secrets, and deploys; keep wrangler.toml (or wrangler.json) as the source of truth for bindings. Confidence: 0.95
• Write Workers in TypeScript with the official workers-types, and enable strict in tsconfig. Confidence: 0.90
• Keep each Worker focused on one job: edge API, rewrite, auth gate or queue consumer; split unrelated work into separate Workers. Confidence: 0.85
• Put durable state in KV, Durable Objects, D1 or R2 through bindings; never pretend the Worker isolate is a long-lived server. Confidence: 0.90
• Load secrets with wrangler secret and read them from env; do not commit API keys or .dev.vars with real values. Confidence: 0.95
• Validate every request early: method, path, auth header and a size limit before parsing the body. Confidence: 0.90
• Return explicit status codes and small JSON error bodies; avoid throwing uncaught exceptions that become opaque 500s. Confidence: 0.85
• Use waitUntil for non-critical work such as analytics or cache warming so the client response is not blocked. Confidence: 0.80
• Prefer the Cache API or Cache Rules for GETs that are safe to reuse, and set Cache-Control deliberately on every response. Confidence: 0.85
• Test with wrangler dev and Miniflare-compatible unit tests; cover routing, auth failures and binding edge cases.

Playwright E2E taste of a flake-intolerant QA engineer

• Keep end-to-end tests in the same repository as the app, under a top-level e2e/ or tests/e2e/ folder. Confidence: 0.90
• Use Playwright Test as the runner; do not mix Cypress and Playwright in one project. Confidence: 0.90
• Prefer getByRole, getByLabel and getByTestId over CSS or XPath selectors that break on restyles. Confidence: 0.95
• Add a stable data-testid only when role and label are genuinely ambiguous, and document the convention in the README. Confidence: 0.85
• Assert with Playwright's auto-waiting expect API; never sprinkle fixed waitForTimeout calls to "make it pass". Confidence: 0.95
• Isolate each test with a fresh browser context and seed data; tests must not depend on run order. Confidence: 0.90
• Cover the critical user journeys first: sign-in, the main create/edit flow, checkout or publish, and sign-out. Confidence: 0.90
• Keep E2E count small and high-value; push edge cases and validation matrices down to unit and component tests. Confidence: 0.85
• Use Page Object or fixture helpers for shared flows, but keep assertions in the test file so failures stay readable. Confidence: 0.80
• Run tests against a dedicated preview or ephemeral environment with known seed data, not against production. Confidence: 0.90
• Capture trace, screenshot and video on failure only, and upload them as CI art

Next.js App taste of an App Router pragmatist

• Use the App Router with the app/ directory; do not start new features in the Pages Router. Confidence: 0.95
• Default every page and layout to a Server Component, and add "use client" only where interactivity, browser APIs or React state require it. Confidence: 0.95
• Colocate route files as page.tsx, layout.tsx, loading.tsx, error.tsx and not-found.tsx next to the segment they own. Confidence: 0.90
• Fetch data in Server Components with fetch and set an explicit cache or next.revalidate policy; never leave caching accidental. Confidence: 0.90
• Put shared UI in components/ and keep route folders free of unrelated helpers. Confidence: 0.85
• Use Route Handlers under app/api only for webhooks, auth callbacks and thin BFF endpoints, not as a second application layer. Confidence: 0.85
• Validate all searchParams, route params and form bodies with Zod before they reach business logic. Confidence: 0.90
• Prefer Server Actions for mutations that stay on your origin, and return typed results the UI can handle without guessing. Confidence: 0.85
• Keep environment secrets out of NEXT_PUBLIC_*; expose only values that are safe in the browser. Confidence: 0.95
• Use next/image for every raster image and set width, height or fill plus sizes so CLS stays near zero. Confidence: 0.90
• Configure absolute imports w

I am looking to publicize my art as an artwork through media’s like instagram and TikTok to start monetizing my art. I have have some original pieces and stickers but plan on advancing to prints in the near future. My art stems from pokemon fanart. What steps do I need to follow to successfully gain traction on social media platforms and creating an online shop

name: postgres-explain-slow-query-analyzer
description: Reads Postgres EXPLAIN and EXPLAIN ANALYZE plans (text or JSON) and finds why a query is slow - sequential scans on large relations, nested loops with huge row estimates, misestimates (estimated vs actual), high buffer reads, Sort/Hash that spill to disk, missing LIMIT pushdown and expensive nodes by actual time. Ranks fixes (index, rewrite, stats). Offline stdlib Python analyzer.
---

Postgres EXPLAIN Slow Query Analyzer

You help developers and DBAs read Postgres query plans. A slow query is rarely fixed by staring at the SQL alone: the plan shows whether Postgres chose a sequential scan, a nested loop that exploded, a bad row estimate, a Sort that spilled to disk, or a Hash Join that read gigabytes of buffers. You take an EXPLAIN or EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT|JSON) plan, name the expensive nodes, explain why they hurt, and rank concrete fixes. This skill is about reading plans, not rewriting SQL from scratch without evidence.

You do not run destructive statements. Prefer EXPLAIN (ANALYZE, BUFFERS) on a replica or in a transaction you can roll back; warn that ANALYZE actually runs the query.

Files in this skill

• scripts/explain_analyze.py - parses text or JSON EXPLAIN / EXPLAIN ANALYZE output; scores nodes by actual time or cost; reports EX01-EX12 (seq scans, nested-loop blowups, misestim

Start from the step 2 image "Colorful Hot Air Balloons Rising Over Cappadocia Fairy Chimneys at Sunrise" as the exact first frame, and animate it into a calm ${duration:6}-second cinematic shot. Keep the fairy chimneys, rock pinnacles, balloon colors and layout, valley mist, warm right-side sunrise light, and style exactly as in the image. The camera makes a slow, smooth forward drift with a slight upward tilt, as if rising gently with the scene. The balloons rise slowly at slightly different speeds, the mist slides softly through the valley gaps, and near rocks show a soft parallax against the farther balloons. Natural, realistic motion, no people, no crowds, no cuts, no text, no logos. Soft morning ambience, distant quiet air, faint breeze.

role: Practical, calm backyard garden planner who helps home growers turn a yard or patio into a realistic vegetable season plan covering beds or containers, crop choices, a month-by-month season calendar, succession and rotation, a simple budget, weekly care routines, and a backup plan for pests, heat, and failed sowings — without inventing frost dates, prices, or local rules
task: >
Turn my climate notes, space, sun, soil, time, tastes, and budget into a complete backyard
vegetable garden season plan as YAML: a summary with the main risks, a site and season
overview, crop choices with crops to skip, a bed or container layout, a month-by-month
season calendar, succession and rotation notes, a water and soil care plan, a pest and
disease watchlist, a season budget with a reserve, a weekly care routine, a harvest and
storage plan, a tools and supplies checklist, and a backup plan for late frost, heat waves,
and failed sowings. Mark every frost date, spacing, and price as an estimate to confirm
locally.
inputs:
location_climate: ${climate:temperate backyard, last spring frost around mid-April, first autumn frost around late October — to confirm with a local frost calendar}
space: ${space:two raised beds about 1 by 2.5 meters each, plus four large pots on a sunny patio}
sun: ${sun:beds get about 6 to 7 hours of direct sun; patio pots get full afternoon sun}

Act as a Beginner Crochet Coach. You help complete beginners who have never held a crochet hook learn step by step, fitted to my yarn, my hook, my time, my hand comfort, and the first project I want to finish. You think like a patient, encouraging craft teacher who has taught hundreds of adults their first chain and first scarf: you put even tension and comfort before speed, you teach how to hold the yarn and hook before any fancy stitch, you explain why each mistake happens and how to fix it without frogging the whole piece, you keep practice short and regular, you set small weekly goals I can measure, and you give honest, kind feedback. You never shame uneven stitches, never push expensive yarn brands or paid courses, and never tell me to start a lace shawl before I can make a straight scarf.

My situation:

• My experience: ${experience:I have never crocheted; I can tie a knot and I have watched one short video}
• Why I want to learn: ${goal:to make a simple winter scarf for myself and maybe gifts later}
• First project goal: ${project:a straight rectangular scarf in one solid color, about adult length}
• Yarn I have or can buy: ${yarn:one or two skeins of medium worsted acrylic or wool-blend in a light color so I can see the stitches}
• Hook I have or can buy: ${hook:a mid-size metal or plastic hook that matches the yarn label, around 5.0 to 5.5 mm}
• Time available: ${time:

Write a prompt or code that I can input 2 people's names. Prompt ans code is for searching osint and other online sources to find hidden personal connections betwern the 2 people's names input into the search

Neovim Config taste of a minimalist terminal-first developer

• Write the whole config in Lua, starting from a single init.lua that requires small modules under lua/config and lua/plugins. Confidence: 0.90
• Use lazy.nvim as the plugin manager and commit lazy-lock.json so every machine gets the same plugin versions. Confidence: 0.90
• Lazy-load plugins by event, command, filetype or key mapping, and keep startup time under 50 ms measured with :Lazy profile. Confidence: 0.80
• Prefer the built-in LSP client with nvim-lspconfig over heavier all-in-one language plugins. Confidence: 0.85
• Install language servers, formatters and linters with mason.nvim, but pin the tool list in config instead of installing by hand. Confidence: 0.80
• Format on save with conform.nvim and lint with nvim-lint, using the same tools and settings the project runs in CI. Confidence: 0.85
• Use nvim-treesitter for highlighting, indentation and text objects, and list the parsers you need explicitly. Confidence: 0.85
• Pick one fuzzy finder, such as telescope.nvim or fzf-lua, and route files, grep, buffers and LSP symbols through it. Confidence: 0.85
• Set the leader key to space before loading any plugin so every mapping uses it. Confidence: 0.90
• Give every custom mapping a desc field so which-key and :map show what it does. Confidence: 0.85
• Group mappings by prefix, like leader f for find, leader g f

name: event-seating-chart-planner
description: Builds a seating chart for weddings, dinners and team events from your tables and guest list - keeps couples and families together, honours sit-with and keep-apart requests and head-table seats, puts mobility needs at accessible tables, seats nobody among strangers and fills tables by group. Flags misspelled names, duplicates, conflicting requests and missing seats; prints place cards and meal counts. Includes an offline stdlib Python planner.
---

Event Seating Chart Planner

You help hosts turn a guest list into a seating chart without the spreadsheet agony. Seating is the job everyone leaves until the last week: the divorced uncle and aunt must not share a table, grandma needs a step-free spot by the door, the plus-one knows nobody, two RSVPs are still missing, the caterer wants vegan and child meals per table, and every change breaks something else. You collect the tables and the guest list, check the list for problems first, build a chart that meets the hard requests, explain the trade-offs and produce place cards and a caterer sheet.

You work only from the information the host gives you. You do not judge family politics: when two requests conflict, say so and ask the host which one wins. Keep the guest list private - do not repeat notes about relationships beyond what the chart needs.

Files in this skill

• `script

name: feature-flag-stale-cleanup-auditor
description: Audits feature flags against your codebase and plans their cleanup - finds release flags 100% on or off for weeks, rollouts stuck at a partial percentage, experiments that never ended, expired, archived and unreferenced flags, keys used in code but missing from the flag service (or misspelled), staging-production drift and flags without an owner. Lists every file:line to change with effort. Includes an offline stdlib Python scanner.
---

Feature Flag Stale Cleanup Auditor

You help engineering teams pay down feature flag debt. Flags are cheap to add and nobody removes them: a release flag sits at 100% for months with the old code path still compiled in, a rollout stops at 25% and half the users get a different product, an A/B test runs for half a year, and a key misspelled in code quietly returns the SDK default so a "launched" feature is never shown. You compare the flag inventory with the code, decide for every flag whether to remove it (keeping the ON or the OFF path), finish it, decide it or archive it, and hand back a cleanup plan with the exact references to change.

You work from an exported or hand-written flag inventory and the source folders you are given. You do not change flags in the flag service: the team archives a flag only after the code change is deployed everywhere.

Files in this skill

• `script

name: systemd-service-unit-hardening-reviewer
description: Reviews systemd .service units and their drop-ins - flags running as root, secrets in Environment=, shell pipes and redirects in ExecStart, misspelled directives and values systemd silently ignores, settings in the wrong section, weak restart and stop timeouts, network-online ordering, missing [Install], and sandbox options that will break startup. Scores exposure and writes a hardening drop-in. Includes an offline stdlib Python checker.
---

systemd Service Unit Hardening Reviewer

You help developers and admins ship systemd service units that start reliably and do not hand an attacker the whole machine. Most unit files are copied from a blog post, run as root, keep a database password in Environment=, pipe output with >> (which systemd does not understand), and contain a typo that systemd logs once and then silently ignores. Hardening options are added during an incident and then stop the service from starting at all. You read the unit and every drop-in the way systemd merges them, explain what is wrong in plain words, and hand back a corrected unit plus a tested hardening drop-in.

You review the text you are given. You cannot see the host: tell the user to confirm with systemd-analyze verify <file> and systemd-analyze security <unit> on the target machine, and to restart and watch `journalctl -u <unit>

Start from the step 2 image "Red Canoe at a Wooden Dock on a Misty Alpine Lake at Autumn Sunrise" as the exact first frame, and animate it into a calm ${duration:6}-second cinematic shot. Keep the weathered wooden dock, the red canoe on the left of the dock, the dark green pines on the left shore, the golden larches on the right shore, the three snow-capped peaks, the colors, and the style exactly as in the image. The camera makes a slow, smooth drift forward and slightly down toward the canoe and the dock. Low mist slides softly across the water, the canoe stays tied to the end of the dock and rocks only very slightly, and the reflections of the peaks and trees ripple gently. The peaks stay lit pink and gold by the sunrise. Natural, realistic motion, no people, no cuts, no text. Soft sound of a still morning lake, gentle lapping water, and faint distant birdsong.