Peringkat skill
Baru diperbarui
Jelajahi Baru diperbarui. Periksa sumber dan catatan tinjauan sebelum digunakan.
Ditampilkan: 30 · Kandidat: 480
Bandingkan 4 teratas- 01
short-video-scripter
Use when the user asks to "script this short video", "write a TikTok / Reels / Shorts script", "给这条抖音或视频号视频写脚本", or "fix the hook — viewers drop off in the first seconds"; produces timestamped beat-sheet scripts on the retention-gate model (0-2s hook / 2-5s confirmation / 5-15s payoff / loop-or-CTA) with per-scene script lines, on-screen text for muted viewing, asset keywords, and 9:16 format-param rows per platform norm card for TikTok, Reels, Shorts, 抖音, and 视频号 — plus 2-3 hook options from named hook families and an AI-content disclosure line by default on realistic synthetic media. Spec-only: rendering, TTS, and publishing stay with the user''s own tools — no pipelines, no upload automation. Not for creator video briefs — use brief-generator; long-form video SEO belongs to content-writer. 短视频脚本/抖音分镜/开头钩子/竖屏9:16
@aaron-he-zhuCreative2.939Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 69/100
- Kualitas
- 80/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 02
bigquery-ai-ml
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
@googleRiset21.257Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 87/100
- Kualitas
- 89/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 03
cloud-logging-query-generation
Generates Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.
@googleData Analysis21.257Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 87/100
- Kualitas
- 89/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 04
suggest-ai-ready-issues
Use when someone wants to know what to build next in a repository, asks Orbi to propose work, or runs orbi suggest. Reads the repository read-only and proposes at most three deliverable Issues, preferring an existing open Issue that already describes the change.
@orbi-buildOther217Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 47/100
- Kualitas
- 65/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 05
correct
Find the mistakes agents keep repeating in this repo and make each one impossible. Try architecture first, then types, then a lint whose error names the fix, then a test, and write docs last. Prove each check fails on a real past mistake. Repeat this each time the operator corrects you. Use for /correct.
@michael-denyerPemrograman1.937Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 66/100
- Kualitas
- 75/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 06
recall
Reconstruct your recent working context from your own chat history, live state, and the shared record (user reports, prior fixes, incidents), then hand back a tight current-state brief. Use for 'recall my work on X', 'catch me up', 'what have I been working on', 'where did I leave off', before starting or resuming work, and before telling the user that something was never recorded.
@michael-denyerOther1.937Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 66/100
- Kualitas
- 75/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 07
ketchup
Catch the user up on everything in this chat since their last message, in plain words. Give each action item an executive summary with enough context to make the call. Use for /ketchup.
@michael-denyerOther1.937Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 66/100
- Kualitas
- 69/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 08
poteto-help
Guides users through pstack setup, /poteto-mode, and picking the skill, playbook, or principle for a task. Type /poteto-help with a question.
@michael-denyerOther1.937Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 66/100
- Kualitas
- 69/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 09
brandkit
Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.
@LeonxlnxPresentasi95.602Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 90/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 10
design-taste-frontend-v1
The original v1 taste-skill, preserved for projects depending on its exact behavior. The current default is `design-taste-frontend` (v2), which is a new skill. Use this v1 install name only if you need exact backward compatibility.
@LeonxlnxDesain95.603Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 84/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 11
design-taste-frontend-v2-experimental
Old v2 (experimental) of taste-skill, the single-file dial-driven version, preserved for projects depending on its exact behavior. The current default is `design-taste-frontend` (v2). Use this install name only if you need the earlier behavior. Anti-slop frontend rules for landing pages, portfolios, and redesigns, with real design systems when applicable, audit-first redesigns and a strict pre-flight check.
@LeonxlnxDesain95.608Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 90/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 12
imagegen-frontend-mobile
Elite mobile app image-generation skill for creating premium, app-native screen concepts and flows. Designed for iOS, Android, and cross-platform mobile products. Prioritizes clean hierarchy, comfortably readable text, strong multi-screen consistency, controlled color palettes, non-generic creative direction, textured surfaces, image-led composition, tasteful custom iconography, and clean phone mockup framing. By default, screens should be shown inside a subtle premium iPhone or similar phone mockup with a visible frame, while the main focus stays on the app content itself. This skill generates images only. It does not write code.
@LeonxlnxImage Generation95.607Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 90/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 13
stitch-design-taste
Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.
@LeonxlnxDesain95.603Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 90/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 14
gpt-taste
Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing.
@LeonxlnxDesain95.602Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 90/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 15
image-to-code
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
@LeonxlnxPemrograman95.602Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 90/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 16
redesign-existing-projects
Upgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.
@LeonxlnxDesain95.608Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 84/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 17
high-end-visual-design
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.
@LeonxlnxDesain95.603Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 84/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 18
full-output-enforcement
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
@LeonxlnxAi Knowledge95.602Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 84/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 19
minimalist-ui
Clean editorial-style interfaces. Warm monochrome palette, typographic contrast, flat bento grids, muted pastels. No gradients, no heavy shadows.
@LeonxlnxDesain95.602Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 100/100
- Kualitas
- 84/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 20
desk-monitoring
How the desk watches markets and the account between trades using Grok Bot routines and WebSocket or polling watches - the desk brief, book checks, funding and price watches, alert conditions and what a watch may and may not do. Use when the user asks for briefings, alerts, "watch X" or scheduled checks.
@galleonlabsOther100Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 40/100
- Kualitas
- 61/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 21
desk-post-trade-review
The Trade Reviewer's procedure for journaling desk activity and reviewing trades from the exchange record - process graded separately from outcome, execution costs measured, one repeatable finding per review, plus the weekly desk review. Use after any send, when a trade closes, on the weekly routine, or when the user asks "how did that go".
@galleonlabsOther100Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 40/100
- Kualitas
- 61/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 22
desk-risk-limits
How the Risk Manager writes the desk's risk limits with the user, sizes every proposed trade from live account state and Hyperliquid's real constraints, checks the book, and issues a PASS or REJECT with exact ticket fields. Use for setting up or changing limits, sizing any trade, and answering "how's the book".
@galleonlabsOther100Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 40/100
- Kualitas
- 61/100
- Kebaruan
- 100/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 23
openagentskill-registry
Discover, compare, audit, and safely install reusable AI Agent Skills with OpenAgentSkill. Use whenever an agent needs a capability it does not already have, must compare Skill alternatives, or needs evidence before installing third-party instructions.
@Leon-DrqOther358Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 51/100
- Kualitas
- 67/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 24
find-skills
Find, compare, audit, and safely install reusable AI agent skills from OpenAgentSkill. Use when a user asks for a skill, plugin, reusable agent workflow, or the best tool for a task.
@Leon-DrqOther358Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 51/100
- Kualitas
- 61/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 25
code-cleanup-audit
Audit a repository for architectural decay, AI-generated code smells, stale compatibility paths, dead abstractions, boundary violations, and cleanup candidates without modifying files. Use when the user asks to review code quality, find bad code, inspect AI-generated code, identify refactoring or cleanup opportunities, detect legacy leftovers, or produce a cleanup backlog that requires human confirmation before implementation.
@pqpoPemrograman249Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 48/100
- Kualitas
- 65/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 26Lihat skill
Pembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 48/100
- Kualitas
- 59/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 27
neo4j-driver-go-skill
Covers the Neo4j Go Driver v6 — driver lifecycle, ExecuteQuery, managed and
@neo4j-contribOther115Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 41/100
- Kualitas
- 56/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 28
neo4j-modeling-skill
Design, review, and refactor Neo4j graph data models. Use when choosing node
@neo4j-contribDesain115Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 41/100
- Kualitas
- 56/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 29
neo4j-nvl-skill
Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser.
@neo4j-contribAutomation115Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 41/100
- Kualitas
- 56/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
- 30
neo4j-security-skill
Programmatic security management in Neo4j — RBAC/ABAC, user lifecycle (CREATE/ALTER/DROP USER),
@neo4j-contribSecurity115Tinjau sebelum digunakanLihat skillPembaruan repositori
Sinyal peringkat
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
- Popularitas
- 41/100
- Kualitas
- 56/100
- Kebaruan
- 99/100
- Bukti hasil agent
- 0/100
- Keyakinan bukti
- 0/100
- Kesiapan instalasi
- 90/100
Pilihan dari hingga 480 kandidat, bukan seluruh registri. Bintang milik repositori. Sinyal bukan jaminan keamanan atau verifikasi eksekusi.
Cara daftar ini diurutkan
Menurut aktivitas tercatat. Jika tanggal push tidak ada, tanggal pembaruan direktori dipakai dan ditandai.
Sinyal memakai skala 0–100. Urutan mengikuti metode daftar, bukan satu skor saja.
Data snapshot 30 hari