1.1천건 · 관련도순
Plan or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet code.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cascade approved evolution changes through downstream layers, assess impact, run consistency checks, and write evolution audit record
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design, validate, and optimize schema.org structured data for eligibility, correctness, and measurable SEO impact.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
When the user wants to design or optimize their app's monetization — pricing, paywalls, subscriptions, or in-app purchases. Also use when the user mentions "pricing", "paywall", "subscription", "IAP", "how to monetize", "revenue optimization", "free trial", or "conversion to paid". For retention impact, see retention-optimization. For competitive pricing, see competitor-analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create user-facing App Store release notes by collecting and summarizing all user-impacting changes since the last git tag (or a specified ref). Use when asked to generate a comprehensive release changelog, App Store "What's New" text, or release notes based on git history or tags.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Explain an unfamiliar repository, find where to make a change, and maintain source-cited documentation. Use for repo onboarding, wiki questions, documentation impact, or updating and sharing a WhaleWiki.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review Codex/Claude agent skills, profile manifests, and skill-related deploy changes before they ship. Use when creating, editing, importing, installing, or reviewing SKILL.md files, agent manifests, optional third-party skills, or plugin skill bundles. Checks trigger quality, progressive disclosure, evidence gates, security, hallucination risk, validation, and deployment impact.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "identify web application vulnerabilities", "explain common security flaws", "understand vulnerability categories", "learn about injection attacks", "review access control weaknesses", "analyze API security issues", "assess security misconfigurations", "understand client-side vulnerabilities", "examine mobile and IoT security flaws", or "reference the OWASP-aligned vulnerability taxonomy". Use this skill to provide comprehensive vulnerability definitions, root causes, impacts, and mitigation strategies across all major web security categories.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
You bring the third dimension to the web. You know when 3D enhances and when it's just showing off. You balance visual impact with performance. You make 3D accessible to users who've never touched a 3D app. You create moments of wonder without sacrificing usability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols. USE THIS SKILL WHEN: - You need to audit ad revenue performance or diagnose yield drops - Someone asks about header bidding configuration or Prebid.js optimization - You are analyzing fill rates, eCPM trends, or ad unit performance - A project involves programmatic advertising, SSP integration, or ad serving - You need to verify ads.txt, sellers.json, or supply chain compliance - Someone mentions CPM floors, bid density, or auction dynamics - You are evaluating ad quality controls, viewability, or ad-related UX impact - A codebase integrates Google Ad Manager, AdMob, Prebid, or any SSP TRIGGER PHRASES: "ad yield", "eCPM", "fill rate", "header bidding", "Prebid", "programmatic ads", "ad monetization", "CPM optimization", "ad revenue", "ads.txt", "ad serving", "SSP", "ad unit performance", "viewability"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use after the Lean model changed and before touching backend code — regenerates Kotlin from Lean, lists changed generated symbols, the hand-written files and contract tests that reference them, and golden regressions, so the blast radius is known before implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you need the current position in the Lean spec-driven pipeline, or any deterministic check of a Cradle project — status, golden regression, Lean layer walls, regeneration impact, unslop lint, ddd cleanup. Runs the scripts under scripts/ and reports their output verbatim.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Triage PostHog error tracking issues during a daily or on-call review. Use when the user asks "what's broken?", "what new errors do we have?", "show me top errors today", "what should I look at this morning", or wants a prioritized list of active issues to work on. Surfaces new and high-impact issues, ranks by users affected and recency, points at linked replays, and proposes next actions (investigate, assign, suppress, merge).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Increase visual impact, contrast, personality, and compositional confidence when a design feels bland, generic, or too safe. Use when the design needs more energy, confidence, or memorability—not when the real problem is clutter, over-intensity, or micro-detail polish.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add moments of joy, personality, and surprise through success states, empty states, loading moments, hover details, and celebratory polish once the fundamentals already work. Use when the user wants delight, charm, personality, or memorable micro-interactions—not when the main need is baseline cleanup or stronger visual impact.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze blast radius before making code changes
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous rule adherence checker. Scans the codebase for rule violations, fixes the highest-impact ones in an isolated worktree, runs full validation, creates a PR. Uses memory to track progress across runs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review only changes since last commit using impact analysis. Token-efficient delta review with automatic blast-radius detection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Select traceable regression coverage from change impact, affected risks, criteria, dependencies, and prior defects rather than execution convenience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.