uzysjung
UserCurate vetted AI-coding skills & plugins by your tech stack — install only what you need, across Claude Code, Codex, OpenCode & Antigravity
Categories
Indexed Skills (36)
architecture-decision-record
Use this skill when documenting significant architectural decisions. Provides ADR templates following the Nygard format with sections for context, decision, consequences, and alternatives. Use when writing ADRs, recording decisions, or evaluating options.
agent-introspection-debugging
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
continuous-learning-v2
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
eval-harness
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
strategic-compact
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
e2e-testing
Playwright E2E testing patterns, Page Object Model, configuration, CI/CD integration, artifact management, and flaky test strategies.
investor-materials
Create and update pitch decks, one-pagers, investor memos, accelerator applications, financial models, and fundraising materials. Use when the user needs investor-facing documents, projections, use-of-funds tables, milestone plans, or materials that must stay internally consistent across multiple fundraising assets.
investor-outreach
Draft cold emails, warm intro blurbs, follow-ups, update emails, and investor communications for fundraising. Use when the user wants outreach to angels, VCs, strategic investors, or accelerators and needs concise, personalized, investor-facing messaging.
python-patterns
Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.
python-testing
Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.
deep-research
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
verification-loop
A comprehensive verification system. Selects and runs proportional verification tracks for UI, API/service, CLI/TUI, library/SDK, documents/configuration, and real user flows, then ends every run with a fixed verdict — PASS / PASS_WITH_NITS / FAIL — plus severity-labeled findings (CRITICAL/HIGH/MEDIUM/LOW) and the evidence each one rests on. Use after implementation, before a PR or handoff, after a refactor, or to verify a claimed fix. Do NOT use a green build, a passing type check, or file existence as proof of user-visible completion, and do NOT let the instance that wrote the change issue its own verdict.
market-research
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.
nextjs-turbopack
Next.js 16+ and Turbopack — incremental bundling, FS caching, dev speed, and when to use Turbopack vs webpack.
asis-tobe-decision
Present a decision or confirmation request in the user's four-part format: 전후맥락 (context) → 추천 + 이유 (recommendation) → UI/UX 형태 (a scannable table/option-list) → ASIS→TOBE contrast, led by the recommendation so the user can say yes fast. Fire at genuine A-or-B / approval / explicit-ASIS-TOBE moments. Triggers on the user's verbatim phrases "ASIS TOBE로 설명", "ASIS-TOBE로 알려줘", "화면으로 ASIS TOBE로 설명", "의사결정 / 컨펌 요청", "이거 진행할까요?", and the softer "다음 진행할 것들 알려줘"; English equivalents: "present this as ASIS/TOBE", "give me the as-is to-be", "should I do A or B", "ask for my approval", "lay out the options". Do NOT fire for pure information with no decision, or trivial reversible actions you would just do.
compaction-handoff
Create a compact, reconstructible checkpoint immediately before context compaction. Persist only durable facts and decisions, overwrite one current resume anchor, verify Git and open-PR state, and emit one concrete next action plus a short /compact line. Use for "컴팩션 준비해줘", "컴팩션하고 이어서 진행할 수 있게 준비해줘", "핸드오프 준비해줘", or "prepare for compaction", or equivalent. Repeated execution must be idempotent and must not grow state files without bound.
gap-analysis-e2e
Two-mode chained gap analysis. DETECT scans the current service end-to-end through three lenses — north-star alignment, correctness (bugs), and user-perspective (UX) — and enumerates concrete, severity-ranked gaps. Then BENCHMARK researches how reference/benchmark services actually solved each high-ranked gap and PROPOSES a closing approach. Use when the user says any of: "북극성 기준으로 부족한 점", "사용자 관점에서 부족한 점", "다른 벤치마크 서비스는 이 부분을 어떻게 해결했는지", "갭분석", "레퍼런스 서비스랑 비교해서 부족한 점 찾아줘" — or the English equivalents: "gap analysis", "what are we missing vs the ideal/north-star", "benchmark against reference services". Fires for both Korean and English phrasing. NOT for a whole-codebase multi-dimension audit (use ultracode-service-audit) or single-artifact prose review (use multi-persona-review) — this is the narrower gap-vs-benchmark loop.
harness-health-audit
Audit the health of an AI-coding harness — the CLAUDE.md / AGENTS.md files, rules, skills, agents, hooks, and commands that steer an agent in a repository — across four questions a linter cannot answer: is it TRUE (does it match the real code?), is it USED (do skills actually trigger and does the loop actually verify?), is it AFFORDABLE (is it inside the budget where instructions are still followed?), and is it SAFE (is a live, accurate instruction still a good idea — permission bypasses, unpinned remote scripts, untrusted content flowing in as instructions? — safety findings are flagged for the user's decision, never auto-removed). Then surgically correct or remove only what is proven wrong or dead. Use whenever the steering layer may have rotted or may not be working: "하네스 점검해줘", "하네스 드리프트 감사", "CLAUDE.md가 실제랑 맞는지 봐줘", "룰/스킬이 최신인지 확인해줘", "스킬이 제대로 활용되는지 봐줘", "루프 엔지니어링 잘 되고 있는지 검토해줘", "죽은 훅/커맨드 정리해줘", "하네스 안전한지 점검해줘", "audit my harness", "check the rules still match the real stack", "are my skills actually be
northstar-roadmap
Read the project's NORTH_STAR / vision doc, measure current state against the goal, then propose a forward direction plus prioritized feature proposals — persisted as a durable roadmap in docs/plans + memory so the plan survives /compact and new sessions. Use when the user asks where the project should go next or wants a backlog grounded in the vision. Fires on the user's real phrasings: "앞으로 어떤 방향으로 개선·발전시킬지 고민해봐", "NORTH.md / NORTH_STAR 보고 나아갈 방향 + 기능 제안", "나아갈 방향 + 기능제안 (수용 → 계획 수립하고 메모리에 기록)", "북극성 정렬 로드맵", as well as the English equivalents: "what direction should we take next", "propose a roadmap / feature backlog from the north star", "plan the next milestones and save it to memory". Not for detecting bugs or auditing current quality (see gap-analysis-e2e / ultracode-service-audit) — this skill DIRECTS forward planning.
ultracode-service-audit
Run a multi-agent, adversarially-verified full-service audit across 7 dimensions (code / UX / scalability / planning+north-star / security / promotion / extensible), separating findings into confirmed / unverified / rejected and producing a priority-ranked, M-numbered milestone roadmap (as many milestones as the findings warrant). Use when the user says "ultracode 전체 서비스 점검", "전체 서비스를 점검하자", "코드·UX·확장성·기획·북극성지표·보안·홍보 문제점을 파악하고 우선순위에 따라 개선", "다차원 서비스 감사", or in English "audit the whole service / full multi-dimensional service audit / find code, UX, scalability, planning, security, and marketing problems and prioritize fixes". The heavyweight superset audit — orchestrate it as a Workflow with fan-out finders and an adversarial verify pass. NOT for a single-artifact prose/README review (use multi-persona-review) or a single-axis gap-vs-benchmark loop (use gap-analysis-e2e) — those are the lighter siblings.
codex-consult
Consult OpenAI Codex (via the local `codex` CLI, non-interactive `codex exec`) for the two things it is comparatively strong at: (1) CONCISE, well-STRUCTURED writing — tightening verbose prose, restructuring a doc into a clean outline / tables / sections, executive summaries, README skeletons, changelog entries — and (2) IMAGE GENERATION — hero/placeholder art, logos, simple illustrative art, produced as real PNG files on disk (labeled flowcharts / architecture / sequence diagrams are NOT this — render those natively as Mermaid). Use whenever a document needs to get SHORTER and better ORGANIZED (not prettier-sounding), or whenever the user wants a generated image. Triggers: "codex한테 물어봐 / codex로 정리해 / 간결하게 정리해줘 / 구조화해줘 / 문서 구조 잡아줘 / 이미지 만들어줘 / 그림 생성해줘", or in English "ask codex", "tighten this up", "make this concise", "restructure this doc", "generate an image". Korean NUANCE/copy polish belongs to the sibling skill gemini-consult — this skill owns structure, concision, and images. Returns candidates/files f
explain-plainly
Explain a technical finding to someone who has not read the code: fix the referent first (one name often points at two things), lead with who is affected and what changes, then show evidence. Run it whenever you explain a bug, a cause, or what your change did — especially the moment the reader says they don't follow ("뭔 소리야", "쉽게 설명해줘", "이해가 안 돼", "I don't follow", "in plain terms"), or when your draft opens with a file path or symbol name.
gemini-consult
Consult Google Gemini (via the local Antigravity `agy` CLI, Pro tier) for three things: (1) natural, native-sounding KOREAN phrasing — copy, UI microcopy, marketing/brochure text, toasts, user-facing messages, translations, rewrites — (2) a MULTI-PERSONA / second-opinion review of a design, plan, spec, PR, or piece of writing, and (3) IMAGE GENERATION via Gemini's image tool (real PNG/JPG files, collected from agy's artifact store). Use this whenever Korean text needs to read naturally (not translated/stiff), whenever the user says the Korean "sounds awkward / 어색해 / 자연스럽게 다듬어줘", whenever you are about to hand-write polished Korean copy yourself, whenever you want an independent NON-Claude model's critique, or when the user asks for a Gemini/Nano-Banana-style generated image. Claude's Korean often reads machine-translated — delegating Korean polish here is this skill's premise (the installing user's standing preference, not a benchmark). Returns candidates/files for the user to choose from. Also triggers on "g
model-orchestration
Apply the fixed model-role and thinking-effort policy whenever work is delegated to subagents or a model/effort choice is made: the orchestrator (top-tier model, Fable) DIRECTLY owns 설계· 기획·분배·리뷰 — sets service direction, arbitrates and reviews plan/spec documents (with multi-persona-review), improves shipped features, and hunts performance/security problems; core implementation, test authoring/execution (E2E included), and code verification/V&V go to Opus at xhigh or above; repetitive/simple implementation (applying an established pattern) goes to Sonnet at high or above — Sonnet is never used for tests or verification; plan/spec drafts may be produced by Opus from a Fable direction brief, but the review/decision is always Fable's. Never delegate below the effort floors. Use whenever you are about to spawn an Agent/Task/Workflow worker, pick a model for a subtask, set a thinking/effort level, assign verification, or hand off orchestration because the current model's quota is exhausted. Trigger on "위임해", "에이전
multi-persona-review
A panel-review skill that critiques ONE artifact (launch post, README, doc, markdown, plan, design) via 3-5 disjoint user-perspective personas running in parallel, then synthesizes deduped, severity-ranked improvement points (P0/P1/P2). Use when the user says "작성글을 사용자 관점의 페르소나를 여러명 만들어서 (손넷 모델정도로) 피드백 받아바", "다면 리뷰 해볼까", "페르소나로 리뷰", "여러 관점으로 피드백", or in English "multi-persona review", "review this from different user perspectives", "get persona feedback on this post/README/doc", "panel review this artifact". Lighter than a full service audit — point it at ONE artifact, not a whole codebase. Do NOT use it for a whole-service or whole-codebase audit, nor for a gap-vs-benchmark loop (both are audit-service-gaps), and do NOT simulate diversity by renaming reviewers that inspect the same evidence.
north-star
Defines and enforces a project's long-term direction (North Star Statement, metric-as-proxy NSM, strategic Pillars with a module↔pillar map, Will/Won't, 4-gate + priority-order decision heuristics), then turns that direction into a ranked, persisted roadmap so the plan survives /compact and new sessions. Use when starting a new project, when scope creep is suspected, when a non-obvious feature request needs prioritization, or when the user asks where the project should go next. Sits one layer above SPEC/PRD — answers 'why and where to', not 'what and how'. Fires on the user's real phrasings: "앞으로 어떤 방향으로 개선·발전시킬지 고민해봐", "NORTH.md / NORTH_STAR 보고 나아갈 방향 + 기능 제안", "나아갈 방향 + 기능제안 (수용 → 계획 수립하고 메모리에 기록)", "북극성 정렬 로드맵", as well as the English equivalents "what direction should we take next", "propose a roadmap / feature backlog from the north star", "plan the next milestones and save it to memory". Do NOT use it to find what is broken right now — detecting bugs, gaps, or quality regressions belongs to the audit/ga
recurrence-prevention
When the same defect, mistake, or incident happens AGAIN — a recurrence, not a one-off — verify it against prior evidence (memory, rule case tables, git/CHANGELOG history), classify it as a simple slip vs a complex harness problem, then escalate the countermeasure one level up the ladder: record (1st) → forced rule with a case table (2nd) → structural gate — test, hook, or derive — once prose has failed (3rd+). Complex problems get countermeasure candidates designed by a multi-persona panel instead of a quick patch. Use for "재발했어", "같은 실수 또 했네", "이거 저번에도 그랬잖아", "재발방지 대책 등록해줘", "재발방지 룰 만들어", "this happened again", "same bug as last time", "add a recurrence countermeasure", "postmortem this failure". Do NOT use it for a first-time defect (fix it, record it, stop), do NOT create a standing rule from an unverified first occurrence, and do NOT use it as a general audit of the steering layer at rest.
gh-issue-workflow
Runs work through GitHub Issues so the user and the agent can see the same backlog: normalize an incoming request into an issue before building, group tasks under a parent (epic) issue, order them with milestones and priority labels, read every comment before starting, and promote decisions out of comments into the issue body so they survive. Enforces the body template (배경/문제/근거/레퍼런스/제안/전제/방향성/AC/후속) so issues become reusable agent context, and keeps read-only, draft, remote-write, implement, verify, and status stages distinct. Use whenever a request will outlive the chat turn, whenever work needs an order the user can review, or when the user names issues ("이슈로 등록해줘", "에픽으로 묶어줘", "이슈 정리해줘", "#42 작업해줘", "우선순위 다시 잡자", "backlog this", "break this into sub-issues", "implement issue #N"). Read-only stages make no remote change; never create, edit, label, comment on, close, or re-parent a remote issue, and never touch a project board, without the user asking for that change.
ui-visual-review
Captures screenshots of key UI flows after E2E tests pass, runs an agent-side first-pass diff (regressions, console errors, layout shifts), then surfaces a checklist for the user's final approval. Also owns the browser-launch procedure the `playwright-launch` rule delegates here: use it whenever a browser must be opened for a human to drive or for automated capture — manual E2E checks, UX/fidelity comparison against a reference product, or a one-time OAuth login. Use after E2E tests pass on a UI track (csr-*, ssr-*, full).
clear-korean-communication
Make a technical explanation land, and turn the decision at the end of it into something the reader can approve in one pass. Two halves of one job: (1) EXPLAIN — fix the referent first (one name often points at two things), lead with who is affected and what changes, put file paths and symbols after the claim as evidence; (2) DECIDE — present approval/choice moments in the user's four-part format 전후맥락 (context) → 추천 + 이유 (recommendation) → UI/UX 형태 (a scannable table/option-list) → ASIS→TOBE contrast, led by the recommendation so the user can say yes fast. Run it whenever you explain a bug, a cause, or what your change did — especially the moment the reader says they don't follow ("뭔 소리야", "쉽게 설명해줘", "이해가 안 돼", "I don't follow", "in plain terms"), or when your draft opens with a file path or symbol name — and whenever you are about to ask "should I do this?". Triggers on the user's verbatim phrases "ASIS TOBE로 설명", "ASIS-TOBE로 알려줘", "화면으로 ASIS TOBE로 설명", "의사결정 / 컨펌 요청", "이거 진행할까요?", the softer "다음 진행할 것들 알려줘"
external-model-consult
Consult a second, non-Claude model through a bundled wrapper for the four things an external round-trip actually buys: (1) natural, native-sounding KOREAN phrasing via Google Gemini (Antigravity `agy` CLI) — copy, UI microcopy, marketing/brochure text, toasts, user-facing messages, translations, rewrites; (2) a MULTI-PERSONA / second-opinion review of a design, plan, spec, PR, or piece of writing; (3) CONCISE, well-STRUCTURED writing via OpenAI Codex (`codex exec`) — tightening verbose prose, restructuring a doc into a clean outline / tables / sections, executive summaries, README skeletons, changelog entries; and (4) IMAGE GENERATION as real PNG/JPG files on disk (labeled flowcharts / architecture / sequence diagrams are NOT this — render those natively as Mermaid). Use whenever Korean text needs to read naturally rather than translated, whenever the user says the Korean "sounds awkward / 어색해 / 자연스럽게 다듬어줘", whenever you are about to hand-write polished Korean copy yourself, whenever a document needs to get S
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
task-brief
Rewrite a task request into the canonical XML brief — objective, inputs, invariants, success_criteria, boundaries, autonomy, verification, communication, output_format — so the worker receives one judgeable definition of done instead of prose. Runs in two directions: INBOUND reshapes a sprawling or half-formed request (pasted requirements, a wall of background, a request that grew across several messages) into that shape, filling each field from context already on screen and deleting sections that do not apply; OUTBOUND writes the prompt that a spawned worker actually receives, so nobody hand-rolls a one-off prompt shape per delegation. Trigger on "브리프로 정리", "작업 지시서로 만들어", "프롬프트 구조화", "브리프 만들어줘", "이 요청 정리해줘", and in English "turn this into a task brief", "structure this prompt", "write the brief for this", "draft the spawn prompt". Fire unprompted the moment you are about to hand a multi-part task to a subagent, a workflow worker, or a parallel lane. Do NOT fire on a one-line question, a lookup, or an ordinar
humanize-korean
한국어 글에서 실제로 어색한 문체 문제를 진단하고, 근거가 있는 부분만 최소 수정해 자연스럽게 다듬는다. 번역투, AI 상투구, 기계적 구조, 접속사·형식명사·완곡 표현의 반복, 불필요한 영어 장식 등을 다루며 의미·사실·수치·전문용어·말투는 보존한다. "AI 티 없애줘", "자연스럽게 다듬어줘", "번역투 고쳐줘", "사람이 쓴 글처럼", "문체 진단 후 고쳐줘" 같은 요청에 사용한다.
self-hosted-github-runner
GitHub 호스티드 러너를 못 쓰게 됐을 때, 레포의 기존 워크플로를 **그대로** 자기 머신의 Docker self-hosted runner 에서 돌리게 만든다. 결제 실패·지출한도·분(minutes) 소진·조직 쿼터·Actions 장애로 CI 가 멈췄을 때, "CI 를 로컬에서 돌리자"·"self-hosted runner"·"act 로 돌릴까"·"CI 죽었는데 머지·배포 어떻게 하냐"는 말이 나올 때 쓴다. **누군가 "CI 스텝을 스크립트에 옮겨 적어 로컬에서 돌리자"고 제안할 때도 반드시 이 스킬을 켠다** — 그 안은 워크플로를 두 벌로 갈라놓고, 갈라진 순간부터 로컬 초록이 CI 초록을 뜻하지 않게 되기 때문이다.
spec-scaling
Detects when SPEC.md or PRD.md exceeds 300 lines and proposes feature-based splitting with a master route document. Use when SPEC.md grows too large to be effectively used as a single document.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.