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홍익메디케어 공개 MCP 서버로 지역별 동물약국 목록, 동물용의약품 검색, 최근 6개월 구매 이력 기반 취급 약국을 조회한다. 조회 전용.
스킬 불러오는 중
스킬 불러오는 중
스킬 불러오는 중
SKILL DIRECTORY
설치 수와 최근 흐름을 기준으로 필요한 Agent Skill을 찾아보세요.
skills.sh API 기준방금 확인
홍익메디케어 공개 MCP 서버로 지역별 동물약국 목록, 동물용의약품 검색, 최근 6개월 구매 이력 기반 취급 약국을 조회한다. 조회 전용.
토스증권 공식 Open API(OAuth2)로 계좌, 보유주식, 시세/종목/시장정보, 주문조회를 안전한 read-only 흐름으로 조회한다. 공식 credentials가 없거나 공식 API가 지원하지 않는 기능은 비공식 경로로 우회하지 않는다.
한국 구직자의 이력서·경력요약과 희망조건을 바탕으로 잡코리아·사람인 공개 채용공고를 검색하고 적합도와 지원 전략을 정리합니다. 돌쇠에서는 공식 표면을 통한 후속 액션까지 진행한다.
Use when deciding what tests a change needs - a feature, a bug fix, a refactor, any behavior change - or when reviewing whether a diff's tests are sufficient. Encodes tests-change-with-behavior, the bug-regression pattern, and assertion discrimination. Use whenever behavior changes and the test diff is empty, especially when the change is "too small to test".
Use when a change crosses module boundaries, adds a dependency direction between modules, touches a critical path, or conflicts with a stated principle - and when writing or updating architecture principles themselves. Encodes structural invariants as named, enforced contracts: statement, rationale, guard. Use whenever \"we'll just import it from there for now\" appears, which is how boundaries die.
Use when starting any non-trivial change, investigating a bug, or working in unfamiliar code - before the first line is written. Also use for pure investigation with no change planned yet - \"dig into this\", \"figure out why\", \"sometimes the export is empty\", intermittent errors after a deploy. Encodes the ground-first discipline: map the real code and data, quote evidence, never guess conventions. Use whenever a change or a conclusion is about to be built from belief instead of from the tree, even under time pressure.
Use when writing or refactoring unit tests - a new test file, added cases, a flaky test, an unreadable one. Encodes behavior-first testing: one behavior per test, names that state the claim, deterministic setup, mocks only at boundaries you do not own. Use whenever a test is being written, even a quick one, and whenever a test needs a sleep, a mock of your own code, or a copy of the implementation's math.
Use when deciding how big a fix should be, when scope is drifting mid-task, when a plan is being quietly trimmed to fit, or when "while you're at it" appears in any form. Encodes size-to-trigger, decompose-not-descope, and drive-to-completion. Use whenever the work is about to grow past its cause or shrink below its promise.
Use when about to add, update, vet, or remove a dependency - a package, library, SDK, GitHub action, base image, or vendored code - or when a project's dependency posture needs declaring. Encodes the exhaust-what-you-have ladder, the vetting questions, and pin-and-prove updating. Use even for a tiny utility package: that is exactly how the tree grows.
Use when writing or touching any error path, catch block, fallback, default value, retry, or degraded mode - in any language, any repo. Encodes the no-silent-swallows contract and the fail-loud discipline. Use whenever an exception is about to be caught, a null is about to get a default, or a failure could pass unnoticed, even if the goal is "just make it not crash".
Use when an action touches live systems, shared state, or things that do not come back - deleting, overwriting, restarting, killing processes, editing shared config, handling secrets, or working beside concurrent sessions. Encodes the destructive-op guards, secrets hygiene, and concurrent-session safety. Use before the destructive command, not after, even when the command looks routine.
Use when about to say \"done\", \"fixed\", \"passing\", or \"shipped\", or when reporting the outcome of any change. Encodes verification as the definition of done: drive the change at its surface, run the verify command, report faithfully, distrust green suites, own failing gates. Use before every completion claim, even when the change was small and obviously correct, which is when this is skipped.
Use when doing any non-trivial engineering work - implementing, debugging, refactoring, configuring, operating, or investigating why a system misbehaves - or any change where being wrong has a cost. Encodes the evidence-over-theory discipline, the hard safety rules, and the pre-change checkpoint. Use whenever code, data, or infrastructure is about to change or must be understood before it can, even if the task looks routine or is only "find out why". Foundation for the sibling skills.
Plan, generate, source, normalize, and validate cohesive visual game assets. Use for art direction, style bibles, sprites, tilesets, backgrounds, UI art, icons, textures, concept art, or 3D asset briefs.
Use when adding data, config, state, constants, an enum-like string, a cache, or anything that could exist in two places - or when two sources already disagree. Encodes the one-fact-one-source doctrine for code and data: derive rather than store, extend the owner, absorb duplicates. Use at the moment copying a value feels faster than referencing it.
Generate images with Pruna P-Image models via inference.sh CLI. Models: P-Image, P-Image-LoRA, P-Image-Edit, P-Image-Edit-LoRA. Capabilities: text-to-image, image editing, LoRA styles, multi-image compositing, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna, p-image, pruna image, fast image generation, optimized flux, pruna ai, p image, fast ai image, economic image generation, cheap image generation
Generate talking head avatar videos with Pruna P-Video-Avatar via inference.sh CLI. Turn a portrait image into a realistic speaking video with built-in TTS. 18x faster and 6x cheaper than competitors. Models: P-Video-Avatar, P-Image (for portrait generation). Capabilities: text-to-avatar, audio-driven avatars, 30 voices, 10 languages, 720p/1080p, built-in TTS, dynamic backgrounds, full-body control. Use for: AI presenters, product demos, explainer videos, virtual influencers, marketing, education, multilingual content, UGC, gaming avatars. Triggers: avatar video, talking head, ai avatar, p-video-avatar, pruna avatar, video avatar, ai presenter, digital human, virtual presenter, lipsync, talking avatar, ai spokesperson, heygen alternative, synthesia alternative, veed alternative, fabric alternative, omnihuman alternative
Generate and edit videos with Alibaba HappyHorse 1.0 models via inference.sh CLI. Models: HappyHorse T2V, I2V, R2V, Video Edit. Capabilities: text-to-video, image-to-video, reference-to-video, video editing with natural language, character preservation, 720P/1080P, up to 15 seconds. Use for: physically realistic video, video editing, character-consistent content, product demos, social media. Triggers: happyhorse, happy horse, alibaba video, happyhorse 1.0, dashscope video, alibaba happyhorse, video editing ai, ai video editor
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Generate videos with Pruna P-Video and WAN models via inference.sh CLI. Models: P-Video, WAN-T2V, WAN-I2V. Capabilities: text-to-video, image-to-video, audio support, 720p/1080p, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna video, p-video, pruna ai video, fast video generation, optimized video, wan t2v, wan i2v, economic video generation, cheap video generation, pruna text to video, pruna image to video