3.5천건 · 관련도순
Write measurable acceptance criteria for requirements or user stories. Use when the user says "write acceptance criteria", "definition of done", "how do we test this", "fit criteria", "given when then", "Gherkin scenarios", "what does done look like", "testable conditions", "how will we know this works" - even if they don't explicitly say "acceptance criteria".
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Use this skill when authoring INVEST-compliant Salesforce user stories from gathered requirements: shaping the As-A/I-Want/So-That stem, writing Given-When-Then acceptance criteria, sizing complexity (S/M/L/XL), splitting stories that are too large for a sprint, and emitting handoff metadata that names the next downstream agent. Trigger keywords: user story, INVEST, story splitting, story sizing, As-A I-Want So-That, story handoff, recommended_agents, backlog item shape. NOT for requirements elicitation or stakeholder interviews (use admin/requirements-gathering-for-sf). NOT for the Given-When-Then technique itself in depth (use admin/acceptance-criteria-given-when-then). NOT for UAT test case design (use admin/uat-test-case-design). NOT for backlog prioritization (use admin/moscow-prioritization-for-sf-backlog).
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Diagnose monogenic and syndromic obesity in children and adolescents using a structured step-by-step algorithm. Use this skill whenever a clinician suspects a genetic cause of obesity, asks about leptin deficiency, MC4R mutation, POMC deficiency, PCSK1 deficiency, leptin receptor deficiency, Bardet-Biedl syndrome, Prader-Willi syndrome, Alström syndrome, or any case of early-onset severe obesity with hyperphagia. Also trigger for questions about targeted pharmacotherapy including setmelanotide or metreleptin, or when to order a genomic obesity panel. Cross-references the NHS Genomic Test Finder skill to surface the relevant R-code once a diagnosis is reached.
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Use this skill when a company needs a written risk assessment method before it builds or rescores a register, or when the current method produces registers where everything scores 3. Triggers: "write our risk methodology", "define likelihood and impact scales", "what is our risk appetite", "risk acceptance criteria", "who can accept a risk", "our auditor says the method is not repeatable", "risk matrix for our ISMS". Drafts a short method with likelihood and impact anchors in the company's own money, hours and records, a scoring rule, an acceptance threshold with an authority ladder, reassessment triggers, and a calibration test on five sample risks.
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Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.
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Knowledge base from the Expanded Guidance for NASA Systems Engineering (NASA/SP-2016-6105-SUPPL, Volume 1, March 2016) — the practitioner-depth supplement to the NASA SE Handbook. Use for the load-bearing mechanics the handbook compresses: the SE Engine's per-phase cadence and the iterative-vs-recursive distinction, the AS9100 crosswalk, life-cycle tailoring vs. customization and the Compliance Matrix, the recursive system-design consistency loop (ConOps vs. Operations Concept, requirement flow/type/ownership, successive refinement), product realization (verify/qualify/accept/certify cardinality, protoflight, 'test the way we fly' coverage, two-form transition), and the crosscutting technical-management procedures (six-step scheduling, cost-estimating method selection, JCL, interface document family, RIDM/CRM, CM baselines, EVM arithmetic, MOE/MOP/KPP/TPM, the three required leading indicators, and the decision-analysis method catalog). This is the DEPTH layer — it deliberately does NOT restate the base definitions; pair it with the nasa-se-handbook pack. Scope: NASA space-flight SE practice (Vol 1 Practices); thin on the Vol 2 process-appendix detail, on non-NASA/commercial life cycles, and on software-engineering methodology.
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Dağıt:s deception-based honeytokens in Active Directory including fake privileged accounts with AdminCount=1, fake SPNs for Kerberoasting Tespit (honeyroasting), decoy GPOs with cpassword traps, and fake BloodHound paths. Monitors Windows Security Event IDs 4769, 4625, 4662, 5136 for honeytoken interaction. Use implementing yaparken AD deception defenses for Tespit etme lateral movement, credential theft, and reconnaissance.
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Audit whether a "current product focus receipt" (Decision/Now/Why-now/Evidence/Not-now/Cut-suspend/First-visible-proof/ Acceptance-gate/Kill-revisit-trigger/Owner/Review-date) and the work order it gates state a real, testable decision and a real agent launch — not planning-placeholder prose that sounds like an assignment but can't be proven. Use when deciding whether to launch an agent chain against a milestone or a stated "focus," when a work order lacks explicit input/output/owner/proof-gate, or when an acceptance gate would only prove a cached UI state instead of daemon truth. NOT for distinguishing roadmap items from energy-driven sidequests (legible-roadmap-with-sidequests), reviewing a PRD/prototype for product-completeness gaps like cold start or pricing (product-reality-reviewer), or accepting already-finished work against a personal acceptance bar (port-daddy-user-surrogate-pm-review).
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Analyze prompts and offer depth / token-budget options BEFORE answering. Use this skill when the user wants to control token usage, tune response depth, choose between short and long answers, or optimize their prompt. Triggers on: "tokens", "token budget", "depth", "consumption", "short vs long answer", "how many tokens", "save tokens", "answer at 50%", "give me the short version", "I want to control how much you use", "tune your response", "presupuesto de tokens", "profundidad", "responde al 50%", "dame la versión corta", or any equivalent phrasing in English or Spanish. If the user wants to control length, detail or depth -- even without mentioning tokens explicitly -- this skill applies.
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Hunting skill for cache poison vulnerabilities. Built from 10 public bug bounty reports including X-Forwarded-Host poisoning, X-HTTP-Method-Override / GCS cache, reflected→stored XSS via cache, classic Omer-Gil Web Cache Deception, Cloudflare Cache Deception Armor bypass, session-token cache deception, Akamai hop-by-hop smuggling → server-side edge poisoning, and Kettle's 2024 path-normalization WCD against Cloudflare/Fastly/GCP. Host/X-Forwarded-Host injection that reaches app logic (reset-link poisoning, routing SSRF, OAuth issuer) is owned by hunt-host-header; this skill owns the case where the poisoned response is CACHED and served to other users. Use when hunting cache poisoning, Web Cache Deception, CDN-fronted apps.
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운영자가 설정한 Team 서버의 업데이트를 지정된 Night Watch 담당자를 통해 조율합니다. 기존 업데이트 체계의 검증과 복구 결과를 확인하고 정식 승인까지 추적합니다.
개념, 업무 흐름, 소프트웨어와 클라우드 구성을 도표로 정리합니다. 브라우저에서 여는 SVG 포함 HTML이나 편집 가능한 Excalidraw 파일로 저장합니다.
Manim 애니메이션으로 기술 원리, 그래프, 시스템 구성과 제품 흐름을 설명합니다. 스토리보드와 장면 계획을 세우고 짧은 영상과 썸네일을 만듭니다.
Spring Boot와 Quarkus의 Java 코드를 명명, 불변 데이터, 예외 처리, 프로젝트 구조와 테스트 규약에 따라 작성하고 검토합니다. 빌드 파일로 프레임워크를 판별합니다.
과학 원리, 사물의 구조, 작업 흐름을 색상과 여백이 일관된 SVG 도표로 만듭니다. 밝은 화면과 어두운 화면을 지원하며 단일 HTML 파일로 출력합니다.
기존 작업이 수용 조건을 충족하는지 필요한 만큼만 검사합니다. 현재 상태에도 유효한 결과를 재사용하고, 통과 여부와 실행하지 못한 이유를 정리한 뒤 검증을 마칩니다.
LLM Wiki의 페이지 간 모순, 오래된 내용, 끊어진 링크와 출처 부족을 점검합니다. 본문을 자동 수정하지 않고 근거와 수정안을 중요도별로 정리합니다.
저장된 자료를 읽어 LLM Wiki의 출처, 인물·조직, 개념, 종합 지식 페이지로 정리합니다. 상호 링크와 색인, 수집 이력을 갱신하고 기존 정보와의 모순을 기록합니다.
Generate, remix, and manage short video clips through OpenAI's Sora API. Use it to create cinematic shots, produce b-roll—supplementary footage for an edit—or try different versions of a video concept.
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Generate mobile app screen concepts and connected screen flows for iOS, Android, or cross-platform products. The skill emphasizes readable text, consistent colors and navigation, and clean phone mockups. It produces images rather than implementation code.
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아직 형태가 잡히지 않은 아이디어를 단계적인 질문과 대안 검토로 구체적인 디자인으로 정리합니다. 초기 구상에서 나아갈 방향을 좁히는 데 사용합니다.
Generate 3D models from text descriptions or images through fal.ai for game assets, augmented reality previews, product mockups, and concept sculpting. This catalog entry points agents toward the upstream bundle needed for the full workflow.
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Create a brand overview image combining logo concepts, colors, typography, and examples across digital and physical materials. The skill bases the visual direction on the brand’s purpose and audience, arranging connected panels in a clear presentation grid.
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Plan an AI music album from its concept through lyric drafts, track order, and export. Intended for independent album experiments and brand soundtracks, with the full production workflow provided through a separately installed upstream bundle.
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