'capability' 검색 결과

Comprehensive Lean Six Sigma consulting skill supporting all belt levels (White Belt to Master Black Belt). Use this skill for DMAIC/DMADV project execution, Lean waste elimination (VSM, 8 Wastes/DOWNTIME, 5S), statistical analysis (process capability Cp/Cpk, control charts, hypothesis testing), and Six Sigma training/education. Triggers include "improve process", "reduce defects", "sigma level", "DMAIC project", "value stream mapping", "Kaizen", "process capability", "control chart", "root cause analysis", "5 Whys", "Fishbone diagram", "FMEA", "DOE", or requests involving process improvement methodologies.

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Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.

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Knowledge base from the DoD M&Q BoK (the Manufacturing and Quality Engineering Body of Knowledge), v3.0 of July 2025, published by OUSD(R&E). Use for the manufacturing-and-quality view of the defense acquisition life cycle: what M&Q engineers do across the Adaptive Acquisition Framework phases (Pre-MDD, Materiel Solution Analysis, Technology Maturation and Risk Reduction, Engineering and Manufacturing Development, Production and Deployment, Operations and Support); the twelve M&Q threads (A–L) built on the '5 Ms'; manufacturing feasibility and producibility; Manufacturing Readiness Levels and assessments; Key/Critical Characteristics and process capability (Cp/Cpk, SPC); the standards stack (AS6500, AS9100/ISO 9001, AS9103, AS9145); technical reviews (ASR/PDR/CDR/PRR) and milestone gates; DCMA surveillance; industrial base and supply chain; and sustainment (LCSP, IPS, ILA, ISR). Scope is DoD acquisition M&Q practice on the Major Capability Acquisition path — it is a best-practice compilation, not policy, and is thin on detailed shop-floor process engineering, contract law, and the full text of the cited industry standards (which it names but does not reproduce).

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Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (OUSD R&E, August 2022). Use for DoD test and evaluation (T&E) across the Adaptive Acquisition Framework — the four test communities (contractor testing, developmental DT&E, operational OT&E, live-fire LFT&E); the five operational events (Operational Demonstration, EOA, OA, IOT&E, FOT&E) and the OTRR gate; the T&E Strategy / TEMP and its IDSK, with the upstream documents that feed it and the test plans that decompose it; the T&E organizations and oversight roles (USD(R&E), DOT&E, TRMC, the Service Executives and OTAs, the T&E WIPT, CDT); and pathway-specific T&E for all six acquisition pathways (Urgent Capability, Middle Tier, Major Capability, Software, Defense Business Systems). Statutory anchors include 10 U.S.C. §4171 (IOT&E) and §4172 (LFT&E/FUSL). This is guidance, not policy: it points outward to DoDI 5000.89 and the DoDI 5000-series for binding detail, and is thin on detailed test statistics/design-of-experiments methodology, cost estimating, the Acquisition of Services pathway, and any CUI/CAC-gated companion guides.

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Ranks eligible skill candidates with explicit retrieval evidence, capability coverage, and uncertainty. NOT for discovery, authorization, or execution planning.

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Matches natural language task descriptions to appropriate skills using semantic similarity. Handles fuzzy matching, intent extraction, and capability alignment. Activate on 'find skill', 'match task', 'semantic search', 'skill lookup', 'what skill for'. NOT for ranking matches (use dag-capability-ranker) or skill catalog (use dag-skill-registry).

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

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Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.

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AI Agent skill marketplace integration for A2A Market. Enables agents to buy skills, sell skills, and earn money autonomously. Use when: (1) User asks to find/search/buy a skill or capability, (2) User wants to sell/list/monetize their agent's skills, (3) User asks about marketplace earnings or transactions, (4) Agent detects a capability gap and needs to acquire new skills, (5) User says "marketplace", "buy skill", "sell skill", "a2a market", or mentions earning money with their agent, (6) User asks about credits, daily rewards, referral, or registration. Supports x402 USDC payments on Base L2 and Credits payment system.

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Use when a new feature needs a table, column or capability — evolving the schema so the new thing joins the permission, audit and RLS contract instead of sitting outside it. Covers the DDL/DML split, the resource/capability wiring, and the self-verifying migration.

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ClawHub에서 필요한 플러그인이나 스킬을 찾고 OpenClaw로 검증·설치·업데이트합니다. 전용 CLI로 스킬 삭제·게시·동기화도 처리합니다.

• 필요한 기능을 제공하는 플러그인을 찾을 때
• 지정한 스킬을 검증한 뒤 설치할 때
• 설치한 스킬을 업데이트하거나 삭제할 때

대화형 CLI, SSH 연결, 로컬 개발 프로세스를 새 터미널에서 여는 계획을 만듭니다. 실행 파일과 인수를 구분하고 미리 보기, 터미널 감지, 대체 실행과 복구를 지원합니다.

• 대화형 CLI를 별도 창에서 엽니다
• SSH 연결이나 개발 프로세스를 시작합니다
• 실행 전에 시작 계획을 확인합니다

개발 전에 제품의 목적을 검토하고 추진 여부, 첫 사용의 장애물, 기능 우선순위를 정리합니다. 근거와 다음 행동을 담은 기획서나 로드맵을 작성합니다.

• 모호한 아이디어의 기획서 작성
• 개발할 기능의 우선순위 결정
• 출시 전 첫 사용 과정 점검

기계 학습이 없는 기존 앱에 추천, 분류, 예측 기능을 추가하는 절차를 정리합니다. 문제와 데이터 확인부터 초기 모델의 학습, 평가, 통합까지 돕습니다.

• 기존 앱에 추천 기능 추가
• 학습 데이터 준비 상태 확인
• 대체 규칙을 갖춘 예측 기능 통합

AI 개발 전에 성공 조건을 정하고 코드·규칙·모델·사람의 평가로 새 기능과 기존 기능을 확인합니다. 여러 차례 시도한 결과에서 신뢰성을 기록합니다.

• AI 구현 전에 성공 조건 정의
• 프롬프트나 에이전트 변경 후 회귀 확인
• 여러 차례 시도로 신뢰성 측정

모호한 아이디어를 목표, 작업 범위, 검증 가능한 완료 조건이 담긴 명세로 정리합니다. 기능과 의존 관계를 나누고 사람의 승인을 거쳐 계획, 작업 분할, 구현으로 진행합니다.

• 구현 전에 아이디어를 구체화할 때
• 완료 조건을 포함한 요구사항 작성
• 큰 요구사항을 독립 기능으로 나누기

Prepare media assets required by an existing OD Next plan. Reuse verified capabilities and suitable assets, acquire files, measure dimensions, and retrieve background job results while preserving required content, licensing constraints, and quality conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing all media assets required by an existing plan
• Reusing suitable user assets across multiple pages
• Measuring asset dimensions for layout sizing

fal.ai의 AI 초해상도 모델로 이미지와 동영상의 해상도를 높이는 스킬입니다. 실행하려면 원본 배포 패키지를 별도로 설치해야 합니다.

• 이미지 해상도를 높입니다
• 동영상을 고해상도로 변환합니다
• 에이전트에 AI 초해상도 처리를 요청합니다

Compare ruflo and HAL systems for running the GAIA benchmark, an AI evaluation. Examine differences in code execution, browsing, file handling, memory, and model selection to prioritize improvements by expected impact and effort.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan the next round of GAIA improvements
• Compare code execution and browser capabilities
• Weigh improvement priorities against implementation effort

Automatically repair source code against a failing test while preserving the test itself. Set a spending limit and attempt count, then receive verification results and usage costs based on rerunning the test command.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Repairing a failing CI test after a commit
• Implementing code to pass a test written first
• Investigating a regression within a fixed budget

Review a reusable Agent Skill package before implementation. Validate its identity fields, separate instructions from external capabilities and execution timing, and receive a validation report with a minimal set of building blocks and reasons for each choice.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a workflow before turning it into a reusable skill
• Checking SKILL.md identity fields
• Choosing instructions, scripts, or delegation

Explain how dot works and check why a task is blocked, including computer access, login, approvals, and Slack errors. It provides supported next steps and answers questions about changing dot’s avatar and the user’s message color.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand why a dot task is blocked
• Check computer connection or login requirements
• Clarify Slack errors and delivery status

Create a new native Muse plugin package with its manifest and requested capability files, then validate it using the installed muse CLI. Receive a file inventory, validation results, and an installation proposal for later use.

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• Package skills as a new Muse plugin
• Create a Muse plugin with command templates
• Define hooks or MCP server connections