'adoption' 검색 결과

Design a partner program by defining value for your company, partners, and shared customers. Provides frameworks for partner tiers, phased pilots, joint marketing, and build-versus-partner decisions, with explicit measures for expanding or ending a partnership.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing partner tiers and support levels
• Deciding whether to build internally or partner
• Defining pilot metrics and expansion criteria

Review MCP configurations for exposed credentials, risky command patterns, and package versions that can change unexpectedly. Produce a report with severity levels and suggested fixes without running the commands defined in the configuration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing .mcp.json before committing changes
• Checking a new MCP server before adoption
• Finding hardcoded credentials in configurations

Retrieve GitHub Copilot usage for an organization or enterprise through the GitHub CLI and REST API. Summarize active users, suggestion acceptance, and chat activity, with aggregate results or per-user tables for selected dates.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check Copilot adoption across an organization
• Review suggestion acceptance rates and chat usage
• Compare usage across individual users

Find and evaluate a small, compatible set of AI Agent Skills for a multi-step goal. Check existing capabilities, compare candidates for fit and safety, identify overlaps, and prepare recommendations with installation subject to user approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a set of Skills for a multi-step project
• Audit installed Skills for overlap and conflicts
• Find supporting Skills for writing quality or compliance

Inspect npm packages and dependency files for malicious installation scripts, credential theft, and self-spreading behavior. Use GuardDog scanning, source inspection, known-malicious version checks, and isolated execution to produce an evidence-based assessment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Vetting a new npm dependency before adoption
• Reviewing dependency files during code review
• Checking lockfiles against known-malicious versions

Find npm and PyPI packages that imitate trusted package names to exploit typing mistakes. Compare spelling similarity, publication dates, downloads, and maintainer details, then rank suspicious candidates and produce reports or blocklists for further review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Auditing project dependencies for misleading package names
• Monitoring names similar to important packages
• Investigating a suspected misspelled package installation

Build dashboards and reports for security operations teams using incident and alert data. Track detection and resolution times, false alarms, analyst workload, and detection coverage to assess operational trends and the effects of improvement initiatives.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing monthly or quarterly security operations reports
• Reviewing analyst workload and shift staffing
• Comparing performance before and after tool adoption

This skill is identified as development related, but the supplied text gives no concrete workflow or expected result. Its name suggests continuous learning, yet the material does not explain any learning behavior or how to use it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review the stated purpose of a development skill
• Check whether learning behavior is documented
• Assess available usage information before adoption

Audit an existing project's design documents and supporting files to check whether their formats work with the template's skills. Get a numbered migration plan that prioritizes gaps and required changes, rather than simply listing what exists.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking existing documents before adopting a project template
• Auditing game design documents or architecture decision records
• Prioritizing gaps in project tracking and configuration files

Implement and debug Google OAuth 2.0 refresh tokens bound to a client key. Covers key generation, signed DPoP proofs that demonstrate key possession, and nonce challenge retries in WebCrypto and JavaScript environments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Bind Google OAuth refresh tokens to a client key
• Debug DPoP proof signatures and nonce retries
• Plan a backend for a browser application using DPoP

Use past local Codex sessions to propose updates to a selected skill and project memory file. Recurring tasks are replayed, changes are checked on separate validation tasks, and proposals are staged for review before adoption.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn recurring Codex requests into skill updates
• Consolidate feedback into skills and project memory
• Compare validation scores before applying changes

Study a reference adaptation connecting the shared skillopt_sleep cycle to a custom DeepSeek backend for OpenClaw. It provides prototype wrappers, sample tasks, and a porting checklist for developers building an integration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Study a prototype DeepSeek backend for OpenClaw
• Identify changes needed for current engine interfaces
• Review sample tasks and environment-specific settings

Review past dsh agent sessions to identify recurring tasks and test proposed skill or memory improvements. This skill guides preview runs, validation on reserved tasks, staged proposals, adoption with backups, and scheduled nightly cycles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract recurring tasks from past agent sessions
• Test proposed skill and memory improvements
• Review staged changes before adopting them

Review AI vendor contracts against your organization's policies. Identify gaps in data training rights, liability, model changes, and other terms, then prepare targeted contract edits and escalation options for issues outside agreed limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an AI vendor's agreement before adoption
• Checking whether inputs may be used for model training
• Comparing contract terms with internal AI policies

Assess how your product team uses AI across five capabilities, from shared context to team review practices. Identify whether work is simply faster or fundamentally redesigned, and choose which capability to strengthen next.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing a product team's AI maturity
• Choosing the next AI capability to develop
• Reviewing workflows beyond task automation

Evaluate whether a product feature deserves investment by comparing revenue or retention benefits with development and ongoing costs. Receive calculations, payback estimates, and recommendations that account for strategic value and uncertain assumptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide whether to build a feature this quarter
• Compare feature ideas by financial return and strategic value
• Explain roadmap priorities to stakeholders

Analyze Mixpanel data with Python to explore usage trends, conversion steps, returning users, and differences between user groups. Also manage business context documents that explain an organization's structure and goals to AI assistants.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find where users drop out before purchasing
• Compare retention across user groups
• Explore feature usage and revenue trends

Make C# code testable when it directly uses static services such as time, files, or processes. It introduces existing abstractions or minimal wrappers, supplies dependency registration, and demonstrates tests with controlled substitutes for real resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Introducing TimeProvider for time-dependent tests
• Testing file operations with MockFileSystem
• Wrapping Environment, Console, or Process calls

Evaluate policy effects with difference-in-differences, a method comparing changes in treated and untreated groups. Guides assumption checks, regression models, staggered adoption analysis, robustness checks, and reporting with event study plots.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluating policy effects with comparison groups
• Checking outcome trends before treatment
• Analyzing policies adopted at different times

Plan how to introduce AI into economic research, teaching, or policy analysis. Identify suitable tasks, choose tools with privacy and licensing in mind, and design reproducible data workflows with output checks and ongoing model monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan AI adoption for an economics research team
• Design data workflows that protect personal information
• Choose AI tools for teaching or policy analysis

Refactor React components that have accumulated many boolean options. Apply component composition, meaning building interfaces from cooperating parts, and shared state patterns to design reusable component libraries and clearer APIs, with guidance specific to React 19.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refactoring components with too many boolean props
• Designing reusable component library APIs
• Reviewing shared state and provider responsibilities

Generate images, reference-based edits, and video clips from stills through configured APIs or local Codex and Grok tools. Check available generation options, configure keys, track estimated costs, and resume interrupted jobs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate an image or edit using reference images
• Animate a still image
• Check available tools and configure API keys

Activate this skill for ANY future-oriented question. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", "Can [X] succeed?", "What's the future of X?", foresight analysis, scenario planning, STEEEP analysis, futures cone, prediction requests, or any question about a future outcome. Year is NOT required — the engine infers the horizon. Also activate when the user says "predict", "forecast", "what are the odds", "scenario analysis", or asks about competitive races, technology adoption, geopolitical shifts, or market dominance. REQUIRES web search to be enabled — if web search is unavailable, tell the user before proceeding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", prediction requests with high stakes, foresight analysis, STEEEP scenario planning, futures cone, competitive race analysis, technology adoption curves, geopolitical shifts, or any question about a future outcome that deserves rigorous multi-step analysis. This agent runs a 12-step deterministic pipeline: Claude handles intelligence, Python handles all arithmetic. Year is NOT required — the engine infers the horizon. REQUIRES: Bash tool + Python 3.x. Not compatible with claude.ai web (use Soft Predict Future instead).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.