'complete' 검색 결과

Execute code-quality gates for a completed phase including tests, coverage, lint, types, build, CI/CD, staging, migrations, and spec coverage

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when about to claim work is complete, fixed, or passing — requires running verification commands and confirming output before making any success claims. Evidence before claims, always.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrate backend spec production from completed IA shards through classify and write passes with mandatory ambiguity gates

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validate ideation completeness through constraints, exhaustion, rubric checks, and output compilation

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compile architecture draft into dated architecture design and engineering standards with rubric-complete quality gates

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete WhatsApp management for OpenClaw agents: per-conversation memory (groups + DMs), unanswered message tracking, loop prevention, and multi-PA coordination. Use when: tracking conversation context, recalling past decisions, finding unanswered messages, or preventing echo/duplicate message loops.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Complete feature development with structured options for merge, PR, or cleanup

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review final-assignment materials for requirements, citations, AI-use disclosure, and data-analysis completeness.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Review homework notebooks for completeness, reproducibility, safe execution, and academic-integrity boundaries.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and implement context-fit frontend interfaces with deliberate art direction, accessible interaction, responsive behavior, complete states, and rendered verification. Use when creating or substantially reworking pages, components, product workspaces, dashboards, marketing sites, editorial surfaces, commerce flows, or justified immersive experiences.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Validate documentation before merging - check completeness, broken links, code example accuracy, and factual correctness. Use when reviewing docs for quality gates or running pre-merge doc validation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Export Twitter/X data to CSV for analysis using Xpoz. Search by keywords, author, date range, and download complete datasets (up to 500K rows). Use when asked to "export tweets", "download Twitter data", "get tweets as CSV", "Twitter dataset", or "bulk tweet download".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run or maintain the full tdmcp repo quality-audit team: command sweeps, all package/Makefile/CI gates, security review, usability/flow review, refactor/test-gap analysis, coverage hardening, QA, and follow-up fix waves. Use whenever the user asks for a complete audit, improve repo/code quality, test all commands, find security/usability failures, refactor debt, add missing tests, re-run the audit, continue a previous quality wave, or verify the repo is ready.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bring this machine to a complete, verified AOS installation (skills, MCP servers, gate hooks, memB, OpenWiki, Synapse). Generated from plugin-commands.json; invoke as $bdb-aos:setup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Set up a complete CI/CD pipeline for a project. Detects the stack, then generates and validates GitHub Actions workflows, a Dockerfile, and a Makefile as one coherent pipeline. Use when asked to "set up CI", "add pipeline", "create GitHub Actions", "ci/cd setup", "automate builds", "containerize", or "add a Makefile".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bring a machine to a complete, verified AOS installation: the skills pack, MCP servers, gate hooks, memB (engine, MCP, ambient hook, WebUI), OpenWiki (CLI, auth, harness integrations, refresh daemon) and Synapse. Run once on a new computer, and again whenever something in the ecosystem stopped working.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Convert any document into a production-ready Ozor.ai video prompt. Use this skill whenever the user uploads a document (PDF, PowerPoint, Google Doc, Markdown, text file, or URL) and wants to turn it into a video. Also trigger when the user mentions 'turn this doc into a video', 'make a video from this PDF', 'video from my pitch deck', 'convert this presentation to video', 'video from this blog post', 'video from these release notes', 'create a video from this document', 'turn this into an explainer', or any request to transform written content into video format using Ozor. This skill reads the document, extracts the core message, structures it into scenes, and outputs a complete prompt the user can paste directly into Ozor.ai.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement, review, or improve maps and location features in iOS/macOS apps using MapKit and CoreLocation. Use when working with Map views, annotations, markers, polylines, user location tracking, geocoding, reverse geocoding, search/autocomplete, directions and routes, geofencing, region monitoring, CLLocationUpdate async streams, or location authorization flows. Also use when working with maps, coordinates, addresses, places, directions, distance calculations, or location-based features in Swift apps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.