'oss' 검색 결과

Set up OSS-Fuzz for an open-source project and test it locally. Prepare configuration and build files, run fuzz tests that probe code with varied inputs, reproduce reported crashes, and inspect which code the tests reach.

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• Preparing an open-source project for OSS-Fuzz enrollment
• Testing an OSS-Fuzz build locally before submission
• Reproducing crashes from OSS-Fuzz reports

可审计的 agent 团队：BMAD 全自动研发流水线编排器。编排 9 个专业 Agent（PM、架构师、UI Designer、Tech Lead、Scrum Master、Frontend、Backend、QA、DevOps）从需求到部署，每一步都有事件溯源 + 不可绕过门禁 + 确定性 eval——可验证测试真跑、门禁真过。支持单环节切片命令（/boss:plan /review /qa /ship）与无 CLI 纯 Markdown 降级。 Triggers（全流水线）: 'boss mode', '/boss', '全自动开发', '从需求到部署', '帮我做一个', 'build this', 'ship it', '全流程', '自动化开发', '一键开发', 'start a project', 'new feature' Triggers（单环节切片，对存量项目最常用）: - /boss:review — '帮我评审', '代码评审', '技术评审', '看看这段代码有什么问题', '风险评估', 'review this', 'review my PR', 'code review' - /boss:qa — '跑一下测试', '补测试', '检查质量', '测试覆盖够不够', '门禁过了吗', 'run the tests', 'check quality', 'add tests' - /boss:plan — '先出个方案', '写个 PRD', '设计一下架构', 'write a spec', 'plan this feature' - /boss:ship — '可以发布了吗', '部署检查', 'ready to ship', 'deploy checks' Does NOT trigger: - 单文件修改或简单 bug 修复（直接编辑即可） - 纯代码阅读或解释（使用 read 工具） - 已有 pipeline 正在运行时的重复启动 - 极小事（预计 <30 分钟人工可完成、不需要 PRD/架构/门禁记录） Output: 完整项目代码 + PRD/架构/UI/测试/部署文档，写入 .boss/<feature>/ 目录

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구체적인 사례와 코드 대조로 프로젝트의 업무 용어와 개념 경계를 명확히 합니다. 합의한 정의를 용어집에 기록하고 필요에 따라 중요한 설계 결정의 이유도 남깁니다.

• 모호한 업무 용어 정리
• GLOSSARY.md 생성과 갱신
• 업무 정의와 코드의 일치 여부 확인

Gather web pages, social discussions, job listings, video subtitles, and other online material across 16 platforms. The skill checks available connections and guides the agent to suitable command-line tools or APIs for retrieving content.

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• Collecting discussions about a topic across social platforms
• Retrieving articles and RSS content
• Finding job listings on LinkedIn and Boss直聘

Benchmark distributed consensus protocols, methods computers use to agree on shared results. Compare processing rates, response times, scaling behavior, and resource usage across Byzantine, Raft, and Gossip, then identify bottlenecks and potential tuning changes.

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• Comparing Byzantine, Raft, and Gossip performance
• Measuring response times under increasing load
• Finding CPU, memory, or network bottlenecks

Connect ruflo agents running on different machines to share status, tasks, and results through signed messages. Coordinate work ownership across nodes, verify messages against registered public keys, and synchronize shared rooms over HTTP.

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• Sharing agent status and results across machines
• Coordinating ownership of shared tasks
• Handing work from one node to another

Coordinate compliance work across multiple standards and regulations. Use a company profile to identify relevant frameworks, map shared controls, prepare mock audits, and build a unified evidence checklist showing where documents can be reused.

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• Starting a compliance program across multiple frameworks
• Planning an annual audit calendar
• Preparing for a certification audit

Work with the Toss Securities Open API for Korean and US stock market data, stock info, exchange rates, market calendars, account and holdings lookups, order history, buying power, sellable quantity, commissions, conditional orders, rankings, trading trends, market indicators, realtime WebSocket integration, and delegated trading workflows. Use when an agent needs to run user-delegated buy/sell/order-management loops, call or build against developers.tossinvest.com, inspect the Toss OpenAPI schema, generate client code, or operate with TOSS_API_KEY/TOSS_SECRET_KEY credentials.

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직전 설명을 이해하기 어려울 때 배경을 보충해 다시 설명합니다. 간소화된 기술 영어 표준과 프로젝트 용어집을 따라 대화의 흐름을 전달합니다.

• 직전 설명을 이해하기 쉽게 다시 듣기
• 대화 흐름을 이해할 배경 설명 받기
• 프로젝트 공통 용어로 설명 받기

기사나 공지를 바탕으로 X, LinkedIn, Threads, Bluesky용 게시글을 작성합니다. 작성자의 말투를 유지하며 각 매체의 제약에 맞게 조정합니다.

• 새 서비스 공지를 여러 매체에 전합니다
• 기사를 매체별 게시글로 바꿉니다
• 출시 정보를 여러 매체에 맞게 조정합니다

Coordinate gossip protocols, where nodes pass information among peers, for distributed systems. The skill covers update propagation, state synchronization, conflicting changes, failure detection, and tuning communication so nodes eventually reach a consistent state.

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• Designing update propagation between distributed nodes
• Synchronizing state and resolving concurrent changes
• Managing node membership and detecting failures

Investigate a username, file, code identifier, URL, or concept by exploring connected entities across relevant sources. Produce a dossier with a summary, relationship diagram, source references, and a machine-readable JSON graph.

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• Mapping connections around a username or concept
• Investigating references to a file or code identifier
• Tracing links to an architecture decision record

Build a PowerPoint presentation with Morph transitions, which smoothly transform shared shapes between slides. Plan how objects move, resize, rotate, appear, and exit across a slide sequence, then deliver a single .pptx file.

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• Shrinking and moving a cover title onto the next slide
• Creating a continuous background across a slide sequence
• Explaining changing values with animated rectangle bars

Analyze closed sales opportunities to explain patterns behind wins and losses. Compare win rates, deal sizes, and sales stages, then use available transcripts and emails to recommend evidence-based process improvements and coaching priorities.

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• Comparing win rates by sales rep or lead source
• Finding the sales stages where deals are lost
• Identifying objections in major wins and losses

Analyze won and lost HubSpot deals by customer segment, competitor, deal value, and the stage before a loss. Group recorded loss reasons into themes and post a weekly Slack summary with concrete recommendations, leaving deal records unchanged.

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• Reviewing weekly win rates and won or lost deal value
• Comparing outcomes by customer segment and competitor
• Finding price bands and sales stages with frequent losses

Review changes of 1,000 lines or more by dividing work across files, focused areas, and commits. Combine several review methods, check consistency across subsystems, verify bug fixes, and report validated findings alongside a checklist showing review coverage.

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• Reviewing feature branches with 1,000 or more changed lines
• Checking a large change before merging
• Finding inconsistencies across commits and subsystems

Update an existing intellectual property practice profile one setting at a time. Review current values, understand how changes affect later work, and adjust risk preferences, contacts, portfolio coverage, enforcement policies, or open-source license review rules.

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• Updating legal contacts and escalation routes
• Changing trademark monitoring coverage
• Revising infringement enforcement policies

Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.

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Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Total-loss determination and settlement pricing. Triggers: "total loss valuation", "claims value", "settlement offer", "salvage estimate", "insurance claim pricing", "total loss threshold", "what's the claim worth", "settlement range", "pre-loss value", "diminished value", "total loss determination", insurance claim vehicle valuation, total-loss determination, settlement pricing, or salvage value estimation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.