Claude Code Skills · page 130

Use when user asks about 南传, 上座部, Theravāda, 巴利, 清净道论, Visuddhimagga, 戒定慧, 四十种业处, kammaṭṭhāna, 十遍, kasiṇa, 七清净, 十六观智, 阿毗达摩, Abhidhamma, 觉音尊者, Buddhaghosa, 大寺派, Mahāvihāra, 缘起十二支, 三法印, or wants teaching in 觉音尊者 Buddhaghosa's voice. Triggers include "觉音"、"Buddhaghosa"、"清净道论"、"Visuddhimagga"、"戒定慧三学"、"四十业处"、"七清净"、"十六观智"、"阿毗达摩注释"、"尼柯耶注释"、"上座部论师"、"大寺派" — invoke whenever user's question touches Theravāda commentarial / Visuddhimagga / Abhidhamma exegesis, even without explicit request.

Use when user asks for a sequenced learning path within a Buddhist tradition — 学修次第, 先学什么, 从哪入门, 下一步读什么, curriculum, 学习计划, 路径推荐. Differs from /compare-masters (parallel opinion) and /master-debate (adversarial dialectic) by being 纵向 / 时序: stage-by-stage plan keyed on tradition × level (L0-L3) → foundation → intermediate → advanced + blind spots. Trigger is planning intent — "禅宗对比" goes to /compare-masters; "禅宗从哪开始学" goes here.

Use when user explicitly asks for an adversarial / multi-round dialectic between masters — 祖师辩论, 各执一词, 谁更对, debate, 应成 vs 顿悟, 顿渐之争. Differs from /compare-masters (parallel single-round) by being adversarial multi-round via fresh-subagent orchestration. Topics 空有 / 禅净 / 性相 / 戒律 vs 内观 — trigger is adversarial framing: "禅净比较" → compare; "禅净辩论 / 谁更究竟" → here.

Use when user asks about 华严宗, 法界缘起, 四法界, 事事无碍, 十玄门, 六相圆融, 金师子章, 一即一切, 因陀罗网, 华严经, 五教判, or wants teaching in 法藏大师 Fazang's voice. Triggers include phrases like "华严"、"法藏"、"贤首"、"法界"、"事事无碍"、"十玄"、"六相"、"金师子"、"一即一切"、"理事无碍"、"因陀罗网"、"别教一乘"、"五教"、"毗卢遮那"、"一真法界" — invoke whenever user's question touches Huayan doctrine, even without explicit request.

Use when user asks about 禅宗, 六祖, 坛经, 顿悟, 见性成佛, 直指人心, 不立文字, 自性, 本心, 无念, 无相, 无住, 般若, 定慧一体, 明心见性, 南宗禅, or wants teaching in 慧能大师 Huineng's voice. Triggers include phrases like "禅"、"慧能"、"六祖"、"坛经"、"顿悟"、"见性"、"本来面目"、"菩提本无树"、"风动幡动"、"本来无一物"、"自性"、"机锋"、"烦恼即菩提"、"不二"、"弘忍" — invoke whenever user's question touches Chan/Zen doctrine, even without explicit request.

Use when user asks about 中观, 三论宗, 般若, 空性, 中道, 八不, 缘起性空, 法华经, 金刚经, 维摩诘, 不二法门, 一佛乘, 大智度论, or wants teaching in 鸠摩罗什 Kumārajīva's voice. Triggers include phrases like "中观"、"三论"、"空"、"般若"、"中道"、"八不"、"缘起性空"、"法华"、"金刚经"、"维摩诘"、"不二"、"实相"、"一佛乘"、"鸠摩罗什"、"罗什"、"会三归一"、"火宅"、"方便"、"中论"、"大智度论"、"百论"、"十二门论" — invoke whenever user's question touches Madhyamaka/Prajñā/Lotus doctrine, even without explicit request.

Use when user asks about 南传, 上座部, 缅甸内观, Mahasi Method, 标记法, Noting Method, 腹部起伏, 毗婆舍那, vipassanā, 四念处, 七清净, 十六观智, 刹那定, 行舍智, 马哈希尊者, Mahasi Sayadaw, Mahasi Sasana Yeiktha, IMS, or wants teaching in 马哈希尊者 Mahāsi Sayādaw's voice. Triggers include "马哈希"、"Mahasi"、"Sayadaw"、"标记法"、"腹部起伏"、"缅甸内观"、"密集禅修"、"十六观智"、"刹那定"、"妄念太多" — invoke whenever user's question touches Burmese vipassanā / Mahasi noting method, even without explicit request.

Use when user asks about 藏传佛教, 噶举派, 大手印, phyag chen, 拙火, tummo, 那洛六法, 苦行, 闭关, 道歌, mgur, 米拉日巴, 玛尔巴, 上师瑜伽, 出离, 暇满, 中阴, 气脉明点, 觉受, nyams, 本觉, rig pa, or wants teaching in 米拉日巴尊者 Milarepa's voice. Triggers include "米拉日巴"、"密勒日巴"、"Milarepa"、"道歌"、"十万歌集"、"大手印"、"拙火"、"那洛六法"、"玛尔巴"、"噶举"、"白教"、"山洞修行"、"苦行"、"上师瑜伽"、"中阴"、"明空" — invoke whenever user's question touches Tibetan Kagyu / Mahāmudrā / yogi practice or asks about Milarepa's life and teachings, even without explicit request.

Use when user asks about 蕅益大师, 教宗天台, 行归净土, 六信, 弥陀要解, 教观纲宗, 灵峰宗论, 性相融会, 禅教律净, 念佛, 事持理持, 现前一念, 一念心性, 净土宗第九祖, 明末四大高僧, 占察忏, or wants teaching in 蕅益 Ouyi's voice. Triggers include "蕅益"、"智旭"、"弥陀要解"、"教宗天台"、"行归净土"、"六信"、"事持"、"理持"、"性相融会"、"禅教律净"、"教观纲宗"、"灵峰"、"现前一念"、"明末四大高僧"、"占察轮相" — invoke whenever user's question touches Ouyi's cross-school synthesis or Tiantai-Pureland integration, even without explicit request.

Use when user asks about 藏传, 格鲁派, Gelug, 黄教, 三主要道, 菩提道次第广论, lam rim, 密宗道次第广论, 应成中观, 缘起性空, 辨了不了义, 宗喀巴, Tsongkhapa, Je Rinpoche, 甘丹寺, 戒律, 因明, 月称, 入中论, or wants teaching in 宗喀巴大师 Tsongkhapa's voice. Triggers include "宗喀巴"、"杰仁波切"、"Je Rinpoche"、"格鲁"、"黄教"、"道次第"、"广论"、"三主要道"、"应成中观"、"辨了不了义"、"甘丹"、"达赖喇嘛传承根基" — invoke whenever user's question touches Gelug doctrine / lamrim / Madhyamaka prasaṅgika / Tibetan tantra-shastra studies, even without explicit request.

Use when user asks about 唯识, 法相宗, 阿赖耶识, 末那识, 三性, 遍计所执, 依他起, 圆成实, 五位百法, 因明, 转识成智, 种子, 熏习, 瑜伽师地论, 成唯识论, or wants teaching in 玄奘法师 Xuanzang's voice. Triggers include phrases like "唯识"、"法相"、"玄奘"、"阿赖耶"、"末那"、"三性"、"百法"、"因明"、"转识成智"、"种子"、"遍计所执"、"依他起"、"圆成实"、"五种不翻"、"唯识三十颂"、"瑜伽"、"慈恩" — invoke whenever user's question touches Yogācāra/Vijñānavāda doctrine, even without explicit request.

Use when user asks about 虚云, 参禅, 话头, 念佛是谁, 疑情, 开悟, 桶底脱落, 禅七, 行香, 丛林, 五宗兼嗣, 临济, 曹洞, 沩仰, 云门, 法眼, 老实修行, 头陀行, 持戒, 禅净双修, 云居山, 南华寺, or wants teaching in 虚云老和尚 Xuyun's voice. Triggers include "虚云"、"参话头"、"念佛是谁"、"疑情"、"禅七"、"行香"、"丛林规矩"、"桶底脱落"、"五宗"、"杯子扑落地"、"老实修行"、"头陀"、"禅堂"、"坐禅"、"数息" — invoke whenever user's question touches Chan practice, meditation methods, or monastic discipline, even without explicit request.

Use when user asks about 印光大师, 净土, 念佛, 持名念佛, 十念法, 摄耳谛听, 老实念佛, 信愿行, 带业往生, 仗佛慈力, 自力他力, 竖出横超, 往生, 极乐, 阿弥陀佛, 净土三经, 敦伦尽分, 闲邪存诚, 因果报应, 文钞, 一函遍复, or wants teaching in 印光大师 Yinguang's voice. Triggers include "印光"、"文钞"、"老实念佛"、"信愿行"、"带业往生"、"仗佛慈力"、"横超竖出"、"都摄六根"、"净念相继"、"敦伦尽分"、"闲邪存诚"、"因果"、"十念法"、"摄耳谛听"、"一函遍复"、"净土三经"、"往生" — invoke whenever user's question touches Pure Land practice, Amitabha recitation, or faith-vow-practice, even without explicit request.

Use when user asks about 天台宗, 止观, 一念三千, 三谛圆融, 五时八教, 摩诃止观, 法华经, or wants teaching in 智者大师 Zhiyi's voice. Triggers include phrases like "天台"、"智者大师"、"止观怎么修"、"三谛"、"法华"、"一心三观"、"判教"、"圆教"、"四种三昧" — invoke whenever user's question touches Tiantai doctrine, even without explicit request.

Use when user asks about 中观, 空性, 缘起性空, 八不中道, 二谛, 世俗谛, 第一义谛, 戏论, 毕竟空, 不可得, 如幻, 离四句, 破自性, 难行道易行道, 龙树, or wants teaching in 龙树菩萨 Nāgārjuna's voice. Triggers include phrases like "空"、"中观"、"缘起"、"性空"、"八不"、"中道"、"二谛"、"世俗谛"、"第一义谛"、"戏论"、"毕竟空"、"不可得"、"如幻"、"离四句"、"涅槃与世间"、"龙树"、"中论"、"大智度论"、"十二门论"、"回诤论"、"易行道" — invoke whenever user's question touches Madhyamaka/emptiness/two-truths doctrine, even without explicit request.

Use ONLY when the user says they do not know which master or which teaching mode to use — 不知道问谁, 该找哪位祖师, 该用哪个模式, 有哪些法师, which master should I ask, help me choose. This is a router, not a teacher: it names a destination and stops. If the user asks an actual doctrinal or practice question, do NOT invoke this — let the matching master skill answer directly.

Analyzes Rails applications and generates comprehensive upgrade reports with breaking changes, deprecations, and step-by-step migration guides for Rails 2.3 through 8.1. Use when upgrading Rails applications, planning multi-hop upgrades, or querying version-specific changes. Based on FastRuby.io methodology and "The Complete Guide to Upgrade Rails" ebook.

Clean up after (or abandon) a Rails upgrade. Drop NextRails.next? and NextRails.current? branches and retire dual-boot scaffolding (Gemfile.next, Gemfile.next.lock, conditional Gemfile groups), keeping either the next or the current version. Trigger when the user says they are done with the upgrade, want to clean up dual-boot, want to drop NextRails branches, want to finish the upgrade, want to abandon or revert the upgrade attempt, want to roll back to the current Rails version, or want to pause this upgrade hop. Based on FastRuby.io's "Finishing an Upgrade" methodology, extended with an abandon/pause path.

Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.

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Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.

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Pre-flight checklist for long-running ML training jobs covering config diff, run naming, path verification, monitoring setup, and restart-cleanup. Use when the user asks to launch, kick off, start, restart, or kill a training run, or mentions launching a multi-hour or multi-day GPU job (python train, accelerate launch, torchrun, deepspeed, sbatch, tmux training).

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End-to-end paper reproduction from arxiv URL through smoke runs to replication experiments. Handles missing or partial official code, missing training scripts, missing hyperparameters, and private datasets via similar-public-dataset substitution. Use when the user asks to reproduce, implement, replicate, or re-run a paper from scratch, or pastes an arxiv URL with reproduction intent.

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Run health checks on Claude Code configuration and sessions. Use when troubleshooting Claude Code issues. (cozempic — does not shadow Claude Code's built-in /doctor)

Analyze Claude Code session bloat — shows token count, context usage %, and bloat breakdown. Use when the user asks about session size, context usage, or when you notice the context window is getting full.

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Treat the current session and auto-resume in a new terminal window.

Prune bloated session with a prescription. Removes progress ticks, stale reads, duplicate content, and more.

Query and orchestrate Arkloop Activity Record local activity data. Covers browser history, search terms, screen time, bluetooth, shell commands, window focus, keyboard, mouse, clipboard, screen content (accessibility tree), microphone audio transcription, and Codex sessions.

Query the user's screen recordings, audio, UI elements, and usage analytics via the local Screenpipe REST API at localhost:3030. Use when the user asks about their screen activity, meetings, apps, productivity, media export, retranscription, or connected services.

Check Screenpipe health status, process state, and diagnose common issues

Retrieve and analyze Screenpipe CLI backend logs and desktop app logs for debugging

Drive real macOS applications through the CUA Driver MCP server when the user asks to inspect, operate, or automate visible desktop UI.

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求职工具包。把「找岗位 → 签 offer」拆成六个独立子 skill：⓪ Job Hunt 批量发现岗位，① Job Description 解码具体 JD，② Resume 定向简历，③ BQ 准备行为面试，④ Offer Compare 对比多份 offer，⑤ Salary Negotiation 谈 package。任何求职相关请求（找工作、该不该投、改简历、准备面试、选 offer、谈薪）都从这里进，再路由到对应子 skill。关键词：求职, offer, career, job hunt, LinkedIn jobs, JD, resume, CV, behavioral interview, BQ, STAR, offer compare, salary negotiation, 薪资谈判, 选 offer。

行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。

Job Description 解码器 + Offer 策略系统。把任何 JD 翻译成「这家公司到底在招什么人 / 我的匹配度 / Gap 在哪 / 为什么投/不投 / 面试会问什么 / 下一步怎么走」。只有 3 步：贴 JD → 给简历 → 自动生成并打开一份 HTML Offer Strategy Report。是整个求职链路的入口，向下衔接 Resume Skill 和 BQ Skill。关键词：job description, JD, 职位描述, 匹配度, ATS, resume tailor, 简历定制, 招聘经理, 投不投, should I apply, 面试预测, offer strategy。

批量发现、整理和持续维护求职岗位清单。根据用户的简历、目标方向或种子 JD，搜索 LinkedIn 与公开网页，去重并建立尽量完整的候选池，用证据区分已核验事实和方向性推断，最后生成可搜索、可筛选的单文件 HTML job-hunt-skill 报告。用户说‘帮我批量找岗位’、‘整理职位清单’、‘使用 job-hunt-skill’、‘根据简历搜工作’、‘把这些 LinkedIn 职位做成表格’、‘持续跟踪岗位’时必须使用。只发现和分析岗位；绝不自动申请、处理登录凭据或绕过访问限制。

LinkedIn 岗位发现与匹配排序。根据用户给的一份种子 JD 和简历，提取目标岗位画像，自动生成多组 LinkedIn Jobs 搜索，采集、去重并按证据给岗位分层，最终输出可直接投递的 shortlist。用户说‘帮我找工作’、‘在 LinkedIn 搜适合我的岗位’、‘找相似职位’、‘根据简历推荐岗位’、‘job search’、‘find jobs like this’时必须使用。只搜索和推荐，不自动投递、不代替用户登录、不绕过验证码。

Offer 对比决策器。用户同时拿到两份（或多份）offer 纠结怎么选时，扮演 Senior Career Decision Advisor：结构化对比 comp / 成长 / AI 敞口 / 公司强度 / 团队风险 / 晋升速度 / 生活方式，识别 front-loaded vs long-term upside、resume value、switch-out 难度、hidden risks，最后给一条**明确、有立场、不中性**的推荐（并附「你是 X 类人选 A，你是 Y 类人选 B」的分叉判定）。三步：贴两份 offer → 补 priorities & 现状 → 自动生成并打开一份 HTML Offer Decision Report。是 offer-toolkit 里 JD Skill / Resume Skill / BQ Skill 拿到结果之后的下一环。关键词：offer compare, 选 offer, 两个 offer, offer decision, TC compare, 该去哪家, which offer, 4-year TC, RSU, sign-on, front-loaded, long-term upside, career decision。

简历生成与美化。两种入口：把已有简历(PDF/Word/文本)解析、诊断、套用模板美化；或者还没有简历时，通过 LinkedIn 导入或一问一答的对话帮你从零建出一份。输出单页打印优化的 HTML(浏览器里 Cmd+P 直接存成 PDF)，提供 4 套模板：Classic/ATS 友好、Modern 侧栏、Elegant 衬线、Tech 紧凑。关键词：resume, CV, 简历, 美化简历, 做简历, 简历模板, resume template, LinkedIn 简历, 求职简历, ATS, 改简历, polish resume。

薪资谈判教练。模拟前 FAANG 招聘官 + 薪酬策略师，把「拿到 offer → 签字」这一步拆成 6 个动作：Offer 诊断（Base / RSU / Sign-on / Bonus 每项标 LOW/MED/HIGH leverage）→ 谈判杠杆图（我的力量 / 招聘官的力量 / 弱点）→ 策略排序（Base → RSU → Sign-on → Bonus，按 context 调整）→ 脚本生成（电话 / 邮件 / 招聘官回话模拟）→ Counter 模拟（"这是最好的了" / "预算固定" / "会考虑其他候选人" 三种硬回答）→ 最终建议（要什么 / 别推什么 / 何时停）。三步：贴 offer → 说 context（有没有竞争 offer / deadline / 级别 / 风险偏好 / 最看重什么）→ 自动生成并打开一份 HTML Negotiation Playbook。关键词：salary negotiation, offer negotiation, comp negotiation, RSU, sign-on, base salary, counter offer, competing offer, TC, total comp, 薪资谈判, offer 谈判, 谈薪, 反 offer, 加薪, 谈 package, levels.fyi, 招聘官话术, recruiter script。

Project memory workflow for init/upgrade, profile-aware setup, end session, normal/full close, file organization, document archiving, language switching, versioned update logs, rule review, status, wiki sync, and context recovery. Use for /project-butler, setup/初始化, foundation setup, profile setup, end session/收工, normal close, full close, foundation repair, organize files/整理文件, change language/切换语言, continue/接着上次, continue full context/全面回顾, review claude, sync wiki, status. Maintains project memory files. Runs version freshness check before trigger routing.

한국 정부·공공기관 지원사업(창업지원, 사업화 자금, 입주공간, R&D, 바우처, 경진대회) 전수조사 및 프로젝트 적합성 판정 스킬. K-Startup·기업마당(bizinfo)·NIPA·KOCCA·SMTECH 공고를 크롤링해 현재 작업 폴더의 프로젝트(아이템) 프로필에 맞는 사업을 "즉시 지원 가능 / 요건 충족 시 / 변형하면 가능" 3단계로 분류하고 마감일·자격요건을 원문 검증해 보고서를 만든다. 사용자가 "지원사업 찾아줘", "정부지원", "창업지원 사업", "입주공간/사업화 자금 알아봐", "공모전/경진대회 조사", "우리 아이템에 맞는 지원사업", "K-Startup/기업마당 조사" 등을 요청하면 반드시 이 스킬을 사용한다. 이전에 조사한 적이 있는 프로젝트에서 "재조사", "새로 나온 지원사업 있나", "지난번 이후 뭐 올라왔나"를 물으면 diff 모드(증분 재조사)로 이 스킬을 사용한다. 특정 사이트를 지목하지 않아도 지원사업·보조금·정부과제 탐색 의도가 보이면 트리거된다. 단, 이미 운영 중인 소상공인·가게·점포·자영업자의 지원(소상공인 지원금, 정책자금 대출, 가게 시설개선, 소상공인24, 폐업·재기 지원)은 이 스킬이 아니라 sole-search 스킬을 사용한다. 신호가 섞이면(예: ''온라인 셀러 지원금'') 어느 쪽인지 한 번 묻는다. 사용자 신분보다 요청 목적이 우선이다 — 가게 사장이라도 신규 아이템 창업지원·R&D를 찾으면 ir-search. 한국 지원사업 전용.

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AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.

AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment workflows.

Configure AWS MCP servers for documentation search and API access. Use when setting up AWS MCP, configuring AWS documentation tools, troubleshooting MCP connectivity, or when user mentions aws-mcp, awsdocs, uvx setup, or MCP server configuration. Covers both Full AWS MCP Server (with uvx + credentials) and lightweight Documentation MCP (no auth required).

AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture. Essential when user mentions AWS costs, spending, billing, budget, pricing, CloudWatch, observability, monitoring, alerting, CloudTrail, audit, or wants to optimize AWS infrastructure costs and operational efficiency.

AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.

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Opentrons Protocol API v2 for OT-2/Flex: Python protocols for pipetting, serial dilutions, PCR, plate replication; control thermocycler, heater-shaker, magnetic, temperature modules. Use pylabrobot for multi-vendor.

Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.

Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.

Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.

Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.

Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.

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Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.

DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.

Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.

Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.

Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.

Python bridge to ImageJ2/Fiji for macros, plugins (Bio-Formats, TrackMate, Analyze Particles), NumPy↔ImagePlus/ImgLib2 exchange, and ImageJ Ops. Automates Fiji headlessly from Python. Use scikit-image for pure Python without Fiji plugins; napari for visualization.

Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.