Show when you last had a 1:1 with each direct report / peer, sorted by recency. Flags people who haven't had one in N weeks. Powered by the OneOnOnes view on Meetings.base.
日本語の概要は準備中です。原文の説明を表示しています。
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概要と使いどころ
Show when you last had a 1:1 with each direct report / peer, sorted by recency. Flags people who haven't had one in N weeks. Powered by the OneOnOnes view on Meetings.base.
日本語の概要は準備中です。原文の説明を表示しています。
Backlink profile audit via the DataForSEO Backlinks API. Returns total backlinks, referring domains, dofollow ratio, top anchors with over-optimization flags, top referring domains by authority, and a toxicity assessment with an Authority Score (0-100).
日本語の概要は準備中です。原文の説明を表示しています。
Flagship intra-paper self-consistency forensics: does the paper contradict ITSELF across abstract/intro/tables/body/appendix, and does the method DESCRIBED match the method EVALUATED? Needs no external ground truth — works PDF-only (L0). Runs a deterministic arithmetic pass + a fresh cross-model semantic pass, every finding span-anchored to the evidence ledger (claims.json), reviewer≠adjudicator. Emits consistency-audit.findings.json; NEVER computes the verdict. Triggers: "consistency audit", "check the paper against itself", "self-consistency", "内部自洽".
日本語の概要は準備中です。原文の説明を表示しています。
Checkable-ish surface presentation signals a reviewer notices first — duplicate/near-identical tables, leftover pipeline/template strings, too-few or LLM-looking figures, and page-padding. AUXILIARY ONLY and weak by design: a deterministic pass (tools/check_presentation.py — dup-table + pipeline-artifact) plus a fresh cross-model GROSS-cases-only semantic pass (thin-float, LLM-figure, page-padding), every above-info finding span-anchored to the evidence ledger (claims.json). The adjudicator CAPS everything at minor (SURFACE_ONLY_SKILLS + SURFACE_PATTERNS) — these contribute at most SOFT_FLAGS, never a HARD verdict — default false_positive_risk:high. NOTE: the pure AI writing-STYLE impressions (AI-flavor prose, defensive 'not-X-but-Y' hedging, narrative-arc, jargon-stuffing, invented codenames) MOVED to the zero-verdict-weight AIS track — for those use skills/ai-style-impressions, NOT this. Emits presentation-signals.findings.json; NEVER computes the verdict. Triggers: "presentation signals", "surface check", "duplicate tables", "排版信号".
日本語の概要は準備中です。原文の説明を表示しています。
临床诊断思维 (临床诊断思维 / 临床推理 (Clinical Diagnostic Reasoning) — 医生『怎么想病』的元学科:从症状/体征/检查到诊断结论的认知操作系统,从业者(临床医生/住院医/规培生,尤其全科/急诊/大内科等以未分化主诉为生的科室)、医学生与医学教育者、诊断安全与质量改进研究者、以及做医疗 AI 辅助诊断产品的人的视角。覆盖: (a) 第一性张力 — **直觉模式识别 (System 1: illness scripts 疾病脚本 / pattern recognition / gestalt, 『资深人一眼认出 aunt Minnie』) ⇄ 分析性推理 (System 2: hypothetico-deductive 假设演绎 / Bayesian 概率更新)** 的 dual-process 双过程理论 (Kahneman→Croskerry/Norman 谱系), 资深≠更会分析而是『脚本库更大+校准更好』; 更深层论战 — **『认知去偏可教 (Croskerry: bias awareness / cognitive forcing strategies / diagnostic timeout) ⇄ 偏倚标签是马后炮、知识结构才是主因 (Norman/Sherbino/Monteiro: debiasing 干预 transfer 证据弱, bias 是 hindsight 标签)』** — 本行最核心学术对垒; 『概率思维 (验前概率 × 似然比 → 验后概率, Pauker-Kassirer test/treatment threshold 阈值模型) ⇄ 穷尽式排查 (rule-out everything / 防御性医疗 / VOMIT)』; 『临床 gestalt ⇄ 结构化临床决策规则 (Wells/PERC/HEART)』; 『诊断简约 Occam's razor ⇄ Hickam's dictum (病人可以同时得 N 个病)』; 『床旁体格检查复兴 (Verghese Stanford 25 / McGee 循证体检) ⇄ 影像检验替代床旁』; (b) 方法论正典 — illness script theory (Schmidt/Boshuizen: enabling conditions/fault/consequences 三段结构), problem representation + semantic qualifiers 问题表征与语义限定词 (Bowen NEJM 2006, 把病人翻成 one-liner), hypothetico-deductive model (Elstein 1978《Medical Problem Solving》: 早期假设生成+定向检验), Bayesian 工具箱 (sensitivity/specificity/LR/Fagan nomogram/SnNout-SpPin), threshold model (Pauker-Kassirer NEJM 1975/1980), 认知偏倚分类学 (anchoring/premature closure/availability/confirmation/base-rate neglect/search satisficing/diagnostic momentum — Croskerry 偏倚清单), 去偏与元认知策略 (diagnostic timeout/cognitive forcing/Ely checklist/calibration), schema-based reasoning (Clinical Problem Solvers schemas: 按 pivot 症状走分支), reflective practice 结构化反思 (Mamede/Schmidt), 诊断错误科学 (NAM 2015 报告定义 / Newman-Toker Big Three / Hardeep Singh e-triggers / SAFER Dx 框架); (c) 行业结构与角色 — 医学生→实习/住院医 (晨会 morning report/查房被 pimping/汇报训练)→主治 attending→master clinician (NEJM CPC discussant/晨会大师如 Dhaliwal); 配套生态: 医学教育者 (clinical reasoning curriculum + 评估: script concordance test/key features exam/OSCE), 诊断安全研究者 (SIDM/AHRQ), 诊断辅助与 CDS 工具开发者 (DDx generator/AI); (d) 核心工作流 — 数据采集 (病史为王 + 循证体检 + 针对性检验影像) → 问题表征 (one-liner + semantic qualifiers) → 鉴别诊断生成 (schema / 解剖定位法 / VINDICATE-M 病因筛, 按『常见可能 × 致命不能漏 can't-miss』双轴排序) → 假设定向检验 (按 LR 选检查 / 阈值决策) → working diagnosis + 安全网 (red flags 交代 / test of time / test of treatment) → 反馈校准 (follow-up / M&M / diagnostic timeout); 教学工作流: 晨会渐进披露汇报 / CPC / SNAPPS / one-minute preceptor / virtual morning report; AI 增强工作流 (2023-2026: LLM 鉴别诊断头脑风暴 / OpenEvidence 检索 / ambient scribe 释放认知带宽 + 自动化偏倚 guardrails); (e) 产出物 — one-liner, problem list, prioritized DDx, assessment & plan (按问题分层), 晨会/CPC 汇报, M&M 复盘, 诊断不确定性沟通 (『最可能是 X, 但出现 Y 红旗立刻回来』); (f) 教育与评估 — script concordance test / key feature exam / EPA / 里程碑; 中国语境: 人卫《诊断学》教材 / 执业医师考试 / 规培结业临床思维考核; (g) 争议/批判 — debiasing 之争 (Croskerry vs Norman-Sherbino『knowledge is the cure』, 去偏 RCT 效果弱 / bias 标签不可证伪), 诊断错误率数字之争 (『10-15%』经典估计 / Newman-Toker 79.5 万美国年严重伤害外推方法被质疑 / 尸检符合率), dual-process 二分被批过度简化 (连续谱 / 难以实证分离), 决策规则 vs gestalt (资深 gestalt 常不输 Wells 类规则 / 算法厌恶), vignette 研究外推性 + context/case specificity (推理高度内容绑定不可通用迁移 [Norman/Eva] — 对『教推理通用课』产业的根本批判), AI 辅助诊断 2023-2026 (GPT-4 在 NEJM CPC/vignette 追平或超医生 [Kanjee 2023 JAMA / Goh 2024 JAMA Netw Open『AI alone > physician+AI』悖论] / Google AMIE / OpenEvidence 爆发 vs 自动化偏倚 / 去技能化 / 真实环境验证缺失), 防御性医疗与过度检查 (incidentaloma 瀑布 / VOMIT), 体检衰亡之争, pimping 教学法争议; (h) 流派/思想谱系 — 决策科学/Bayesian 派 (Ledley-Lusted 1959 → Elstein/Kassirer/Sox/Pauker/Eddy → Brush) vs 认知心理/双过程派 (Kahneman-Tversky → Croskerry/Graber) vs 教育认知派 (Schmidt/Boshuizen illness scripts → Norman/Eva/Mamede/Durning 情境认知) vs 诊断安全/系统派 (NAM 2015 / Newman-Toker / Hardeep Singh / Graber-SIDM: 错误=系统×认知共因) vs 床旁临床派 (Osler 传统 → Tierney aphorisms / Verghese / McGee / Dhaliwal / Saint: 病史体检为王 + 刻意练习) vs AI/计算派 (INTERNIST-1/DXplain 专家系统 → Isabel → LLM 世代 Rodman/Topol)。不含: 具体专科治疗方案与手术决策深度 (足踝外科/种植牙等另有 skill), 中医辨证 (另有 skill), EBM 文献批判性评价方法学 (相关但本 skill 聚焦诊断推理本身), 护理诊断体系 (NANDA), 影像/病理知觉型读片训练 (相邻但独立), 精神科 DSM 结构化访谈细节, 医患沟通技巧全集 (只覆盖诊断不确定性沟通), 患者自查/自我诊断指
日本語の概要は準備中です。原文の説明を表示しています。
Routes, triages, flags and rates a piece of text with a probability for every option: which department or queue a ticket goes to, which intent a message expresses, whether a yes/no condition holds (is this tool call grounded, should the agent ask first, is the customer angry), and how severe or urgent it is on a rubric, each with a calibrated confidence. Runs Claude Haiku in the request and response shapes of OpenAI's Decisions API, for when no purpose-built decision model is at hand. Use for "route this ticket", "triage these", "classify with a confidence", "give me a probability that", "rate the severity", "score against this rubric", guardrail checks before a tool call, or "a Decisions API without OpenAI".
日本語の概要は準備中です。原文の説明を表示しています。
arkcli 共享执行协议:首次配置入口、业务命令执行前的认证闸门、命令路由与选择顺序、输出/安全/二次确认规则。深度细节(身份解析、AK-SK 边界、API Key 恢复、实名闸门、profile 默认与漂移、临时数据面执行上下文、版本检查与显式升级、全局 flags、故障分流)按需在 references/ 加载。当用户第一次使用 arkcli、遇到未登录/鉴权失败、询问版本是否最新或要求升级 arkcli、需要判断该走产品命令还是 raw api、或任何 arkcli-* skill 需要公共上下文时触发。
日本語の概要は準備中です。原文の説明を表示しています。
Use when an agent or harness needs to drive the full tiny.place network from the command line — register an @handle identity, search the open directory, read/post to channels and broadcasts, send and acknowledge encrypted messages, manage inbox items, fund and win bounties (contest-style work), read reputation, fetch pricing, manage approved signers, settle/verify x402 payments and subscriptions, and read the ledger. Wraps the flagship TypeScript SDK CLI (`tinyplace`), which carries the complete API surface.
日本語の概要は準備中です。原文の説明を表示しています。
Translate existing blog posts into one or more target languages with SEO-optimized localization. Produces native-quality translations that preserve markdown structure, frontmatter, schema JSON-LD, image and chart embeds, and citation capsules. Localizes keywords, meta tags, numbers, dates, currencies, and quote styles per locale. Flags machine-translation artifacts for review. Run BEFORE blog-localize: this handles language conversion; localize handles cultural adaptation after translation completes. Use when user says "translate blog", "blog translate", "uebersetzen", "traduire", "traducir", "translate post", "blog auf Deutsch", "blog en espanol".
日本語の概要は準備中です。原文の説明を表示しています。
Verify statistics and claims in blog posts by fetching cited source URLs and checking if the claimed data actually appears on the page. Extracts all statistical claims (numbers, percentages, named sources), fetches each cited URL via WebFetch, and scores match confidence (exact match 1.0, paraphrase 0.7-0.9, not found 0.0). Flags uncited claims as UNVERIFIED. Use when user says "fact check", "verify statistics", "check sources", "validate claims", "factcheck", "source verification".
日本語の概要は準備中です。原文の説明を表示しています。
Audit a directory of multilingual blog content for completeness, consistency, hreflang correctness, meta-tag parity, and freshness. Builds a translation coverage matrix, flags stale translations, validates hreflang and schema, and emits a prioritized report with runnable fix commands. Use when user says "locale audit", "blog locale-audit", "check translations", "multilingual audit", "translation check", "hreflang check", "Uebersetzungen pruefen".
日本語の概要は準備中です。原文の説明を表示しています。
Generate unit tests for untested branches and edge cases. Use when coverage is low, CI flags gaps, or a release needs hardening. Not for integration/E2E suites, framework migrations, or fixing production bugs.
日本語の概要は準備中です。原文の説明を表示しています。
Upload one or many videos to YouTube. Use when the user wants to "上传到 YouTube", "发 YouTube", "批量上传", "upload to YouTube", "post videos to YouTube", or to publish a finished `final/` directory of MP4s. Reads per-video metadata (title / description / tags) from a sibling `UPLOAD_META.md` file when present (the user's standard markdown format), or from command-line flags. Survives behind a SOCKS/HTTP proxy by using `requests` directly for the resumable upload (the stock `google-api-python-client` MediaFileUpload stalls under this user's proxy setup).
日本語の概要は準備中です。原文の説明を表示しています。
Suede Labs skill that prepares a song for sync pitching: scene and emotional angles, a supervisor-facing one-sheet, an exists/missing/unknown asset checklist covering master, instrumental, clean, and stems, screened lyric flags, mood tags, and the open clearance questions kept in their own labeled pile. Use when a track is headed for film, TV, ads, games, trailers, or creator campaigns; when a music supervisor, sync agent, or brand asks what a song is and whether it can be used; or when someone asks for a one-sheet, pitch email, or sync package for a release. Prepares review-ready materials only: never claims clearance, promises placement, or sends outreach. NOT FOR: finding and organizing the rights gaps behind an open clearance question (use suede-rights-audit); building the rights transfer package (use suede-rights-passport); linting the release folder itself (use suede-release-linter); a full rollout campaign (use suede-campaign-in-a-box).
日本語の概要は準備中です。原文の説明を表示しています。
Monitor open GitHub PRs across all repos in ~/Dev. Checks PR age, flags stale PRs, and optionally alerts via Aivena/Telegram. Uses `gh pr list` for accurate GitHub data. **TRIGGERS - Use this skill when:** - User asks about open PRs, pull requests, or PR status - User says "check PRs", "any stale PRs?", "PR review status" - Cron job `pr-monitor` fires - User asks "what needs review?" or "what's waiting for merge?" - User wants a PR dashboard or PR age report
日本語の概要は準備中です。原文の説明を表示しています。
Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.
日本語の概要は準備中です。原文の説明を表示しています。
Spec CLI surface area before implementation - command tree, flags, exit codes, I/O contract. Use when designing or planning a CLI interface.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare a meeting-ready alternative-investments brief for a client, household, IRA, trust, or named account before a review — pulls the alts book from iCapital or Addepar (positions, NAV vs. commitment, called and unfunded amounts, contributions/distributions/subscriptions/redemptions, lockups, and the platform's own IRR/TVPI/DPI/RVPI) and sits it beside the liquid book and IPS target from the connected portfolio system (Orion Connect, Addepar, or Envestnet), then flags cash needed for capital calls, alts % vs. target, concentration, unfunded concentration, and stale as-of data. Cites the source platform on every figure and keeps disclosures on the page. Does not recommend funds, forecast NAVs/IRRs, or simulate allocation changes. Triggers on "alts brief", "/alts-brief", "prep the [client]'s alts review", "what's going on with [client]'s PE/private investments", "how's [client] doing on their alts", or any request to review a household's alternative investment positions before a meeting.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare a meeting-ready estate and tax brief for a household — opens with what was discussed and actioned since the last meeting (Wealthbox/Redtail/Salesforce), pulls the estate plan from Wealth.com (structure, key documents and whether each is signed, balance sheet) plus the prior-year tax look-back and that year's tax constants, and cross-checks it against how accounts are titled and beneficiaried at the custodian and portfolio platforms (Schwab, Orion/Addepar, iCapital) — surfacing unfunded trusts, titling mismatches, and beneficiary gaps as a prioritized flags list. The only write is creating advisor-approved follow-up tasks in the CRM (optional, approved first). Never drafts trust language, gives legal advice, retitles an account, or changes a beneficiary. Triggers on "estate brief", "estate meeting brief", "estate and tax brief", "estate plan review for [client]", "check [client]'s trust funding", "are [client]'s beneficiaries up to date", or "does [client]'s estate plan match their accounts".
日本語の概要は準備中です。原文の説明を表示しています。
Prepares and controls software releases with risk classification, CI quality gates, artifact provenance, feature flags, staged rollout, measurable promotion and rollback thresholds, post-deploy verification, and cleanup. Use for deployment pipelines, launch readiness, canaries, production rollout, release automation, or rollback planning. Not for merely merging a completed development branch or proving one local change.
日本語の概要は準備中です。原文の説明を表示しています。
Deep research on a topic — use when investigating unfamiliar domains, answering complex questions requiring multiple sources, or when an evaluation flags something for deeper analysis
日本語の概要は準備中です。原文の説明を表示しています。
Expert skill for building CLI applications with Cobra and Viper, authored by spf13 — the original creator of both libraries. Covers command-first architecture, decoupled business logic, configuration management, environment variable binding, context-aware commands, and in-memory CLI testing. Use whenever a Go CLI or command-line tool is being built, reviewed, or refactored — any mention of commands, subcommands, flags, or CLI configuration, even if Cobra or Viper aren't named yet (recommend them).
日本語の概要は準備中です。原文の説明を表示しています。
Analyze a feature or sprint for scope creep by comparing current scope against the original plan. Flags additions, quantifies bloat, and recommends cuts. Use when user says 'any scope creep', 'scope review', 'are we staying in scope'.
日本語の概要は準備中です。原文の説明を表示しています。
Guided, section-by-section authoring of the master architecture document for the game. Reads all GDDs, the systems index, existing ADRs, and the engine reference library to produce a complete architecture blueprint before any code is written. Engine-version-aware: flags knowledge gaps and validates decisions against the pinned engine version.
日本語の概要は準備中です。原文の説明を表示しています。