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概要と使いどころ
Systematic methodology for achieving 100% backward dataflow reachability in a new language. Create gauntlet fixture, write trace, diagnose gaps, fix analyzer/algorithm, iterate to 100%. Language-agnostic process. Use when: (1) adding a new language to Grafema, (2) auditing dataflow coverage for existing language, (3) user says "/dataflow-gauntlet".
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。
最强经理之争——把四位风格迥异的中国权益基金经理（易方达郑希、易方达张坤、易方达武阳、财通金梓才）放进同一个擂台， 用他们各自的全部公开观点原文 + 全部在管基金真实数据（季度持仓/净值/回撤/规模），做可溯源的对比与应用。能做： (1) 经理之争擂台——同口径比收益/最大回撤/回撤恢复/波动/Calmar/换手/规模，给"分场景谁更强、各自长短板"； (2) 行业/个股透视——给一个行业或股票，看四人谁持有/谁加减/谁更早布局，再用各自框架给"四个镜头"的综合看法； (3) 持仓/调仓追踪——逐季还原谁加仓、减仓、新进、清仓（季度颗粒度，公开数据能到的最细）； (4) 多视角参考——用四人的方法和真实操作，多角度帮你理解一只基金/一个行业、校准自己的判断（只呈现不同视角，不替你做决定、不构成建议）； (5) 溯源问答/方法讲解/风格化点评——引用某位经理本人原话答其观点，讲其投资框架，或用其口吻点评； (6) 言行对照——用真实持仓核对某位经理"说的"和"买的"。 (7) 进阶玩法——风格雷达+人设卡、本季调仓快报(共识/分歧)、言行一致性打分、观点时间机器(谁最早看到)、四人会诊(四种视角点评)、自定义擂台(把任意基金/经理拉进来比)。 When the user mentions 最强经理之争 / 经理对比 / 谁更强 / 回撤谁控得好 / 张坤/郑希/武阳/金梓才 / 景气轮动 vs 买入持有 / 风格雷达 / 人设 / 谁最早布局 / 言行一致 / 四人会诊 / 把某基金加进来比， asks who holds a sector or stock, wants 调仓追踪/调仓快报, a 风格化点评, words-vs-holdings, a style radar/persona, an opinion timeline, or to apply their methods to a fund/sector ——use this skill, even without the word "skill", even if the topic isn't in a corpus (fall back to that manager's method). 引用忠于原文、不杜撰；原话/推演/需核实分清；季度颗粒度非逐笔；研究学习辅助，非投资建议。
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。
Calculates CRAP (Change Risk Anti-Patterns) score for .NET methods, classes, or files. Use when the user asks to assess test quality, identify risky untested code, compute CRAP scores, or evaluate whether complex methods have sufficient test coverage. Requires code coverage data (Cobertura XML) and cyclomatic complexity analysis. DO NOT USE FOR: writing tests, general test execution unrelated to coverage/CRAP analysis, or general code coverage reporting without CRAP context.
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。
Structured OSINT collection methodology. Planning, collection techniques, search operators, and documentation. Loaded by the osint-researcher agent.
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。
Organize receipt images and subscription invoice/receipt PDFs for Japanese sole-proprietor or side-business bookkeeping: extract evidence, rename files, and update monthly memos. Use for 領収書, レシート, receipt, OCR, リネーム, 確定申告, 勘定科目, 経費, subscription receipts, or payment-method change history. Not for company/D365 travel expense claims.
"Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.".
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 for hands-on repository work when a quiet, risk-scaled engineering method can improve design, execution strategy, implementation, material incorporation, debugging, review, delegation, workspace lifecycle, or verification. Keep clear local changes direct, preserve the complete requested outcome, and add method only where risk or uncertainty justifies it. Do not use for general technical explanations, simple file lookup, casual discussion, or non-engineering writing.
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。
Reconnaissance and methodology playbook. Use when mapping assets, discovering endpoints, fingerprinting technology, and building a structured testing plan for a new target.
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。
Investigation flow (Glob -> Grep -> Read), evidence-based research with file:line references, structured output format for AI consumption. Use for pattern discovery, implementation research, and codebase investigation.
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。
Consult this skill BEFORE proposing a fix to any CLI gap or schema gap that touches evidence, trust magnitude, evidence types, evidence grades, the TM formula, or star-level promotion gates. Also read it whenever an issue mentions citations, stars, commits, contributors, views, reviewers, percentile, arxiv, github-stars-own, repo-own, social-signal, benchmark-result, peer-review, self-attestation, fusion-recipe, verifier-attestation, or proxy-containment — those are the 10 canonical evidence types and the raw inputs that feed their formulas. Also read it when asked whether a skill qualifies for a given star level (0★–6★): the Star Bar and promotion gates live in `META.md`, not in the TM methodology page. Reading both prevents proposing a calibration that satisfies the TM grade threshold but violates a Star Bar installability or link requirement.
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。
Post-mortem analysis of a PhysicsIntern workspace run (Claude Code, Pi, Codex, or OpenCode host). Reconstructs the trajectory from the session record — JSONL file(s) for Claude/Pi/Codex, the SQLite store for OpenCode — audits methodology adherence against the workspace's own CLAUDE.md / AGENTS.md and skill/agent prompts, checks commit discipline and flag dispositions, and assesses substantive quality. Produces a thorough evidence-anchored markdown report. Use after a workspace has been worked on to identify what went well, where the methodology slipped, and what prompts to improve.
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 a task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search, choosing or building a genomics pipeline (aligner, caller, normalization method) for a specific data-generating process, or judging whether a computational-biology analysis's statistical design actually supports the biological claim being made. Distinct from a bioinformatics-technician (executes and QC-gates an established pipeline someone else designed) and a data-scientist (general product/experimentation judgment, not genomics-specific statistics) — this role owns which method and statistical model a genomics question requires, and why.
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。
Design a research study. Use when the user says "study design", "design my research", "what research method should I use", "how do I test this hypothesis", "research plan", "methodology section", "experimental design", "survey design", "qualitative study design", "how to structure my study", "sample size", "data collection plan", or needs to plan how to test a research question or hypothesis - even if they don't explicitly say "study design".
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。
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.
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。
Eliciting and documenting a sales process before it is built in Salesforce: stage sequencing, entry/exit criteria per stage, win/loss analysis requirements, and stage transition rules. Trigger keywords: sales process design, stage discovery, entry criteria, exit criteria, stage gate, win/loss categorisation, sales methodology, pipeline stages, opportunity stage mapping, stage ladder, probability ladder, forecast category mapping, required fields per stage, stage swim lane, discovery workshop, handoff brief. NOT for configuring OpportunityStage, Sales Processes or Path in Setup — use admin/opportunity-management. NOT for cross-functional swim-lane maps — use admin/process-flow-as-is-to-be.
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。
Configure and troubleshoot Salesforce Billing revenue recognition rules, schedules, and GL transaction generation under ASC 606. Triggers: 'revenue schedule not generated after order activation', 'blng__RevenueSchedule__c missing', 'blng__RevenueRecognitionRule__c on a product', 'Finance Periods not set up', 'performance obligation allocation for bundled products', 'distribution method', 'ASC 606 in Salesforce Billing'. NOT for billing schedules, invoice plans and invoice runs - use admin/billing-schedule-setup. NOT for invoice-to-payment mismatches at month-end close - use data/billing-data-reconciliation.
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 this skill when prioritizing a Salesforce backlog with the MoSCoW method (Must / Should / Could / Won't) plus an effort + value lens, deciding what fits in the next sprint or release, and using WSJF as a tie-break when MoSCoW alone produces ties. Trigger keywords: MoSCoW prioritization, must should could won't, WSJF salesforce, prioritize salesforce stories, sprint commit, backlog grooming. NOT for story writing or splitting (use admin/user-story-writing-for-salesforce). NOT for release train planning (use agents/release-train-planner). NOT for sizing/estimation method debates — this skill assumes a sizing convention is already in place.
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 in optimization methods covering linear programming, convex optimization, gradient methods, and constrained optimization
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。
The operation-generator shape for Skmtc: one definition per (path, method), for any output family — client hooks, SDK methods, forms, route stubs, docs. Output varies wildly; the DECOMPOSITION of the operation and the peer-consumption rules do not. Core teaching: every schema that appears in your output is a reference to a model generator's definition, obtained through the insert machinery — never rendered text. Use when authoring or editing a generator whose subject is operations ("write a gen for API clients/hooks/routes", "generate an SDK/form per endpoint"). Covers the projection shape only — accumulators (many operations → one file) are out of scope. Load ALONGSIDE skmtc-generator and the emitted language's skill.
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。
Look up Microsoft API references, find working code samples, and verify SDK code is correct. Use when working with Azure SDKs, .NET libraries, or Microsoft APIs—to find the right method, check parameters, get working examples, or troubleshoot errors. Catches hallucinated methods, wrong signatures, and deprecated patterns by querying official docs.
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 for ab methodology recipes when optimizing a Coworld player.
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 for reusable ab methodology recipes in software and coding-agent work.
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 for Crewrift-specific ab methodology recipes when optimizing a player.
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 for ab methodology recipes in scripted Coworld policy optimization.
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。