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Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.

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 the user wants to build a financial model — 3-statement model, DCF valuation, LBO, SaaS unit economics, sensitivity / scenario analysis, debt schedule, or fundraising projections — in Excel. Trigger on: 'financial model', '3-statement model', 'P&L + BS + CF', 'DCF', 'WACC', 'NPV', 'terminal value', 'LBO', 'debt schedule', 'cash sweep', 'MOIC', 'IRR / XIRR', 'sensitivity table', 'scenario analysis', 'ARR model', 'unit economics', 'CAC / LTV', 'cap table forecast'. Output is a single formula-driven .xlsx. This skill is a scene layer on top of officecli-xlsx — it inherits every xlsx v2 rule (4-color code, visual floor, number formats, cache-drift, Known Issues, Delivery Gate minimum cycle). DO NOT invoke for a simple budget tracker, CSV dump, or operational KPI sheet — route those to officecli-xlsx 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。

Review UI designs and implementations for accessibility, consistency, usability, and visual quality. Use when asked to review a design, audit accessibility, check UI consistency, compare implementation against mockups, or evaluate a user 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。

Find new skills on GitHub or check a specific skill before installing. Use when the user wants to search for skills, evaluate a skill URL, check overlap and security risk before installing, or compare a local skill against alternatives. Trigger with '/janitor-discover'.

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 calculating control system performance metrics such as rise time, overshoot percentage, steady-state error, or settling time for evaluating simulation results.

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。

Infrastructure and development cost estimation for technical projects. Use when planning budgets, evaluating build vs buy decisions, or projecting TCO for architecture choices.

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。

Review code for quality, correctness, and fit. Use when the user wants judgment on code that already exists — their own changes, a teammate's patch, a PR, branch, commit, diff, staged changes, or one or more files to look over. Activate on requests like review, look over, sanity check, critique, code review, or 'is this good?' The key signal is that the user wants evaluation of existing code and its tradeoffs, not implementation, debugging, or explanation. This skill works independently, but when plans, specs, task artifacts, or prior discussion exist, use them to understand why the code exists before judging it.

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 the user wants a written, reviewable plan or spec produced before coding starts. Triggers on: mapping out changes without implementing, thinking through risks of upgrades or migrations, evaluating approaches before committing to one, writing specs for team review, phasing work into stages, or any request that explicitly defers coding ('don't implement yet', 'before we build'). The distinguishing signal is that the user wants a plan artifact — not implementation, not a conversational answer. MUST activates inside Claude's native plan mode to have a better planning behavior.

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。

Brainstorms and debates approaches, then drives toward an actionable decision. Use whenever someone needs a thinking partner for a decision they're facing: 'discuss', 'debate', 'brainstorm', 'weigh options', 'tradeoffs', 'should I do X or Y', 'help me decide', 'I'm torn between', 'sanity check my thinking', or 'what do you think about'. The user must be asking for help reasoning through a choice — not asking to build, fix, evaluate, plan, or modify something (even if the topic involves this skill itself). Picks the right decision lens, surfaces tradeoffs and blind spots, pushes back when reasoning is genuinely weak, and never implements.

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 writing or changing PyTorch code — model, Dataset/DataLoader, training and evaluation loops, determinism, mixed precision, torch.compile, checkpoints with safetensors, Hugging Face models, ONNX export, and tests that run on CPU

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。

Review test quality using best practices validation. Use when user says "lets review tests" or "I want to evaluate test quality"

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 NFR evidence for performance, security, reliability, and maintainability. Use when implementation evidence exists and the user says "audit NFR evidence", "audit NFRs", or "evaluate non-functional requirements"

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。

Growth strategy for product-led and loop-driven growth systems. Use when building growth strategy, designing growth loops, planning acquisition channels, evaluating network effects, deciding when to scale, or coordinating product, engineering, data, and marketing around growth. For SEO audits use seo-and-aeo-strategy; for page/form CRO use conversion-rate-optimization; for A/B test design use ab-test-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。

Skill to query and update a mistake notebook to memorize mistake. Use this skill when: 1. Encountered a severe problem, needs to recall for historical problem solving experience 2. After fixed a severe mistake with a clear solution valuable to be recorded 3. User requested 'Do not make this mistake again'

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。

用户在评估不同资产类别（货币/黄金/房产/股票/加密等）的长期价值，或需要理解某类资产的根本经济属性时。 语言信号:"黄金值不值得投"/"比特币有没有价值"/"房产vs股票哪个好"/"为什么持有现金会亏" 不适用于:不需要跨资产类别比较的纯个股分析。

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。

用户需要评估一个资产/投资/项目的价值，或纠结某个东西"值不值"时。适用于任何能产生（或不产生）现金流的资产评估。 语言信号："这个值多少钱"/"这只股票贵不贵"/"这个项目值不值得投"/"应该用PE还是PB估值" 不适用于：不需要估值分析的纯情感/审美决策。

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。

Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.

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。

Evaluate Cognee itself from local telemetry: recall hit rate, latency, saves, injected context, token overhead, a scorecard and grade, before vs after an update, and why a memory was or wasn't injected last turn.

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。

Evaluates neuromarketing studies and neural-data law. Use when judging EEG, fMRI, eye-tracking, or GSR ad study or vendor, or if signals are regulated neural data.

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。

Builds QA harnesses and agent evals. Use when evaluating tool, trace, red-team, regression, multi-agent, or carried-workspace coding trajectories. Not for metric design (ai-evals).

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。

Builds ML pipelines: EDA, features, leakage checks, evaluation. Use when doing data science or explaining fairness, privacy, speech, vision-language, or diffusion mechanics.

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。

AI agent architecture, graph and loop composition, protocol choice, evaluation, and observability. Use when scoping or reviewing systems before implementation.

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。

Quality review of test files and manual evidence documents. Goes beyond existence checks — evaluates assertion coverage, edge case handling, naming conventions, and evidence completeness. Produces ADEQUATE/INCOMPLETE/MISSING verdict per story. Run before QA sign-off or on demand.

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。

Read all open bugs in production/qa/bugs/, re-evaluate priority vs. severity, assign to sprints, surface systemic trends, and produce a triage report. Run at sprint start or when the bug count grows enough to need re-prioritization.

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。