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概要と使いどころ
Pitfalls, examples, and edge cases for FETIH Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
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 it is time to make the backend follow the Lean model — regenerate Kotlin from Lean, implement the generated interfaces (UseCase, QueryService, Repository, Entity), wire every generated contract test, make the build green, then run the design and SQL reviews. Backend comes after the human confirmed the screens.
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。
Legal matter-strategy App-in-Skill (Busabase-backed) desk for new-case strategy, issue trees, evidence checklists, risk analysis, pleading outlines, and approval-gated drafting packs grounded in internal precedents. Use when the user invokes $kelly-legal-matter-strategy, mentions 案件策略, 办案辅助, 证据清单, 争议焦点, 文书生成辅助, litigation strategy, arbitration strategy, pleading outline, or wants a Busabase-backed desk where responsible lawyers review agent-prepared strategy before use.
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。
Build, analyze, repair, and audit finance three-statement models and operating forecasts. Use when the user invokes $kelly-finance, asks for 财务三表, 三表模型, income statement, balance sheet, cash flow statement, forecast model, budget model, financial projections, FP&A model, SaaS/unit economics forecast, fundraising model, board finance pack, investor model, model checks, balance-sheet balancing, cash runway, working capital, capex/depreciation schedules, debt schedules, scenario/sensitivity cases, or Excel/Sheets-ready finance outputs.
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 board (Busabase App-in-Skill) that runs a fixed suite of mock test cases against a baseline vs candidate agent version and surfaces rubric-scored regressions before a release. Use when the user invokes $kelly-agent-eval or /kelly-agent-eval, wants to review agent-version regressions, compare baseline vs candidate quality, triage a release, or record a release approve/block decision. Deterministic mock rubric scores only — not a real LLM-judge call, and it never deploys anything.
日本語の概要は準備中です。原文の説明を表示しています。
Validates compliance, security, and GRC terminology in marketing copy. Enforces accurate claims, prevents common mistakes (e.g., calling SOC 2 a "certification"), and applies risk-first narrative framing for B2B SaaS audiences. Use when writing or reviewing any marketing content that references compliance frameworks, security standards, regulatory requirements, or audit processes. Also use when creating ads, landing pages, emails, case studies, or sales collateral for GRC/cybersecurity B2B SaaS products.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze existing code and write comprehensive unit and integration tests for it. Detects the test framework, identifies untested code paths, and generates tests with proper mocking, edge cases, and assertions. Use when the user asks to add tests, improve coverage, or test a specific module.
日本語の概要は準備中です。原文の説明を表示しています。
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).
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。
How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".
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。
End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.
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。
Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.
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。
Install and configure ToolUniverse for any use case — MCP server (chat-based), CLI (command line with 9 subcommands), or Python SDK (Coding API with 3 calling patterns). Covers uv/uvx setup, MCP configuration for 12+ AI clients (Cursor, Claude Desktop, Windsurf, VS Code, Codex, Gemini CLI, Trae, Cline, etc.), full CLI reference (tu list/grep/find/info/run/test/status/build/serve), Coding API quickstart, agentic tools, code executor, API key walkthrough, skill installation, and upgrading. Use when user asks how to set up ToolUniverse, which access mode to use (MCP vs CLI vs SDK), configuring MCP servers, using the CLI, troubleshooting installation, upgrading, or mentions installing ToolUniverse or setting up scientific tools. Also triggers for "how do I use ToolUniverse", "what's the best way to access tools", "command line", "tu command", "coding API", "tu build".
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。
End-to-end GWAS workflow from VCF to association results. Covers PLINK QC, population structure correction, and association testing for case-control or quantitative traits. Use when running genome-wide association studies.
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 changes adversarially using parallel review layers (Blind Hunter, Edge Case Hunter, Acceptance Auditor) with structured triage into actionable categories. Use when the user says "run code review" or "review this code"
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。
Forensic case investigation with evidence-graded findings, calibrated to the input. Use when the user asks to investigate a bug, trace what caused an incident, walk through unfamiliar code, or build a mental model of a code area before working on 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。
Build demos for AI product showcases. TRIGGERS - Use when user needs help with ai-demo-builder related tasks.
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。
Extract case numbers, parties, filing dates, court details, and relief sought from court filing documents and legal pleadings.
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, excruciating code review. Use anytime to analyze code for correctness, edge cases, security, performance, and design issues. Not tied to baseline—this is pure code 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。
依存関係更新のローカル差分やPRをレビューする。人間・エージェントによる更新、Dependabot・Renovateの更新PRを対象に、upstreamの変更と利用箇所を照合し、根拠付きの判定を返す。
GitHub Pull Request を作成するときに使う。PRの作成、差分からのタイトル・本文ドラフト、既存PRテンプレートの適用を依頼された場合に適用する。
Use CodeRabbit CLI to receive external code review feedback and address identified issues. Invoke this skill when the user requests CodeRabbit review, or proactively after implementing significant code changes to get feedback and improve code 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。
Gitの変更をステージしてコミットする。「コミットして」「コミットしたい」「変更をコミット」といったリクエストで使用。差分を分析し、whatとwhyを含む適切なコミットメッセージを自動生成する。
現在の変更内容を確認し、適切なブランチ名を提案してブランチを作成するときに使う。ユーザーが「ブランチ作って」「作業用ブランチ切りたい」「変更に合わせてブランチ名を決めて」と言ったとき、または新規作業を始める前にブランチを作りたいときに適用する。git status / diff を確認し、プレフィックス規則（feat/fix/release/epic）に従ってブランチ名を提案し、ユーザー確認後に git checkout -b で作成する。
Prepare the internal case for hosting an Otameshi Tenshoku trial: total what it costs the company with candidate pay, staff hours and platform or incidental fees kept apart, compare it with the budget, state what remains if no hire follows, and lay out the risks, stop conditions, decision owners and deadlines. Use when a hiring manager needs approval or budget from a decision maker, a recruiter has to explain the plan to the team that will host, or a small-business owner wants to see the full cost including their own time before posting; do not use to design the work (trial-work-design), to write the listing (trial-listing-draft), to predict whether a hire will follow, to state platform fees, agency fees or market rates from memory, or to submit the request or post the listing on the user's behalf.
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。