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概要と使いどころ
Load this skill when the user asks specific insurance education questions in the Indian context, such as term vs endowment vs ULIP, riders, claim settlement process, nominee concepts, or policy jargon; do not load for generic budgeting or emergency fund queries.
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。
Personal assistant for the [YOUR LAST NAME] family. Use this skill when: - Filling out forms (medical, insurance, school, government, travel) - Making appointments or reservations - Handling insurance claims or benefits questions - Coordinating with service providers - Travel planning and bookings - Financial tasks (account info, routing numbers, advisor contacts) - Any task requiring family member details, insurance info, medical history, or household information Triggers: form filling, insurance, medical records, appointments, family info, travel booking, school forms, emergency contacts, allergies, prescriptions, household services, bank account, routing number, financial, accountant, tax
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。
Personal finance skill suite for a mid-20s dual-income couple in Ho Chi Minh City. Covers budgeting, emergency fund building, investments, insurance, tax optimization, goal planning, and couple dynamics — all calibrated to Vietnam's financial ecosystem. Use when the user asks about budgeting, saving, investing, insurance, taxes, or financial goal planning — trigger phrases include "review our budget", "how should we invest", "do we have enough insurance", and "plan for a home purchase". Use even if the user only mentions one financial topic without asking for a full review.
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。
Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.
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 guide for building real-time collaboration features using WebSockets, WebRTC, CRDTs (Yjs, Automerge), and Liveblocks / Panduan ahli untuk fitur kolaborasi real-time.
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。
Creates GitHub Pull Requests with automated validation and task tracking. Use when user wants to create PR, open pull request, submit for review, or check if ready for PR. Analyzes commits, validates task completion, generates Conventional Commits title and description, suggests labels. NOTE - for merging existing PRs, use github-pr-merge instead.
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。
Parallel agent fleet dispatch with progress tracking and result aggregation. Launch N agents, monitor completion, merge results. Proven at 7-agent scale.
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 household as an economic unit with income, expenses, savings, and debt. Covers the envelope method, fixed vs variable expenses, the 50-30-20 baseline, emergency reserves, the true cost of ownership, opportunity cost in household decisions, and the distinction between a budget and a spending plan. Use when building a household budget, diagnosing why a household is always over, planning a major purchase, or teaching financial literacy at the household level.
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。
Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.
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 code review covering style, readability, and security concerns with actionable feedback. Use when reviewing pull requests or merge requests to identify issues and suggest improvements.
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。
ALIFE 2025 Proceedings - 337 pages, 80+ papers on artificial life, evolution, emergence, cellular automata, neural networks, agent-based models. Full Mathpix extraction with equations and figures.
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。
Integrate multiple source findings into an attributed synthesis or comparison. Use for combine these summaries, synthesize results, merge findings or multi-source analysis. Preserve claim-level provenance, coverage, qualifiers, conflicts and uncertainty rather than repeatedly compressing narrative summaries.
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。
Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.
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。
Reviews Python code across type safety, error handling, security, performance, modern patterns, design clarity, typed-boundary compliance, test quality, and documentation. Use when performing code review, PR review, pre-merge quality checks, or assessing Python for security vulnerabilities, bare except clauses, Any usage outside boundaries, or missing input validation at system boundaries.
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 pattern for converting rule-following, checklist, or rubric skills into a fan-out map-reduce ensemble of parallel rigid sub-agents with corroboration-weighted merge; worker model tier and diversity are knobs matched to inference load and stakes, not fixed values. Apply when creating or refactoring a skill or agent that applies 10+ independent criteria in a single pass. Triggers on: 'review against a checklist', 'fan out', 'map reduce review', 'ensemble', 'split the rules', 'apply rubric', or any large ruleset being applied by one agent in one pass. NOT for tight single-pass transforms or rulesets under 5 criteria.
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 designing or validating GitLab CI/CD and automatic release lifecycles, inspecting pipeline and job state read-only, composing non-interactive merge-request commands, or setting up protected refs and release credentials. Also covers reusable components and inputs, GitLab Functions, gitlab-ci-local, glab command composition, and GitLab Flavored Markdown. For pipeline execution or retry requests, provide inspection and command drafting while leaving execution to the operator.
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。
Executes a groomed milestone with parallel kage-bunshin sessions in isolated worktrees. Use when a milestone has been groomed and /groom-milestone has produced a dispatch plan. Reads the dispatch plan, creates an integration branch, spawns one kage-bunshin (independent claude -p process) per wave item in its own worktree — each session is a full orchestrator with the Agent tool. Sequentially merges worktree branches, relays wave discoveries to subsequent waves, then lands the integration branch to main. Takes a milestone number as argument.
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。
Synthesize multiple independent structured review verdicts into one lossless deduplicated punch list. Use when findings from several review perspectives must be merged without inventing, dropping, or changing source verdicts.
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 modifying, converting, or transforming structured data with dasel v3 — in-place mutations, format conversion, batch operations, array manipulation, object construction, and merge patterns across JSON, YAML, TOML, XML, CSV, HCL, INI
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。
Spawn full peer Codex or Claude sessions in their own panes and git worktrees — real sessions, not subagents — briefed by the lead and merged back. Not for bounded consults a subagent covers. Use when work must outlive or run beside this session, needs its own worktree, or must stay user-steerable.
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。
Spawns full Claude Code peer sessions in separate terminal panes and git worktrees, real sessions rather than subagents, each with a directed task and contract brief, monitored and merged by the spawning lead. Detects the environment and uses herdr first, then tmux, then a native claude background agent. Use when work must persist or run beside the current session, needs a separate worktree or permission settings, should remain user-steerable in a visible pane, or when the user asks to spawn, hand off, or parallelize full sessions. Also spawns Codex peers into those panes. Excludes bounded consults and subagent-scale 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。
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.
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。
[AUTO-INVOKE] MUST be invoked BEFORE deploying DeFi contracts (DEX, lending, staking, LP, token). Covers anti-whale, anti-MEV, flash loan protection, launch checklists, and emergency response. Trigger: any deployment or security review of DeFi-related contracts.
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。
Publish the octop-memory Python package: cut a release branch from develop, bump version, update CHANGELOG / README, open a PR to main; after merge, Actions tag on main (PyPI + GitHub Release) and sync main into develop. Use when the user asks to publish, release, bump version, cut a release, or run /publish.
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。