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概要と使いどころ
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
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How to do feature work in this repo with git worktrees via `pnpm worktree`. Each worktree gets its own branch, its own block of app ports, its own node_modules, and by default shares the primary checkout's standard local Supabase DB for fast setup; pass `--db` for its own database (a copy of the primary inside the same Postgres) when the work needs migrations or destructive data changes. Load WHENEVER starting a feature, bugfix, refactor, experiment, or any change you'll want to run/test in isolation; whenever the user mentions worktrees, isolated/parallel dev instances, running multiple branches at once, or 'spin up a worktree'; and whenever you need the exact non-interactive `pnpm worktree` commands and flags. Enforces: one canonical branch per objective, one worktree per branch — join the existing worktree instead of creating another.
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How this repo manages API secrets and the four local-run environments (local/dev/staging/prod). They are dotenvx-ENCRYPTED in git and the keys live in Dotenv Armor. Load this WHENEVER you touch a secret, API key, token, credential, or any apps/api/.env* file; whenever the user pastes a key/token/secret to store or use; whenever choosing/switching which environment to run; and whenever adding, reading, rotating, or sharing a secret.
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4-tier progressive web scraping that auto-escalates WebFetch to curl to Interceptor to Bright Data proxy for bot detection and CAPTCHAs, with single-URL and multi-page crawl modes, output as markdown. USE WHEN Bright Data, scrape URL, web scraping, bot detection, crawl site, CAPTCHA, can't access, site blocking, extract page content, scrape whole site, spider domain, convert URL to markdown, getting blocked. NOT FOR simple public content (use WebFetch directly), social platform scraping with named actors (use Apify), or real-Chrome bot bypass with logged-in sessions and zero CDP fingerprint (use Interceptor).
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。
Watch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or local WhisperX / cloud Whisper fallback), and hands the result to the agent so it can answer questions about what's in the video. With a Gemini API key, Google's agentic video model watches the full video 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。
Prepare a local LangBot development and testing environment for an AI agent. Use when setting up WSL or Linux development, shared local URL variables, proxy variables, backend/frontend startup, Playwright MCP browser access, GitHub OAuth browser login, persisted Chrome profiles, or future Codex computer-use environment paths.
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。
Manage a stateful, run-directory-based proof project with Codex: continuation across runs, run-local source bookkeeping, manual GPT Pro handoff packages when a local attempt stalls, and an optional DeepSeek second opinion as additional evidence only. Use when the user asks for proof-run orchestration, a GPT Pro handoff, or cross-run proof continuation — use /proof-writer for ordinary proof drafting and /proof-checker for rigorous verification or submission acceptance.
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。
Run the Anti-Autoresearch integrity-forensics DETERMINISTIC slice (numeric core + rules-only reporter) against a paper via a SHA-pinned thin launcher, then convert the verdict into a typed policy gate (BLOCK/WARN/NO_NEW_BLOCKER) and an append-only obligations ledger. Codex-native limitation: upstream ships no Codex-native auditor pack, so the full nine-dimension semantic sweep requires a Claude Code session — this pack runs the honestly-scoped deterministic-only mode (it can flag, it can never say CLEAN). Use when user says "integrity forensics", "forensic audit this paper", "投稿前自查诚信".
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。
Manage a stateful, run-directory-based proof project: continuation across runs, run-local source bookkeeping, manual GPT Pro handoff packages when a local attempt stalls, and an optional DeepSeek second opinion as additional evidence only. Use when the user asks for proof-run orchestration, a GPT Pro handoff, or cross-run proof continuation — use /proof-writer for ordinary proof drafting and /proof-checker for rigorous verification or submission acceptance.
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。
Run the Anti-Autoresearch integrity-forensics sweep (span-anchored evidence ledger → GPT auditors propose findings → a rules-only reporter that lists every proposal with what the auditor said about it) against a paper via a SHA-pinned thin launcher — then convert the verdict into a typed policy gate (BLOCK/WARN/NO_NEW_BLOCKER) and an append-only obligations ledger. Use when user says "integrity forensics", "forensic audit this paper", "投稿前自查诚信", "审这篇论文的诚信", or says "anti-autoresearch" when the upstream repo's own skills are not installed. Also invoked by /paper-writing (submission self-forensics, default ON), /peer-review (forensic appendix), /resubmit-pipeline.
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。
Two-way sync between a local paper directory and an Overleaf project, so ARIS audit/edit workflows stay on the local copy while collaborators edit in the Overleaf web UI. Use when user says "同步 overleaf", "overleaf sync", "推送到 overleaf", "connect overleaf", "Overleaf 桥接", "pull overleaf", "push overleaf", or wants to bridge their ARIS paper directory with an Overleaf project.
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。
Automate time tracking workflows in Clockify -- create and manage time entries, workspaces, and users through natural language commands.
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。
Automate Blocknative tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Debug inference clients that use an attached provider and its native endpoint, including hosted APIs and host-local Ollama, vLLM, SGLang, TRT-LLM, LM Studio, or NIM. Use for provider attachment, endpoint policy, credential substitution, topology, and migration from the removed managed inference endpoint. Trigger keywords - debug inference, managed inference endpoint, local inference, ollama, lm studio, vllm, sglang, trtllm, NIM, inference failing, model server unreachable, credential_endpoint_mismatch, host.openshell.internal.
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。
Inspects a recorded CARLA .log without replaying it — show_recorder_file_info for the header/actors/frames, collision queries between actor categories, and blocked-actor queries for vehicles that got stuck. Use when the user asks to "what's in this recording", "show recorder info", "which vehicles collided", "find collisions in the log", or "which actors were blocked/stuck".
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。
Drives a vehicle to a destination autonomously using CARLA's navigation agents (BasicAgent/BehaviorAgent/ConstantVelocityAgent), or plans a route between two points with GlobalRoutePlanner. The agent handles routing, obstacle avoidance and traffic-light obedience. Use when the user asks to "drive the ego to (x,y,z)", "navigate to the junction", "go to that location", "plan a route from A to B", or "make the car drive itself somewhere specific". Manual control is control-vehicle; autopilot roaming is control-traffic.
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。
Visualises and validates a map's pedestrian navigation mesh by sampling walkable locations (get_random_location_from_navigation) and drawing them as debug points. Use when the user asks to "show/visualise the pedestrian navmesh", "check walkable areas", "validate pedestrian navigation after a map import", or "why won't my walkers move". Confirms the navmesh loaded and how far it covers.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Launch, restart, or troubleshoot the real AO Electron desktop app from this repository; run a checkout against isolated or real local AO data; combine PR branches for local UI review; and diagnose stale Electron processes, port conflicts, or preload bridge mismatches. Use whenever asked to run, open, show, or visually verify AO frontend changes in the actual desktop app rather than ao preview, dev:web, or mock 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。
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU 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。
Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.
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。
Fetch content from Reddit via its JSON API using a browser session (DuckDuckGo-hop unlock). Use when accessing Reddit URLs, researching topics on Reddit, or when Reddit returns 403/blocked errors.
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。
Multi-pass deep code review for large patches (1000+ LOC) that maximizes real bug detection. Orchestrates targeted-review, shallow-review, and deep-review in parallel across all HIGH and MEDIUM risk files and commits: understands the feature holistically, splits by file and commit, runs deep review on every HIGH/MEDIUM file, targeted review across the same scope, and per-commit shallow review, followed by cross-cut consistency checks. Use when: reviewing a large patch (feature branch, multi-commit, or single large diff, 1000+ LOC), doing a thorough pre-merge review, or when shallower reviews miss bugs due to patch size. Triggers on: "review this branch", "review these commits", "review this feature", "mega review", "thorough review", "full review of X commits", or when the user specifies a commit range or feature for 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。