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Diagnose React + Three.js (or any browser canvas) frame stutter by capturing a CDP CPU profile via Playwright instead of guessing from `[perf]` console logs. Use when: (1) frame time is bad but `[perf]` instrumentation doesn't pinpoint the offender, (2) you've spent more than ~30 minutes "fixing" suspects without measuring, (3) hot paths involve animation systems, allocation bursts, or invisible scene-graph traversal, (4) you need self-time per function with line numbers, not aggregate "render slow". The `console.warn` pattern is good for monitoring; CDP is needed for diagnosis. Covers script template, sourcemap setup so minified function names are decoded, and interactive variant where the user drives the interaction and signals stop via `touch /tmp/<flag>`.

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 needs to search or navigate code with Sourcegraph MCP tools. Provides disciplined search workflows for finding implementations, understanding systems, debugging issues, fixing bugs, and reviewing 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。

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.

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。

Execute governed implementation workflow with work items, RFC/ADR checks, phase gates, testing, and closure. Use when: (1) User invokes /gov, (2) A non-trivial change needs work item tracking, (3) Implementation may require RFC/ADR handling

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-angle code and plan review with security, scope, and UI profiles. Use when reviewing code, plans, PRs, or running pre-merge quality gates.

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 has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore - same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API 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。

Delegate an approved finite batch of independent engineering or research responsibilities using the operator's existing worker channel. Use for broad repository discovery, multiple search directions, repeated checks and substantial dirty work; keep coupled single tasks with one worker and explicit solo work in the main session.

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。

Reliability gate for answers. Forces evidence-based reasoning, explicit uncertainty, and “insufficient information” instead of guesses. Use when user says “be 100% sure”, “no hallucinations”, “only if verified”, “grounded answer”, or when stakes are high.

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 a standalone read-only rules compliance gate against changed files or a git ref. Use when you need a dedicated project-rules check without a full review or verify pass.

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。

Systematic debugging with root cause investigation. Four phases: investigate, analyze, hypothesize, implement. Iron Law: no fixes without root cause.

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。

Fast headless browser for QA testing and site dogfooding. Navigate any URL, interact with elements, verify page state, diff before/after actions, take annotated screenshots, check responsive layouts, test forms and uploads, handle dialogs, and assert element states. ~100ms per command. Use when you need to test a feature, verify a deployment, dogfood a user flow, or file a bug with evidence. Use when asked to "open in browser", "test the site", "take a screenshot", or "dogfood this".

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。

Before committing any non-trivial change, dispatch the diff to a reviewer runtime via ATO (`ato dispatch <reviewer> --session <id>`), parse the numbered/severity-tagged findings, apply or defer each one with a recorded justification, then commit. Fights the "build passes therefore ship it" failure mode — what Garry Tan calls the AI agent complexity ratchet. Place in the v2.16 stack: this skill is the LAST gate. `ato-warroom` decides the design; `ato-mission` runs the multi-step work and produces the diff; `ato-review` checks the diff before commit. When the review is part of a Mission, dispatch the review with `--require-tools read_file,grep,git_diff,git_log` so the reviewer can walk the source itself instead of reasoning from a paraphrase (PR-1.5 tool surface). Receipts land in `execution_logs` and the Mission narrative. Fires automatically before commits touching public surface (CLI subcommands, Tauri commands, MCP tools, schema migrations, security boundaries) or whenever a diff exceeds ~50 LOC of behavior change.

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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。

Drive a real web browser: navigate, read, find, click, fill, screenshot

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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。

Aggregate DiT inference outputs into per-dimension VBench metrics via AISBench. Run after the quant-tuning-evaluate DiT workflow produces a directory of mp4s and the user supplies a VBench-1.0-mini JSON + pre-populated cache dir. Single mode (Standard).

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。

Finish NowStack Mobile Google Play setup and internal Android releases. Use for signed AAB upload, gpc/browser fallback, privacy/app-content gates, or gpc commit failures.

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。

Full Stock Analysis Orchestrator — launches 5 parallel subagents via delegate_task for comprehensive multi-dimensional stock analysis with composite Trade Score (0-100). Triggered by "trade analyze <TICKER>" or "analyze <TICKER> stock".

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 social content campaign pipeline. Takes a marketing goal and produces scheduled, on-brand social posts with visuals across postiz-supported platforms, while keeping Typefully for X/thread specialist flows and fallback coverage. Chains: CMO strategy, content writing with voice calibration, AI slop audit, selective Paper MCP visual design, and routed scheduling via postiz or Typefully. Each phase has a human gate. Use this skill whenever someone wants to plan and execute a batch of social posts — not just one-off tweets. Triggers include: "social campaign", "schedule posts", "pre-launch content", "content calendar", "social content pipeline", "build up content", "I need social media content for my launch", "help me build social presence", "plan a week of posts", "batch social content", "social media strategy", or any request involving multiple scheduled posts with optional visuals.

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。

Scan the current market landscape and produce a ground-truth ecosystem snapshot. Chains /last30days for live research, validates with user, and writes brand/landscape.md with a Claims Blacklist that hard-gates all content generation. Use when: "landscape", "ecosystem", "market snapshot", "ground truth", "what's happening", "refresh landscape", "market trends", or before any content campaign to verify claims.

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。

Audits brand/ files for completeness, consistency, and freshness. Use when the agent or user wants to review brand files, audit marketing docs, check brand consistency, verify brand health, or assess what's missing from brand memory. Produces a structured audit report with per-file scores, cross-file contradiction detection, staleness flags, and recommended next skills to run. This is the quality gate for the brand memory system. Also use when someone says 'check my brand', 'brand audit', 'what's missing', 'is my brand ready', 'brand health', 'review my marketing docs', or before running any campaign to verify brand foundations are solid.

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 pipeline: investigates a bug, diagnoses root cause, writes failing tests, implements fix via TDD, and reviews the result. Orchestrates bug-investigator -> bug-fixer -> code-reviewer.

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 every coding task. Enforce strict TDD workflow: activate Serena, investigate first, clarify+confirm requirements, write per-task REQUIREMENTS.md in .requirements/<datetime>_<feature_name>/, verify APIs via web search, then implement in tiny test-verified steps.

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 pre-submission validation. Handles 5 formats — Kaggle CSV / Kaggle Code Competition / prediction-file zip / Docker container / Simulation agent. Delegates to the submission-validator agent for mechanical checks. Use right before any submission.

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。