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概要と使いどころ
Optimize slow React Router loaders by eliminating redundant DB queries, creating slim query variants, and parallelizing independent fetches. Use proactively when writing or reviewing loader code that calls domain operations services (e.g. CourseOperationsService, VideoOperationsService), or when triaging a slow page load.
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。
When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,' 'affiliate,' 'ambassador,' 'word of mouth,' 'viral loop,' 'refer a friend,' or 'partner program.' This skill covers program design, incentive structure, and growth optimization.
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。
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
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。
Close the post-launch loop — turn a live app's App Store signals (reviews, analytics, sales, crashes, listing conversion) into a metric-tagged backlog for the next version, AND verify whether last cycle's changes moved the metric they promised to move. Read-only on App Store Connect; every change is surfaced and routed to another command, never auto-applied. Use before planning the next version, on a monthly cadence, or ~1-2 weeks after shipping to check if a change worked.
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 skills for indie Apple developers — user acquisition, analytics interpretation, press/media outreach, community building, and indie business operations. Also covers the stage-by-stage store-growth audit and the post-launch store-signals loop. Use when user asks about growing their app, auditing growth levers, understanding metrics, getting press coverage, or running an indie dev business.
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。
5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff 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。
Provides guidance for writing, optimizing, and benchmarking Triton kernels for Intel XPU GPUs (Battlemage/Arc Pro B50) using the Xe-Forge optimization framework. Includes an LLM-driven trial-loop workflow (analyze, validate, benchmark, profile, finalize), XPU-specific patterns (tensor descriptors, GRF mode, tile swizzling), KernelBench fused kernels, and Flash Attention.
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。
Provides guidance for writing, optimizing, and benchmarking C++ CPU kernels with SIMD intrinsics (AVX2/AVX512) for the Hugging Face kernels ecosystem. Includes a two-phase workflow: Phase 1 correctness (generic → AVX2) and Phase 2 performance exploration (AVX512 with branching trial loop), runtime CPU dispatch, OpenMP threading, and brgemm integration for GEMM-heavy kernels.
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。
User-facing entry point for the wp-rocket issue workflow. Invoke directly to start a delivery run from a GitHub issue number, URL, or raw description. Runs inline in your conversation context; spawns specialist agents (ticket-writer, grooming-agent, challenger, backend-agent, frontend-agent, release-agent, lead-reviewer, qa-engineer) as isolated sub-agents; invokes supporting skills (knowledge-graph, dod, docs, issue-workflow) inline. Routes based on structured JSON outputs from each agent, manages loop counters, handles escalations, and maintains a live HTML run log.
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 weekly keep/cancel/promote/buy loop for a cold email sending fleet. Decides which domains are burned and should be cancelled, which warmed reserves to promote into campaigns, which capacity to demote, and how many new domains to buy — using measured reply, bounce, age and send-volume floors instead of gut feel. Use when asked "which inboxes should I cancel", "what should I replace this week", "my fleet costs too much", "I'm under my send goal", or as the Friday hygiene routine.
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。
Interactive loop that builds and tunes an AI prompt for evaluating whether a company fits a client's ICP. Run after any list-building skill (disco-like, blitz-list-builder, google-maps-list-builder, prospeo-full-export) to qualify companies before scaling. Iterates batches of 10 companies with user feedback, stops when 2 consecutive rounds have zero corrections, saves the final prompt for reuse. Always uses Claude Code Task sub-agents — never an external API key.
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。
Autonomous cold email campaign launcher. Takes one target company domain, scrapes their website, generates an ICP with Claude, pulls matching leads via Prospeo, enriches emails + company descriptions, personalizes each lead with parallel Claude Code Task sub-agents (A/B/C variants), and uploads as a live Smartlead campaign. Uses local JSON files for state (no database required). Use for automated daily campaign launches after you have an initial `client-profile.yaml` from /icp-onboarding. Triggers on "auto-research", "launch an automated campaign", "daily campaign", "run the research loop".
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 a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".
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。
Reusable approval-loop pattern for fanning out lead personalization across parallel Claude Code Task sub-agents. Shows the user 1 sample personalization, collects feedback, runs 10 more, approves, runs 10 more — stops when 2 consecutive rounds have zero edits, then scales to the full list. ALWAYS uses Claude Code Task tool sub-agents — never an external Anthropic/OpenAI API key. Use when any skill needs per-lead custom variables (situation lines, value lines, CTAs).
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。
One sentence that describes WHEN to invoke this skill. Write it as a trigger phrase, not a summary — include the words a user would say ("fix bug", "add feature", "review diff"). The agent routes on this field.
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 fixing any bug, write a test that reproduces it and watch it fail. Use for every bug fix.
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 before claiming work is complete, fixed, or passing — before committing, opening a PR, or handing off. Requires running the verification command in THIS turn and reading its output before any success claim.
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。
Catch hardcoded secrets, keys, and tokens before they get committed. Use before any commit and on any file with credentials.
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。
Systematically remove one harness component at a time and measure impact, killing scaffolding that no longer earns its complexity.
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 the untested code paths that actually matter, not just the coverage percentage. Use after adding tests.
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。
Write and read the between-session handoff file so a fresh agent with no memory can pick up where the last one stopped without re-deriving context. Structured prose, not JSON — the model writes prose better.
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。
Keep the agent's context lean so accuracy doesn't collapse. Use on long sessions, big files, or when the agent starts hallucinating.
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。
Reduce a change to the minimum that solves the problem. Use when code is over-engineered or a diff is bloated.
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。
Author idempotent init.sh under 120s plus sibling test.sh, stop.sh, reset.sh, serve.sh with fixed names the harness relies on.
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。