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概要と使いどころ
Use when answering Scenario admin analytics questions about Creative Unit consumption, most-used models, usage by user or project, AI adoption observability, cached usage data, CSV exports, enterprise PDF reports with charts, or a local analytics dashboard. Keywords: usage API, CU, analytics, adoption, reporting, consumption, cache.
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 flow lifecycle - publish, test, batch operations, inventory reports. Use when the user asks to publish, test, batch manage, or get an inventory of flows.
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。
Validate a third-party control repo across four automated layers plus one printed manual recipe. Layer 1 asserts native-source structure (Android getName() and iOS +moduleName to manifest nativeModule; @ReactMethod / RCT_EXPORT_METHOD to methods; no @ReactModule) plus load/init readiness (ReactPackage public no-arg constructor, iOS [cls new] no-arg init, requiresMainQueueSetup NO, non-throwing eager construction), so launch-time crashes surface before any build. Layer 2 validates the committed `./manifest.json` against the ppmplugin-format rules. Layer 3 asserts request/response/error-code agreement across native and PCF. Layer 4 compiles the PCF (auto-skipped if absent). Layer 5 prints a device end-to-end recipe. Native compile belongs to /build-android-binary and /build-ios-binary — this is the cheap structural pre-flight before those slow builds. Reports pass/fail per layer with a fix hint and updates .extension-state.md.
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。
Statically audit a built `.ppmplugin` before wrap testing. Checks archive layout, manifest compatibility, bundle consistency, Android DEX integrity and SDK leakage, iOS framework structure, native source-to-receiver alignment, and the PCF composite-key/sendAsync transport contract. Reports CRITICAL, WARNING, and INFO findings with fixes routed to the owning stage; never modifies the archive. Requires `jar`; `dexdump` or `strings` improves DEX inspection. Run after /assemble-ppmplugin or standalone on an existing bundle.
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。
Writes and rewrites text in Simplified Technical English (ASD-STE100), a controlled language for clear technical documentation. Use when the user asks for STE, Simplified Technical English, controlled language, or plain technical writing, and when the user asks to write, rewrite, review, or check technical documentation, procedures, manuals, instructions, warnings, or reports.
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。
对亚马逊竞品Listing进行全维度穿透分析，包括文案逻辑、评论分析、关键词分析、市场动态等。分析完成后自动保存为Markdown报告文档到reports/目录。Invoke when user uses /amazon-analyse command with a product ASIN.
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。
Patterns for individual long-form content pieces. Case studies, whitepapers, research reports, definitive guides, manifestos, ebooks, long-form tutorials. The structural disciplines that distinguish publication-quality long-form from bloggy-long padding or academic bloat. Different from pillar-content-architecture (which covers hub structure); this skill covers individual deep-dive pieces. Triggers on long-form content, case study writing, whitepaper, research report, definitive guide, ebook, manifesto, long-form tutorial, deep-dive article, anchor piece, foundational article, structural archetypes. Also triggers when a piece is over 3,000 words and the team is unsure how to structure it, when a long-form draft feels padded or saggy, or when a flagship asset needs the depth to actually earn the length.
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 read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
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。
Adversarial "perfection" review — hold a change to an *ideal* bar, not just a correct one, assuming eternal time, unlimited energy, and no ship pressure. Use when the user asks to review "for perfection", to make a defect "impossible to express", or to hunt where a defect "relocates" across review rounds. Grounds every claim in the diff, fans out adversarial verifiers via Workflow, and reports residual surfaces with a structural fix for each. ONLY invoke when the user explicitly asks for a perfection / ideal-bar 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。
Expert Section 508 compliance advisor for US federal ICT accessibility. Use this skill whenever a user asks about Section 508, WCAG 2.0/2.1 AA for federal systems, VPAT or Accessibility Conformance Reports (ACR), accessibility audits, remediation planning, PDF accessibility, web or software accessibility, mobile accessibility, federal procurement accessibility requirements, contractor obligations, undue burden exceptions, assistive technology compatibility, or Section 508 testing. Covers the Revised Section 508 Standards (2018), all WCAG 2.0 Level AA success criteria, the four POUR principles, testing methodologies, and agency compliance workflows. Trigger even if the user doesn't say "skill" — any Section 508 or ICT accessibility question for federal systems should use this skill.
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。
A Yahoo Finance (yfinance) powered financial analysis tool. Get real-time quotes, generate technical indicator reports (RSI/MACD/Bollinger/VWAP/ATR), summarize fundamentals, and run a one-shot report that outputs a text summary.
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。
Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal reproductions and issue drafts, to preparing, testing, and submitting a pull request that follows this repository's conventions. Use when the user says they found a bug, wants a feature, wrote a workaround worth upstreaming, or asks how to contribute, open an issue, or make a PR to retentioneering-tools.
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。
Report-only QA — same browser-based testing as qa but documents bugs with screenshots and repro steps only, no code changes. Health score formula weighted across 8 categories. Outputs to .gstack/qa-reports/.
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。
Identifies security vulnerabilities, generates structured audit reports with severity ratings, and provides actionable remediation guidance for SAST, DevSecOps, and compliance.
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 dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.
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。
Diagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters. Scope mirrors the HyperPod troubleshooting guide. Invoke when the user reports a Slurm node stuck in down/drain, "Node unexpectedly rebooted" after auto-repair, slurmd not running, jobs stuck PENDING with REASON=Resources while sinfo shows idle nodes, jobs stuck COMPLETING after node replacement, GRES/GPU counts wrong, scontrol ping failing, slurmctld unresponsive, an Action:Reboot/Replace request that did not trigger HyperPod auto-recovery, or auto-resume not restarting a job. Also triggers on "drain before reboot", "diagnose a Slurm node", "investigate stuck jobs."
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。
Senior PM for enterprise software and SaaS — portfolio management, quantitative risk analysis, prioritization, and executive reporting. Use for portfolio health reviews, board-ready status reports, and multi-project risk/capacity 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。
Validate and score Claude Code skill packages for quality, completeness, and best-practice compliance. Tests Python scripts, checks YAML frontmatter, and generates reports. Use when creating, validating, or auditing skill packages.
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。
Business intelligence across dashboard design, visualization, and reporting automation. Use when designing dashboards, building KPI frameworks, automating reports, creating data stories, or optimizing BI tool performance.
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。
Headcount and delivery-capacity planning — effective capacity from raw headcount, hire/contract/defer scenarios, and capacity-vs-commitment gap reports. Use when planning a quarter, sizing a hiring ask, or testing whether a roadmap fits.
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。
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.
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。
Verify a BibTeX file for hallucinated or fabricated references by cross-checking every entry against CrossRef, arXiv, and DBLP. Reports each reference as verified, suspect, or not found, with field-level mismatch details (title, authors, year, DOI). Use when the user wants to check a .bib file for fake citations, validate references in a paper, or audit bibliography entries for accuracy.
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 fable-mode execution discipline on Claude Fable 5.1 — the top of the escalation ladder and the strongest staged run available. Routes the task to the @fable-orchestrator agent with its model overridden to Fable (`model: "fable"`), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on fable", "stage this on fable", "deep work mode, fable", "run this on 5.1"), or when a fable-opus run reports the task exceeds Opus's ceiling. Benchmarked 2026-09-02: zero correctness delta on Fable 5.1 at ~1.1× tokens, ~2× wall — a provenance runner, not an accuracy runner. Do NOT use for ordinary single-pass tasks — prefer fable-opus, fable-sonnet, or fable-haiku when the task doesn't need peak reasoning. Fable is plan-included; bulk work still routes down-tier.
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 and read this skill immediately if the user request is in any way related to SEO or a site's organic search or AI search presence. That includes site audits, rankings, keyword research, competitors, backlinks, click or traffic changes, indexing problems, crawling, redirects, sitemaps, metadata, structured data, Core Web Vitals, internal links, content opportunities, programmatic SEO, local search, Search Console or Google Analytics questions, Google update impact, llms.txt, AI search visibility in ChatGPT, Claude, Perplexity, or Google AI Overviews, and client SEO reporting. Routes to evidence-backed local reports through the SEO CLI and MCP server.
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。