Skills（SKILL.md）は、AIエージェント（Claude Code、Cursor、Codexなど）に特定の能力を追加するための設定ファイルです。
詳しく見る →Scans QuickBooks Online books via the CleanupOwl API to detect and fix bookkeeping issues. Use when the user mentions CleanupOwl, QuickBooks cleanup, QBO bookkeeping scan, fixing bookkeeping errors, running accounting scans, or applying solver fixes to flagged records.
company-check
pine-strategy-builder
sop-writer
workflow-auditor
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> **Compatible with Claude Code, Codex CLI, Cursor, Windsurf, and any SKILL.md-compatible agent.**
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Automated weekly self-audit of agent operations, cron health, memory gaps, and process failures. Use when performing periodic reviews, operational health checks, identifying automation gaps, surfacing wrong assumptions, or generating improvement recommendations. Triggers on: weekly review, meta-audit, operations audit, what broke this week, process review, improvement cycle, retrospective, week in review.
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This skill is a self-correcting creative agency and a startup within an agent.
A cybernetics × AI agent seminar skill. Grounded in *Cybernetics and Scientific Methodology* (Jin Guantao) and Wienerian cybernetics, it analyzes how cybernetic principles inform modern AI agent R&D, architecture review, and design guidance. Trigger when users mention cybernetics, feedback control, agent architecture, systems theory, black-box methods, negative feedback, positive feedback, agent design principles, steady-state systems, information entropy, purposive behavior, functional simulation, or Ashby's law of requisite variety. Also use when users want to review agent architectures from a cybernetic perspective, discuss feedback mechanism design, analyze agent stability and self-evolution, or organize cybernetics × AI agent seminars.
A double-loop learning execution review skill. Reviews the execution history after a task is completed and turns it into a concise retrospective, handoff, or improvement memo. Use this whenever a coding, writing, research, planning, debugging, or operational task has just finished; whenever files, commands, tests, or deliverables were produced; whenever the user asks to summarize what was done, review the execution, create a handoff, capture lessons learned, or prepare for the next iteration; and especially when the user needs not only a summary of what happened, but a judgment about whether the original goal, assumptions, success criteria, or working rules should change before the next pass. If there is execution evidence in the conversation, err on the side of using this skill instead of waiting for the user to ask explicitly.
Pre-launch security audit for web apps. Runs OWASP checks via shell scripts, then AI analyzes findings. Covers rate limiting, hosting bypass, data exposure, headers, session security, AI risks. Complements /security-review.
Compare, evaluate, and upgrade SKILL.md skills. This skill should be used when introducing a new skill that overlaps with an existing one, checking if an installed skill has upstream updates, merging two skills with complementary strengths, evaluating whether a skill is worth installing, or auditing unused skills. Trigger phrases include 'compare skills', 'check for updates', 'merge skills', 'evaluate this skill', 'upgrade skill', 'audit skills'.
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial transcriptomics (Visium/Visium HD/MERFISH/Slide-seq), CITE-seq, ATAC-seq, WNN, cell cycle regression, hashing/demultiplexing, sketch analysis, BPCells on-disk, pseudobulk, and format conversion. Use this skill whenever writing, debugging, or reviewing Seurat R code, building scRNA-seq pipelines, or looking up Seurat syntax, even for simple questions.
Protect email pipelines from injection attacks, phishing, content manipulation, and AI agent exploitation. Use when building inbound email processing, sanitizing email content, detecting phishing or BEC, securing AI agents that read email, or hardening email infrastructure against spoofing and data exfiltration.
Run email A/B tests with statistical rigor. Use when testing subject lines, content variants, send times, CTAs, or measuring experiment significance.
Write emails people actually read. Use when crafting subject lines, structuring email content, writing CTAs, optimizing preview text, or improving open and click rates.
Avoid triggering spam filters with your email content. Use when emails land in spam, auditing content for filter triggers, checking link patterns, or optimizing HTML structure.
Ramp sending volume on new domains and IPs. Use when launching a new sending domain, switching providers, moving to a dedicated IP, or resuming sends after a pause.
Understand what determines inbox vs spam placement. Use when emails land in spam, investigating Gmail Promotions tab, diagnosing deliverability issues, or optimizing engagement signals.
Monitor and protect your email sender reputation. Use when deliverability drops, investigating spam placement, checking blocklists, or recovering from reputation damage.
Diagnose email problems and plan email infrastructure. Use when emails land in spam, bounce rates spike, deliverability drops, open rates decline, providers throttle sending, you need to set up email for a new product, or you're not sure which email skill to use.
Receive, parse, and process incoming email via provider webhooks. Use when setting up inbound email handling, parsing MIME messages, extracting content from replies, detecting threads, filtering spam on inbound, or routing incoming messages.
Classify inbound email replies by intent and route them appropriately. Use when building reply handling for outreach, support, or transactional email - detecting interested, not interested, OOO, bounce, unsubscribe, forwarded, and question replies.
Choose and configure an agent-native inbox provider for AI agents that send and receive email. Use when giving an AI agent its own email address, setting up two-way agent email communication, comparing agent inbox platforms, or adding guardrails to autonomous email workflows.
Process email bounces and protect sender reputation. Use when handling hard/soft bounces, building suppression lists, configuring retry strategies, or investigating high bounce rates.
Build dashboards, alerts, and monitoring systems for email sending operations. Use when setting up deliverability monitoring, configuring alert thresholds, checking blocklists, building email metrics dashboards, or responding to deliverability incidents.
Design automated email sequences and drip campaigns. Use when building onboarding flows, nurture sequences, re-engagement campaigns, or follow-up automation.
Design product notification emails. Use when building activity alerts, digests, status updates, or managing notification frequency and user preferences.
Build welcome and activation email sequences. Use when designing signup flows, driving users to key actions, converting trials to paid, or reducing early churn.
Design and send transactional emails. Use when building password resets, receipts, shipping notifications, account alerts, or separating transactional from marketing streams.
Choose and configure an email service provider. Use when setting up email for a new project, comparing providers, migrating between providers, or adding failover.
Use when deploying agents in regulated industries or needing compliance guidance. Covers EU AI Act, NIST AI RMF, and ISO 42001 for agent operators.
Use when estimating or optimising LLM costs for agent operations. Framework for calculating actual monthly spend including hidden multipliers.
Use when choosing a memory architecture for an agent system. Decision tree covering context window, conversation buffer, vector store, structured DB, and episodic memory.
Use when deploying a new agent into production. Checklist for onboarding agents.
Use when hardening agents before deployment. 10-point pre-deployment security checklist.
Use when building self-diagnostic capabilities into agents. A prompt agents can run to diagnose themselves.
Use when setting up crypto wallets for autonomous agent payments. Step-by-step wallet configuration.
Use when choosing between multi-agent architecture patterns. Five patterns compared.
Use when making agents feel present rather than scripted. The 25-line fix for AI personas that break character.
Use when understanding how agents discover and use MCP tools. How tool discovery works in the MCP protocol.
Use when writing prompts that generate better prompts. A meta-prompt that improves any AI conversation.
Use when choosing the right LLM for a specific task. Decision framework for model selection.
Use when deciding between on-device and cloud agent deployment. Cost and privacy tradeoffs.
Use when understanding the SOUL.md identity specification. The standard for constitutional agent identity.
Use when choosing between coordination patterns for multi-agent systems. Stigmergic vs centralised comparison.