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One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.

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。

Designs and improves onboarding flows, empty states, and first-run experiences to help users reach value quickly. Use when the user mentions onboarding, first-time users, empty states, activation, getting started, or new user 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。

Preview Convex spend — rank functions by bytes/documents-read × call-volume from insights, project each cost driver's growth curve, name the cheapest fix; confirm-cost for paid actions.

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。

Read the Convex deployment's 72h insights (read limits, OCC contention), root-cause each event in code, report evidence-backed perf/cost findings with fixes.

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。

Combine React's official "You Might Not Need an Effect" guidance with this project's stricter no direct useEffect stance. Use when writing, reviewing, or refactoring React components that might reach for useEffect, derived state, event relays, reset logic, subscriptions, or client fetching.

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。

Convert any phased plan file, or a conversation about what to build, into labelled GitHub issues under one tracking epic mapping each issue to its source task. No plan file required. Don't use for writing plans, resolving issues, or triage.

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。

Writing documents an agent consumes. Use when creating or editing a skill, an AGENTS.md, a README section, a docblock, or any file reached by a pointer.

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。

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

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。

Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

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 GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.

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。

Audit a live page for accessibility issues, locate each WCAG violation precisely, and return a selector-grounded fix worklist without editing.

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。

This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation workloads.

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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack classification.

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。

This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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。

Conducts external reconnaissance using Open Source Intelligence (OSINT) techniques to map an organization's external attack surface without directly interacting with target systems. The tester gathers information from public sources including DNS records, certificate transparency logs, search engines, social media, code repositories, and data breach databases to build a comprehensive target profile. Activates for requests involving OSINT reconnaissance, external footprinting, attack surface mapping, or passive information gathering.

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。

Responds to security incidents in cloud environments (AWS, Azure, GCP) by performing identity-based containment, cloud-native log analysis, resource isolation, and forensic evidence acquisition adapted for ephemeral cloud infrastructure. Activates for requests involving cloud incident response, AWS security incident, Azure compromise, GCP breach, cloud forensics, or cloud identity compromise.

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。

Produce a release test-impact analysis for a Tamanu version upgrade: compare two Tamanu releases and classify every functional area as Full Test, Smoke Test, or Skip, each with a short reason and the Linear card or PR reference. Use when the user wants a test plan, QA scope, regression scope, or "what to test" for a Tamanu release or upgrade, wants to compare or diff two Tamanu versions (e.g. v2.57 to v2.59), or asks which areas changed between two releases. Handles Tamanu's train-release model (release/X.Y branches vs vX.Y.Z tags) and maps changed files and PRs to Tamanu functional sections (Patient Management, Encounters/Charting, Surveys & Programs, Medications, Labs, Imaging, Appointments, Vaccinations, Sync, Settings, Reports, Admin/Permissions, Mobile, etc.). Optionally maps against a regression-testing spreadsheet's sections when one is provided.

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 substantial agent-consumed guidance such as skills, AGENTS.md instructions, and documents reached by agent context pointers. Do not use for ordinary README or user-facing prose.

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。

Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-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。

Quality audit of a whole repo: bugs, security holes, what breaks under real load, risky code without tests, slow paths, and what to delete, merge or split. Ranked, each finding explained in plain English. One-shot report, changes nothing. Use for "audit this codebase", "review the whole repo", "find bloat", "what can I delete", /ponytail-audit.

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-of-life detection for runtimes (Node, Python, Java, Go, .NET) and key packages — surfaces past-EOL items, items reaching EOL within 90 days, and EOL base images for containers. Use when planning a runtime upgrade, auditing for unsupported versions, gating a deploy against EOL deps, or producing a remediation roadmap.

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。

Work through which package to add for a capability, and what each option costs — maintenance, licence, bundle weight, transitive risk, and whether the platform already does it. Use when someone needs a library for a job and has not picked one yet, when weighing two candidates, or when a package looks convenient but unmaintained.

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。

Show what this repository has already decided about its vulnerabilities, read from the durable record rather than by scanning again — open versus resolved, the decision taken on each, and what is still waiting. Use when picking up triage after a break, auditing past decisions, onboarding to a repository with security history, or checking nothing high-severity is open before a release.

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。

Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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。