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概要と使いどころ
Intelligent README.md generation prompt that analyzes project documentation structure and creates comprehensive repository documentation. Scans ./ directory files and creates a README.md file for the project. Only used when creating a new repository. or when the README.md file is missing.
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-timeframe crypto technical analysis with scored trading signals, structured trade plans, and position sizing via Binance public API. Use when user asks to "analyze BTC", "what's the signal on ETH/USDT", "crypto analysis", "trade setup for SOL", "scan crypto pairs", "check the trend on BTC", "multi-timeframe analysis", "generate a trading signal", "what's the bias on ETH", "TA on BTC/USDT", "is it a good time to buy BTC", or "position size for a crypto trade".
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。
Music-video production skill for turning an existing MP3 into a pure-visual (no-voiceover) music video through a bundled Busabase App-in-Skill UI. Use when the user wants to build an MV from a song they upload: write a one-line concept, keep a cast of characters with reference cards, and break the song into storyboard shots where each shot has a scene description and an image + video that can be either AI-generated (image-to-image from the character cards, draft video via local LTX) or uploaded by the user. Same character/storyboard management model as kelly-drama. Song generation (creating the song itself, incl. voice-cloned singing) is a documented future capability.
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。
Personal App-in-Skill customer-support desk (help desk) that triages incoming support tickets from email, WhatsApp, web chat, contact forms, and WeChat, drafts KB-grounded replies, and proposes actions, then holds every send behind a human approval queue with a pre-send quality gate (support-qa) that outputs SHIP/FIX/BLOCK, plus SLA and CSAT tracking. Use when the user invokes $kelly-support or /kelly-support, mentions 客服 / 工单 / 支持台, a support desk, help desk, customer service, support tickets, ticket triage, a knowledge base or canned macros, SLA / first-response time, CSAT, escalations, or refund requests, wants incoming tickets triaged into a needs_review → approved → done pipeline, agent-drafted replies grounded in a knowledge base and reviewed before sending, refunds and commitments gated behind human approval, or breaching-SLA tickets surfaced. This is the OPERATOR desk (agent + human), distinct from a visitor-facing chat bubble.
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。
Goal-driven WeChat relationship strategy App-in-Skill. Reads the operator's own local WeChat through wechat-cli-rs, discovers candidates against an explicit goal, promotes only user-selected people into Busabase, and builds relationship snapshots and next-action suggestions without sending messages or modifying WeChat.
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。
Publish agent skills AND MCP servers to every marketplace, plugin directory, and registry — security-scan for private data, validate with `gh skill`, cut a release, wire the Claude /plugin and Codex marketplaces, publish an MCP server to the official MCP Registry (mcp-publisher + server.json), and prep the curated / monetized stores (Buda Marketplace, Agensi, OpenClaw ClawHub) and review-gated directories (ChatGPT App Directory, Anthropic Connectors + Desktop Extensions/.mcpb). Use when the user wants to publish, release, list, 上架, or distribute agent skills, plugins, or MCP servers to skills.sh / SkillsMP / Claude Code / Codex / the MCP Registry.
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。
Busabase-backed, read-only App-in-Skill portfolio dashboard that aggregates Webull brokerage holdings (accounts, positions, cash/margin, market value, unrealized P/L, day change, buying power). Use when the user invokes $kelly-invest-webull or /kelly-invest-webull, wants to review their Webull portfolio, holdings, positions, accounts (cash/margin), asset allocation, market value, unrealized P/L, day change, cash, or buying power. Read-only aggregation only — it never places, modifies, or cancels orders and never moves money.
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, maintain, and run conformance tests for canonical App-in-Skill projects created by kelly-app-skill-creator. Use when a Kelly app skill needs contract checks, local server smoke tests, responsive browser acceptance, temporary open-source Busabase integration, environment-gated Busabase Cloud OAuth verification, lazy provisioning checks, persistence tests, AirApp parity tests, or CI coverage. Keep app creation in kelly-app-skill-creator and own only the reusable test harness, per-skill fixtures, execution gates, diagnostics, and truthful pass/fail/skip reporting.
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。
Design and create Busabase-backed App-in-Skill packages with a canonical app project, Busabase-native setup and product onboarding, review/execution workflows, a standardized responsive UI, and AirApp-first delivery. Use when a user wants a Busabase research desk, review queue, planner, action console, operating dashboard, control panel, collaboration workspace, onboarding/readiness flow, or an existing Kelly App-based skill updated. Every generated skill contains a complete app/ project, deploys that source to Busabase AirApp by default, runs pnpm dev only when local preview is explicitly requested, follows the Kelly desktop and phone UI contract, delegates AirApp runtime, SDK, security, scaffolding, and deployment constraints to busabase-app-creator, and delegates repository-level conformance testing to kelly-app-skill-creator-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。
Brand-narrative single source of truth (Busabase App-in-Skill) built on the TALE discipline — Trace / Architect / Land / Evaluate. The agent drafts positioning, the message house of value pillars, the story bank, evidence-backed proof points, and vocabulary/tone guardrails; the human curates which drafts become the CANONICAL narrative; a drift monitor flags off-brand usage across channels. Use when the user invokes $kelly-brand or /kelly-brand, mentions brand narrative, brand messaging, positioning, message house, value pillars, story bank, proof points, tone/vocabulary guardrails, narrative quality (NQS), brand drift, or wants a single source of truth for what the brand says.
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。
Influencer/creator marketing command desk (Busabase App-in-Skill) — the agent sweeps and fit-scores creator candidates, drafts outreach and briefs, and gates content before it ships; the human reviews creator cards, approves outreach/briefs/contracts, and tracks campaign ROI. Use when the user invokes $kelly-creators or /kelly-creators, mentions influencer or creator marketing, creator discovery, fit scoring, outreach DMs, campaign briefs, UGC, sponsorship or partnership deals, content review / FTC disclosure gating, or campaign ROI, or wants to review/approve agent-drafted creator outreach before it is sent through other channels.
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 selects_candidates.yaml exists and the user asks to design structure, build an edit blueprint, or create 04_plan/edit_blueprint.yaml.
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。
Resolve registered vertical composition inputs from source-grounded framing evidence, source A/V identity, layout anchors, and safe degradation. Use when a blueprint contains framing candidates or visual intents for a vertical delivery.
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 edit_blueprint.yaml and selects_candidates.yaml are ready and the user asks to compile the rough cut, generate timeline.json, or apply a review patch.
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。
React Three Fiber architecture: production-level Canvas mount, component lifecycle, useFrame. Use when implementing, reviewing or testing react three fiber architecture for React, Vite, Next.js or Perfect_AI projects.
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 independent research, implementation, or verification in parallel when it can save time after dispatch and review overhead, or when explicitly requested. Keep decisions and final integration in the main agent.
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。
Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.
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。
Discover and inventory shadow API endpoints that operate outside documented specifications using traffic analysis, code scanning, and API discovery platforms.
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。
TLS 1.3 (RFC 8446) is the latest version of the Transport Layer Security protocol, providing significant improvements over TLS 1.2 in both security and performance. It reduces handshake latency to 1-R
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。
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。
Conducts comprehensive network penetration tests against authorized target environments by performing host discovery, port scanning, service enumeration, vulnerability identification, and controlled exploitation to assess the security posture of network infrastructure. The tester follows PTES methodology from reconnaissance through post-exploitation and reporting. Activates for requests involving network pentest, infrastructure security assessment, internal network testing, or external perimeter testing.
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。
Auditing Kubernetes cluster RBAC configurations to identify overly permissive roles, wildcard permissions, dangerous ClusterRoleBindings, service account abuse, and privilege escalation paths using kubectl, rbac-tool, KubiScan, and Kubeaudit.
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。
Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert 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。
Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.
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。