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概要と使いどころ
Marc Andreessen-mode decision and productivity skill. A blunt, market-first operator that pressure-tests ideas, ventures, features, and career bets through Andreessen's actual frameworks — market dominates team and product; the only milestone that matters is product/market fit.
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。
You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.
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。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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 AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
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Convert existing tasks into actionable, dependency-ordered GitHub issues for the feature based on available design artifacts.
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。
Execute all tasks from the task breakdown to build the feature. Use after task generation to systematically implement the planned solution following TDD approach where applicable.
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。
Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.
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Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
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。
Synchronize project documentation with recent code changes, refactors, and feature implementations in the current branch.
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。
Generate a custom checklist for the current feature based on user requirements.
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Generate technical implementation plans from feature specifications. Use after creating a spec to define architecture, tech stack, and implementation phases. Creates plan.md with detailed technical design.
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 current branch follows feature branch naming conventions
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。
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
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 a support investigation as a suggest-and-interpret loop, never executing anything yourself — orient to the product's support pack and follow the signal with judgment. Use when someone asks for help triaging, investigating, or resolving an operational or support issue on a deployment. Not for feature-development cards (see investigate-and-fix and implement-this).
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。
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
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Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.
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 PDF documents to clean structured Markdown for LLM context. Supports two modes: fast (PyMuPDF) and accurate (IBM Docling TableFormer AI). Features aggressive persistent caching, image extraction with metadata, table detection, and batch processing. Use when asked to convert PDFs, extract PDF content, parse documents, or prepare PDF data for AI/LLM consumption.
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。
Standalone security audit on audit tier. Focused review of a feature or file set. Produces severity-ranked findings and remediation tasks for worker-tier /resume.
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Test-driven development with red-green-refactor and behavior-focused tests. Use when the user explicitly wants test-first work, a regression test before a fix, or integration tests that drive a feature through a public interface.
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Streamlit Web application development expert skill. Provides guidance on OIDC authentication (st.login/st.logout/st.user), secrets management, data visualization with Plotly/Altair, performance optimization with caching, and modern Streamlit features (v1.42-1.52+). Use this skill when building Streamlit apps, implementing user authentication, creating data dashboards, or optimizing app performance. Triggers include "streamlit app", "st.login", "data dashboard", "streamlit authentication", "streamlit visualization".
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 should be used when developing code using Test-Driven Development (TDD) methodology. Use this skill when the user wants to write tests first before implementation, needs guidance on the red-green-refactor cycle, or explicitly requests TDD-style development. Triggers include requests like "develop this feature using TDD", "write tests first", "help me with test-driven development", or "implement this following TDD practices". The skill guides through writing failing tests, implementing minimal code to pass, and refactoring while maintaining test coverage.
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Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical 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。
Use when designing, evaluating, documenting, modernizing, reviewing, refactoring, or implementing software architecture and code-level structure, including requirements, context, architecture style selection, microservices patterns, WebAssembly/WASI/Component Model, UMA portable capabilities, runtime contracts, capability graphs, governed MCP/agent orchestration, clean code, code smells, Rust crate/library API architecture, Cargo feature/MSRV/SemVer policy, unsafe/FFI/no_std/library testing, modularity, views, NFR tactics, IAM/auth, security patterns, cost estimation, architecture-as-code fitness functions, ADR/RFC work, and engineering execution plans.
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。