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Found 1,660 Skills
Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
Comprehensive skill for project planning and prompt engineering. Covers hierarchical plans (briefs, roadmaps, phases), Claude-to-Claude meta-prompts, and multi-stage workflows. Use when: planning, prompt creation, agentic pipeline work, project roadmap, meta-prompts, research to implement workflow.
Automated content production pipeline: hot topic aggregation from 10+ platforms (Bilibili, GitHub, Reddit, YouTube, Weibo, Zhihu, etc.), AI-powered topic scoring, multi-platform content generation (Xiaohongshu, WeChat, Twitter), draft review, and auto-publishing. Use when: user wants daily content pipeline, hot topic collection, content generation, article publishing, or content factory automation.
Workflow 1: Full idea discovery pipeline. Orchestrates research-lit → idea-creator → novelty-check → research-review to go from a broad research direction to validated, pilot-tested ideas. Use when user says "找idea全流程", "idea discovery pipeline", "从零开始找方向", or wants the complete idea exploration workflow.
Full research pipeline: Workflow 1 (idea discovery) → implementation → Workflow 2 (auto review loop). Goes from a broad research direction all the way to a submission-ready paper. Use when user says "全流程", "full pipeline", "从找idea到投稿", "end-to-end research", or wants the complete autonomous research lifecycle.
Autonomous novel writing CLI agent - use for creative fiction writing, novel generation, style imitation, chapter continuation/import, EPUB export, and AIGC detection. Supports Chinese web novel genres (xuanhuan, xianxia, urban, horror, other) with multi-agent pipeline, two-phase writer (creative + settlement), 33-dimension auditing, token usage analytics, creative brief input, structured logging (JSON Lines), and custom OpenAI-compatible provider support.
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.
Record and analyze post-trade outcomes for signals generated by edge pipeline and other skills. Track false positives, missed opportunities, and regime mismatches. Feed results back to edge-signal-aggregator weights and skill improvement backlog.
Use when setting up or optimizing developer workflows in a monorepo, managing mise tasks, git hooks, CI/CD pipelines, database migrations, or release automation. Invoke for development environment setup, build automation, testing workflows, and release coordination.
Use when writing SQL queries, building analytics dashboards, tracking metrics, designing data pipelines, or analyzing user behavior and product usage