3.2천건 · 관련도순
Benchmark stellar-build skills to measure quality and the lift from optimization. Use when the user says "bench my skills", "bench-skills", "benchmark skills", "did optimization help", "measure my skills", "stellar-loop bench", or wants to quantify whether /optimize-skills actually improved things. Held-out eval prompts scored by an LLM judge, reproducible with fixed seeds. Fully local.
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Stryker-driven test cull: delete slop, consolidate redundants, fill mutation-coverage gaps. Score up, test count and lines down. Use when the user says 'trim tests', '/trim-tests', or wants to clean up LLM-generated test bloat. No args picks the worst-scored file; argument scopes to a section, service, or file.
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Generate a PDF packing slip and a PNG shipping label from the same order data in a single fulfillment workflow.
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Export e-commerce order data to CSV with order numbers, customer details, amounts, and fulfillment status.
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Convert a resume PDF to clean markdown for LLM parsing or candidate pipelines.
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Convert a contract PDF to clean markdown for clause extraction or LLM analysis.
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Convert a PDF invoice to clean markdown for LLM processing or document pipelines.
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OpenAI Codex (CLI / IDE / cloud / ChatGPT desktop app) リファレンス。 GPT-6 系, Fast / Ultrafast mode, codex exec, スラッシュコマンド, prompting, AGENTS.md, rules, subagents, approvals, auto-review, sandbox, permission profiles, profiles, config.toml, 環境変数, roles, MCP 設定, GitHub Action, Codex SDK, App Server (JSON-RPC), Agents SDK 連携, Codex skills (.agents/skills), plugins, scheduled tasks, Browser, Computer Use, cloud / local / worktree 環境, git worktrees, Record & Replay, cloud 委譲, GitHub / GitLab / Linear / Slack 連携, administration, provisioning, analytics API, compliance API, LLM gateway, Codex Security 脆弱性スキャン (SARIF), deep scan, triage, findings, threat model, security hardening。
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Claude Code (code.claude.com) デプロイ・CI/CD リファレンス。 Amazon Bedrock / Google Cloud Agent Platform (Vertex AI) / Microsoft Foundry / Claude Platform on AWS、 Claude apps gateway、LLM gateway (protocol / rollout)、 GitHub Actions (claude-code-action) / GitLab CI/CD / GitHub Enterprise Server (GHES)、 Code Review (REVIEW.md・review level・@claude review always・コスト・トラブルシュート)、 ローカル /code-review、ultrareview (claude ultrareview)、 Slack (Claude Tag、旧 Claude Code in Slack、Pro/Max)、 headless mode、cloud environments、devcontainer、 self-hosted environments（runner 構成・identity 検証・本番デプロイ）。
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Token-slim mode — cut this session's token usage by routing every file read, code search, directory listing, and JSON dump through the token-slim MCP server (or its binary directly). Works in any repository or folder; the server itself is LLM-agnostic (any MCP client). Use when the user types /slim, or asks to save/reduce tokens, "トークン節約", "トークン削減", "コンテキスト節約", "token slim".
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Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.
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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.
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Guide for building and refactoring a FastAPI + Prefect + PostgreSQL/pgvector analytics pipeline that extracts insights from chat data using LLM classification, embeddings, UMAP/HDBSCAN clustering, and narrative generation. Use this skill whenever working on this project's codebase — including adding features, refactoring, debugging, creating new endpoints, modifying pipeline stages, or reviewing architecture decisions. Also trigger when the user asks about project structure, stack decisions, async migration, or best practices for this specific system.
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Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.
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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks
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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
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Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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