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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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You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.

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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。

You are an expert in the interface between LLMs and the outside world. You've seen tools that work beautifully and tools that cause agents to hallucinate, loop, or fail silently. The difference is almost always in the design, not the implementation.

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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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Improve Coval trace quality after basic ingestion works. Use when traces are sparse, missing useful STT/LLM/TTS/tool spans, missing attributes needed for Coval built-in metrics, or when a customer wants maximum debugging and observability value from agent traces.

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Recommend, create, preview, and attach Coval trace-based metrics from OpenTelemetry spans. Use when a user has Coval traces and wants custom trace metrics, trace-aware LLM judge metrics with include_traces, latency, token usage, provider failures, tool behavior, vertical-specific workflow signals, or production monitoring based on trace attributes.

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Live SEO data via DataForSEO MCP server: SERP analysis, keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis, competitor and content analysis, business listings, AI visibility (LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "AI visibility check", or "real search data".

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Find and identify the CDecoyProjectile_Create factory in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the decoy projectile spawn factory. It is a non-virtual, direct-call static factory found via the entity classname string "decoy_projectile" it references (excluding the RTTI class string "CDecoyProjectile"). Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CDecoyProjectile_Create.py preprocessor (xref_strings FULLMATCH:decoy_projectile). Trigger: CDecoyProjectile_Create, CDecoyProjectile::Create, decoy projectile Create

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Find and identify the CCSGameRules_PostCleanUp function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the post-round entity-cleanup routine. It is non-virtual and has no unique string of its own, so it is found via the CCSGameRules_ResetRound predecessor: PostCleanUp is the entity-cleanup call ResetRound makes. Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSGameRules_PostCleanUp.py preprocessor (LLM_DECOMPILE CCSGameRules_ResetRound). Trigger: CCSGameRules_PostCleanUp, PostCleanUp, CGameRules::PostCleanUp

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Convert an existing find-XXXX SKILL.md into a preprocessor Python script, updating configs/<GAMEVER>.yaml and removing the old SKILL.md. Covers xref-string-based and LLM_DECOMPILE-based discovery patterns.

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Create a new find-XXXX preprocessor Python script from scratch (no existing SKILL.md), add configs/<GAMEVER>.yaml skill and symbol entries. Covers xref-string-based and LLM_DECOMPILE-based discovery patterns. Use when a GitHub issue or user instruction specifies a new function to find.

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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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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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Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

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Design and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.

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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.

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Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration

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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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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.

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.

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。

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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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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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.

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。