AI & ML

ckorhonen Bundle

Generate world-class, production-ready prompts in one shot through intelligent 5-7 question flow and 69 presets across 15 professional domains. Supports XML/Claude/ChatGPT/Gemini formats with quality validation. Returns a single mega-prompt ready to use—skill does NOT implement the work described in the prompt. Optimized for modern LLMs including Claude 3.5+, GPT-4.1, and Gemini 2.5.

ckorhonen Bundle

Optimize CoreML models for iOS and macOS deployment. Covers quantization, palettization, pruning, Neural Engine targeting, compute unit selection, and performance profiling. Use when converting ML models to CoreML, optimizing model size/latency, debugging Neural Engine issues, or benchmarking on-device inference.

adelaidasofia Skill

Use when many related hardened instincts (high-confidence feedback_*/discovery_* memories) have piled up in one domain, when the user runs /evolve or asks to promote, cluster, graduate, or consolidate accumulated instincts into a Command, Skill, or Agent, or when files in Instinct Proposals need review. Part of the Instinct Engine. Not for one-off pattern capture (use patterns), daily journaling, or sharing instinct packs (use instinct-export / instinct-import).

datasciencemonkey Bundle

MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.

datasciencemonkey Skill

Generate synthetic PDF documents for RAG and unstructured data use cases. Use when creating test PDFs, demo documents, or evaluation datasets for retrieval systems.

ckorhonen Skill

Battle-tested engineering principles for AI coding agents. Covers plan-first workflow, subagent delegation, self-improvement loops, verification gates, elegant solutions, autonomous bug fixing, and structured task management. Use when configuring agent behavior, writing AGENTS.md files, or improving agent reliability and code quality.

adelaidasofia Skill

Use when the user says /health-setup or /setup-health, asks to set up or connect a wearable (Apple Watch, Apple Health, Oura Ring, Fitbit, Garmin, Whoop), asks how to import Oura / Fitbit / Apple Health data, asks which wearable to use, hits a failing health_vendor_healthcheck, a 429 rate limit, or an expired Fitbit token during setup, or has health-mcp installed with an empty body track. Not for querying already-imported data or building new connectors.

adelaidasofia Skill

Use when the user runs /health doctor or /health status, asks whether the health auto-chain is working, when the last Oura, Fitbit, Apple Health, or labs sync happened, whether the coach prescribed today, why a workout or prescription didn't appear in the calendar, why wearable data looks stale or missing, whether an out-of-range lab marker needs a re-test, or wants to confirm health-mcp hook wiring after first-time setup.

redkenrok Skill

Teaches how to write effective LLM.md files - condensed, practical reference documents that give AI assistants the context they need to use a library effectively without searching through full documentation. Use when creating library documentation for AI consumption.

ahgraber Bundle

Use when a Python change is about to be called done (before commit or PR) and whenever asked to review, audit, sanity-check, or clean up Python code or judge whether it meets standards. Also use at decision points the defaults cover: choosing retry/timeout/idempotency policy, config loading, pydantic placement, module boundaries, or a concurrency model.

ahgraber Bundle

Use when ongoing work or conversational context must continue in a new conversation, session, or agent with no shared history. Triggers: 'handoff', 'prepare context for next chat', 'summarize for a new session', 'continue this in another conversation'. Not for same-chat status updates or repository documentation.

thangchung Bundle

Handle coffee shop order submissions end-to-end: receive customer requests, check available menu items, create and confirm the order, process updates or special instructions, and escalate to a human staff member when necessary. Activate this flow when the user presents an ordering scenario or asks you to role-play as a counter service agent.

thangchung Skill

Converts PDF files to Markdown using Microsoft's markitdown package. Use this skill when the user asks to convert a PDF to Markdown, extract text from a PDF, or read/parse PDF content.

thangchung Skill

Help users explore menu items, categories, and prices. MCP-first, CLI fallback.

thangchung Skill

Best practices, tips, and gotchas for Entra ID, Entra Agent ID, A2A protocol, MCP protocol, and agentgateway-based OBO token exchange — distilled from loop-runtime's entra_agent_id*.md docs. Use when setting up, wiring, or debugging Entra app registrations, Entra Agent ID blueprints, OBO chains, A2A/MCP auth, app-code Agent Identity OBO, Microsoft.Identity.Web.AgentIdentities, fmi_path, or agentgateway jwtAuth/oauthTokenExchange config. ALWAYS remind the user in chat (don't silently assume) when a real value is needed for: tenantId, webAppId, execAppId, execAppSecret, checkAppId, checkAppSecret, mcpAppId, executorAgentIdentityAppId, checkerAgentIdentityAppId.

thangchung Bundle

Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions.

thangchung Skill

Resolve customer identity and account basics by email or customer ID. MCP-first, CLI fallback.

thangchung Skill

Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that relates to software development (.NET/C#).

langwatch Skill

Maintain the canonical LangWatch feature map (/feature-map.json). Use when adding features, APIs, MCP tools, CLI commands, or skills — to update the central registry and keep surfaces in sync.

langwatch Bundle

Validate a feature works by driving a real browser with Playwright MCP. No test files — just interactive verification.

langwatch Skill

Version and manage your agent's prompts with LangWatch Prompts CLI. Use for both onboarding (set up prompt versioning for an entire codebase) and targeted operations (version a specific prompt, create a new prompt version). Supports Python and TypeScript.

langwatch Skill

Generate realistic synthetic evaluation datasets by analyzing the user's codebase, prompts, production traces, and reference materials. Interactive and consultant-style. Asks clarifying questions, proposes a plan, generates a preview for approval, then delivers a complete dataset uploaded to LangWatch. Use when user asks to generate, create, or build a dataset for evaluation, testing, or benchmarking.

langwatch Skill

Take your AI agent to the next level with full LangWatch integration. Adds tracing, prompt versioning, evaluation experiments, and simulation tests in one go. Use when the user wants comprehensive observability, testing, and prompt management for their agent.

langwatch Skill

Test your AI agent with simulation-based scenarios. Covers writing scenario test code (Scenario SDK), creating platform scenarios via the `langwatch` CLI against a connected agent, reading the run parameters that agent declares so the scenarios and comparison runs turn its real levers, and red teaming for security vulnerabilities. Auto-detects whether to use code or platform approach based on context.

langwatch Skill

Read LLM traces back from LangWatch with the langwatch CLI. Use when asked what an agent, prompt or model call actually did in production, when debugging a failed or slow LLM run, when looking up a trace or session by id, or when checking whether this coding session's own activity was captured.

langwatch Skill

Compatibility router for LangWatch evaluation requests. Use only when the user asks for evaluations without making it clear whether they mean pre-deployment experiments or production online evaluations. Routes the request to the focused companion skill and does not implement either workflow itself.

langwatch Skill

Create and run LangWatch experiments for pre-deployment batch testing. Use when the user wants to test an agent against a dataset, compare prompts or models, benchmark quality, detect regressions, or add a CI quality gate. Do not use for production monitoring or guardrails.

muhammedalkhudiry Skill

AI agent skill and instruction-file writing and maintenance.

muhammedalkhudiry Bundle

Agent setup audits and improvement recommendations.

ahgraber Bundle

Use when an agent in one harness must create, observe, or steer one or more agents in another harness through a tmux session the user can watch. Triggers: "use an agent in another harness", "bridge to <agent>", "run <agent> in tmux", "use a different harness", "check on the external agent". Not for subagents or delegation inside the current harness (use `subagent-patterns`).

ahgraber Bundle

Use when tasks require current, source-backed technical information from MCP tools. Apply for library/API questions, dependency version checks, third-party integration work, framework- or SDK-specific debugging, and any case where stale model knowledge could cause incorrect guidance.

ahgraber Bundle

Use when the user wants a deep, multi-source, fact-checked web research report on a topic too broad for a single search. Triggers: "deep research on X", "research report", "investigate thoroughly", "comprehensive findings on", "what's the current state of X", "survey the landscape of". Not for: single-fact lookups, library/API/version questions (use mcp-research), or open-ended ideation (use brainstorming).

langwatch Skill

Change the user's own program, on their machine or through GitHub. Use when a request needs a change to the user's code (instrument tracing, wire the SDK, fix the agent behind a failing scenario, add a run parameter to a connected agent, version a hardcoded prompt) and not when the platform alone can do it (create a scenario, an evaluation, a prompt version, read traces).

langwatch Skill

Connect the codebase's AI agent to LangWatch agent simulations, so test suites run against the real agent process. Adds a small connect function beside the service startup that calls the agent already in the codebase, which opens an outbound connection and registers the agent with its environment and its run parameters, confirms the agent is Online, and runs the first test suite. Falls back to an HTTP registration when the agent cannot import the SDK. Use when the user wants platform scenarios to test their real agent.

langwatch Skill

Test that your AI agent stays observational and doesn't give prescriptive advice in regulated domains (healthcare, finance, legal). Creates scenario tests for boundary enforcement and red team tests for adversarial probing. Use when your agent advises but must not prescribe.

langwatch Skill

Investigates the context economics of your own coding-agent sessions in LangWatch. Reads real sessions to find where carrying a fat context stops paying for itself, measured in cache rebuilds, compactions and cost per turn, and delivers a report with the context size your sessions should stay under, with example sessions behind every claim. Use when coding-agent sessions feel expensive or degrade as they grow.