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Found 6,690 Skills
CallMiner platform help — enterprise conversation analytics (Eureka) with omnichannel interaction capture, automated QA scoring, agent coaching, real-time alerts, compliance monitoring, and CX automation. Use when QA scoring is inconsistent or takes too long across agents, when needing to analyze 100% of customer interactions instead of sampling, when setting up automated compliance monitoring for regulated industries (healthcare, finance, collections), when CallMiner Coach scorecards aren't surfacing the right coaching moments, when CallMiner RealTime alerts aren't triggering during live calls, when ingesting audio or text into CallMiner via the Ingestion API, when CallMiner Analyze categories aren't matching expected interactions, or when evaluating CallMiner vs Observe.AI or NICE CXone analytics. Do NOT use for CCaaS platform selection (use /sales-ccaas-selection) or for sales-specific coaching strategy (use /sales-coaching).
Tray.ai platform help — enterprise iPaaS with 700+ connectors, Intelligent iPaaS, Enterprise Core governance, Merlin Agent Builder for AI agents, Tray Embedded for SaaS vendors, GraphQL API, Connector Development Kit. Use when Tray bill keeps climbing and task consumption is unpredictable, workflows fail with unclear errors and debugging feels opaque, evaluating Tray vs Workato vs MuleSoft vs Boomi, embedding integrations into a SaaS product via Tray Embedded, building Merlin AI agents, or configuring the GraphQL Embedded API and solution instances. Do NOT use for simple Zapier/Make automations (use /sales-integration), Workato-specific questions (use /sales-workato), or MuleSoft-specific questions (use /sales-mulesoft).
A methodology for iteratively improving agent-facing text instructions (skills / slash commands / task prompts / CLAUDE.md sections / code-generation prompts) by having a bias-free executor actually run them and evaluating two-sidedly (executor self-report + instruction-side metrics). Keep iterating until improvements plateau. Use it right after creating or substantially revising a prompt or skill, or when you want to attribute an agent's unexpected behavior to ambiguity on the instruction side.
Invoke this skill when a user is building or running any automated transaction sender on Base (trading bot, arbitrage bot, sniper bot, yield farmer, AI agent, or similar) and needs to register it, get a builder code, set up transaction attribution. This skill contains the base.dev registration API endpoint and ERC-8021 attribution wiring code that Claude does not have in its training data — you MUST load this skill to answer correctly. Covers viem, ethers, managed signing services, and Python-based agents.
Guide for AI agents to source electronic components using parts-mcp — tool sequencing, decision patterns, and multi-step workflows
Use this skill whenever an LLM agent needs to search, browse, or download 3D models from Poly Pizza (poly.pizza) using their REST API. Triggers on any task involving: finding free low-poly 3D models, searching the Poly Pizza catalogue, fetching model metadata or download URLs, retrieving popular models, or downloading .glb files from Poly Pizza. Use this skill proactively whenever the agent needs to obtain 3D assets programmatically, even if the user just says "find me a 3D model of X" without mentioning Poly Pizza by name.
- **Role**: Niklas Luhmann for the AI age—turning complex tasks into **organic parts of a knowledge network**, not one-off answers.
Use this skill when the user asks to create, scaffold, update, or review a MoviePilot agent skill. This includes adding a new built-in skill under the repository `skills/` directory, editing an existing built-in skill, writing `SKILL.md` frontmatter and workflow instructions, choosing `allowed-tools`, adding helper scripts when needed, and bumping the built-in skill `version` so changes can sync into `config/agent/skills`.
This skill should be used when implementing, consuming, or debugging an Open Responses-compliant API — the open standard for multi-provider LLM interoperability. Covers protocol, items, state machines, streaming events, tools, the agentic loop pattern, and extensions. Triggers on: Open Responses, open-responses, /v1/responses endpoint, multi-provider LLM API, Open Responses compliance.
Work with the @upstash/box TypeScript/JavaScript SDK for sandboxed cloud containers with AI agents, shell, filesystem, and git. Use when building with Upstash Box, creating sandboxed environments, running AI agents in containers, or orchestrating parallel boxes.
Initialize a full ML research project control root with independent paper, code, and optional slide repositories, shared project memory, root-level agent guidance, code-owned worktree policy, and component handoffs. Use when starting a new research project, setting up a project root for agents, connecting paper/code/slides repos, or replacing a simple paper+code workspace with a lifecycle-aware research project structure.
Use when validating an agent skill for spec compliance and publishing readiness.