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Found 11,954 Skills
Create and configure AI agents, upload files for RAG, manage MCP servers, and handle agent memories using the Cargo CLI. Use when the user wants to create or update agents, upload knowledge base files, connect MCP tool servers, or manage agent memories. For sending messages to agents, use the cargo-orchestration skill instead.
Used when an Agent needs to control OpenTeam via the local openteamcli: create AI group chats, add temporary roles, publish tasks, wait for replies, read results, or continue existing group chats.
Guide for adding a new benchmark or training environment to NeMo-Gym. Use when the user asks to add, create, or integrate a benchmark, evaluation, training environment, or resources server into NeMo-Gym. Also use when wrapping an existing 3rd-party benchmark library. Covers the full workflow: data preparation, resources server implementation, agent wiring, YAML config, testing, and reward profiling (baselining). Triggered by: "add benchmark", "new resources server", "integrate benchmark", "wrap benchmark", "add training environment", "add eval".
Mission control Kanban board for managing and supervising autonomous Hermes Agent tasks
Integrate Hermes Agent as a managed AI employee in Paperclip companies with full tool access, persistent memory, and skill sync
Execute the /integrate command for LLM agents. Triggers when the user types `/integrate`, `/integrate --product`, or asks to "integrate a Juspay product", "set up payments", "add payment SDK", or any variation of setting up a Juspay product into their app or codebase. This skill drives a fully guided, doc-driven wizard: it reads product summaries locally, probes candidates via MCP, then fetches actual documentation pages and generates complete integration code.
Propose and execute rubric or bucket upgrades. Two modes: **Full rubric bump** (highest-risk action, mandatory 5-step process + cross-model audit) and **--bucket-only lightweight recalibration** (only update bucket boundaries, no changes to rubric formulas). **Phase 2 mandates using cheat-score-blind sub-agent to re-score the calibration pool** — self-scored fallback is not accepted. Trigger phrases: "upgrade rubric"/"bump rubric"/"update formula"/"I want to add a dimension"/"adjust weights"/"recalibrate bucket"/"recalibrate bucket".
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.
This skill is used when users want to add QQ platform support to the official main branch of Hermes Agent, or explicitly mention requests like "add QQ channel to hermes main", "install QQ support as a skill to Hermes", or "enable the official version of Hermes to support QQ and file sending". This skill will update the current repository to a version that supports QQ Bot, QQ file sending, QQ platform configuration, and toolset integration.
Reference guide for CLI-Anything, which auto-generates production-ready agent-controllable CLI harnesses for any GUI application via a 7-phase.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Build agentic UIs using AG-UI protocol with Pydantic AI (Python backend) and CopilotKit (React/Next.js frontend). Use when creating AI-powered applications that need bidirectional agent-UI communication, shared state between frontend and backend, human-in-the-loop workflows, tool-based generative UI, or predictive state updates. Triggers on requests involving CopilotKit hooks (useCoAgent, useCopilotAction, useCoAgentStateRender), pydantic_ai with ag_ui adapters, or building chat interfaces with backend AI agents.