agenticflow-ai-skills

Create, run, and iterate on a single AgenticFlow AI agent — one chat endpoint, one assistant, one persona. Use when the user wants a customer-facing bot, a support assistant, a single task agent, or a prompt experiment. Choose this skill over agenticflow-workforce when there's no orchestration between roles (no handoff, no coordinator → workers). Covers `af agent create/update/run/delete`, the `--patch` partial-update pattern for iteration, `af schema agent --field <name>` for nested payload shapes (including suggested_messages, mcp_clients, response_format), the `model_user_config` / `code_execution_tool_config` settings, and safe iteration loops.

Attach external tool providers (Google Docs, Google Sheets, Slack, Notion, GitHub, Apify, etc.) to an AgenticFlow agent via MCP clients. Use when the user wants their agent to read or write external data, call third-party APIs, save outputs to a doc/sheet, or use any tool beyond the model's built-in knowledge. Covers `af mcp-clients list --name-contains`, `af mcp-clients inspect --id` (classify pattern before attach), and the Pipedream vs Composio write-capability distinction — critical for parametric writes. Route traffic through the `af` CLI; the standalone `agenticflow-mcp` server repo lags the CLI and is not recommended.

Purchase and manage AgenticFlow AI built-in credits — the prepaid balance that covers agenticflow-agent and agenticflow-workforce runs. Covers `af credit purchase/receipts/usage/plans`, plan selection, payment card setup via `af setup`, usage monitoring (daily/user/agent breakdowns), auto-recharge configuration, and handling common credit errors.

List, filter, and recommend LLM models available in the AgenticFlow AI workspace for use in agents and workforce nodes. Should trigger whenever the user mentions LLM models, choosing a provider/model for an agent, model capabilities (reasoning, speed, cost), model selection for specific tasks, or understanding which models are available and their trade-offs. Use the live `af get /models` as the authoritative source. The rest of this skill provides recommendations and context but never overrides the live response.

Deploy and operate a multi-agent AgenticFlow workforce — a DAG of agents that hand off to each other (trigger → coordinator → worker agents → output). Use when the user asks for a team, pipeline, or multi-agent system: research-then-write, triage-then-specialist, dev shop, marketing agency, sales team, content studio, support center, Amazon seller team. Choose this skill over agenticflow-agent when the ask mentions 'team', 'workforce', 'pipeline', 'multiple agents', 'delegation', 'handoff', or names a built-in blueprint. Provides the `af workforce *` command surface, blueprint decisions, graph wiring, MCP attach recipes, and public URL publishing.