Select and configure LLM models for AgenticFlow agents and workforces. Use this skill whenever the user asks which model to use, needs reasoning capabilities, wants fast/cheaper options, gets finish_reason=length errors, or asks about model speed/quality/intelligence trade-offs. Covers the top 5 recommended models, models to avoid, reasoning configuration, and max_tokens settings.
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Install skill "agenticflow-llm-models" with this command: npx skills add antongulin/skillsmp-antongulin-antongulin-agenticflow-llm-models
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Related by shared tags or category signals.
Use AgenticFlow's built-in features and account credits first — before adding external API keys (BYOK). Use this skill whenever the user asks about image generation without API keys, wants to use their existing credits, asks about built-in vs BYOK, or mentions agenticflow_generate_image, web_search, web_retrieval, or credit-efficient workflows. BYOK is only for extension when unsatisfied or explicitly requested.
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.
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.