agenticflow-built-in-credits
AgenticFlow: Credits-First Approach
Primary Philosophy: Use your existing account credits with built-in features
Extension Path: BYOK (Bring Your Own Key) only if unsatisfied or explicitly requested
Goal: Spend your available credits first, upgrade only when needed
When NOT to use this skill
If the user explicitly wants to use external API keys (BYOK) like DALL-E, Stable Diffusion, or OpenAI, use agenticflow-mcp skill instead. If they need specific model recommendations or want to compare available models, use agenticflow-llm-models skill. This skill is for users who want to maximize their existing account credits first.
Orient first
af bootstrap --json
From the response, extract:
More from antongulin/agenticflow-ai-skills
agenticflow-mcp
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.
5agenticflow-agent
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.
5agenticflow-workforce
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.
5agenticflow-llm-models
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 recommended models, upstream canonical models, models to avoid, reasoning configuration, and max_tokens settings.
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