Why Most People Can’t Benefit from AI Agents – They Don’t Even Know Their Daily Tasks

Problem Identification

Agent products such as OpenClaw gained rapid attention in early 2026, but user adoption quickly declined because most users could not articulate what daily tasks they wanted to automate. Without a clear description of their workflow, users could only engage in chat without deriving efficiency gains.

Solution: Agent Workflow Designer Skill

A Skill named Agent Workflow Designer was created to address this gap. Instead of a static prompt, the Skill conducts a sequential, single‑question interview that progressively extracts a user’s work process and highlights points where an AI agent can add value.

Goal identification : The Skill first asks the primary objective (e.g., improve efficiency, save time, create content).

Role and deliverables : It gathers the user’s role, typical outputs, and the step‑by‑step sequence of a representative task.

Time‑consumption and repetition : It probes which steps consume the most time, are most repetitive, or could benefit from AI assistance.

After the interview, the Skill generates an HTML report containing a work profile, a workflow map, recommended automation tasks, a taxonomy of automation levels, and a concrete plan for the first week.

Example: Content‑Creator Workflow

For a self‑media blogger who wants to speed up content production, the Skill asks:

Where does the process start? (e.g., trend research)

What are the subsequent steps? (script writing, graphics, recording, editing, publishing)

Which steps are most time‑consuming? (trend selection, data gathering, script drafting)

Based on the answers, the Skill recommends three semi‑automated agents:

Trend‑selection agent

Data‑gathering agent

Script‑drafting agent

Each agent’s input and output are defined. For example, the trend‑selection agent receives account positioning, target audience, recent hotspots, and competitor accounts; it collects information, evaluates relevance, generates multiple angles, ranks them by propagation potential, and outputs titles and hooks for the user to select.

Design Principle: Half‑Automatic → Fully Automatic

The Skill follows a “half‑automatic then full‑automatic” principle. Initial automation targets low‑risk tasks such as data collection, information整理, draft generation, and competitor monitoring—tasks with low failure cost and easy human review. Once these stages are stable, the workflow can be upgraded to multi‑tool orchestration, automatic triggers, and scheduled monitoring.

Open‑Source Release

The complete Skill—including role definitions, interview rules, workflow decomposition method, automation‑level taxonomy, and HTML report format—is released on GitHub:

https://github.com/xuanyuanzhifeng/agent-workflow-designer

Installation consists of feeding the Skill description to any capable LLM‑agent (e.g., Claude, Codex) and invoking it. Users can also adapt the Skill for other industries by modifying the role templates and interview flow.

Broader Implications

The core insight is that the lasting value of AI agents lies in translating human work into explicit, automatable processes. Prompt engineering improves answer quality, but workflow design determines where AI can genuinely participate. Users must define desired outcomes, input sources, intermediate steps, audit points, fallback strategies, and output formats before an agent can be effective.

Code example

来源丨
经授权转
自
轩辕的编程宇宙（ID：xuanyuancoding）
作者丨
轩辕之风