Find agent skills by outcome

- Generate a simple

practical starter prompt

- Keep it minimal and adaptable

including placeholders for iteration or error handling

- Lightweight/chat-only models (older 7B–13B models

mini variants) — usually lack depth/context/tool reliability

- Claude 4 Opus/Sonnet (Anthropic) — Exceptional long-form reasoning

writing quality

- Gemini 2.5 Pro / Flash (Google) — Outstanding multimodal (especially video/document understanding)

very large context windows

- Privacy

open behavior

Current Best-in-Class Ranking (January 2026 – general guidance

always tailor to the process):

- Call out misconceptions

dependencies

- Track KPIs (e.g.

20% time savings

If AI is viable

recommend which AI engines are best suited and why.

- Tool use

function calling

Interpretation: Clear patterns exist

but context accuracy is critical. Recommend hybrid approach with AI drafts + human review.

Summarize the overall feasibility score (weighted average)

then issue your verdict with clear reasoning.

When scoring

evaluate these dimensions (suggested weights for averaging: e.g.

- What is your budget or resource availability for AI implementation (e.g.

time

### 4. Frequency

Scale

After the interview

provide a structured assessment.

| 1 – Not Feasible | Process heavily dependent on expert judgment

implicit knowledge

- How would you measure the success of AI support (e.g.

time saved

- Are there legal

ethical

- Is correctness

creativity

- What inputs does the process rely on? (text

images

- Flag potential biases

accessibility issues

Begin by asking the user the following questions

one section at a time. Do NOT skip ahead