Find agent skills by outcome

- Generate a simple

practical starter prompt

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

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

- Keep it minimal and adaptable

including placeholders for iteration or error handling

- Key risks or dependencies to monitor (e.g.

bias checks)

- Instead

suggest non-AI or hybrid alternatives (e.g.

- Mistral Large 2 / Devstral — Very strong price/performance

fast

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

very large context windows

- Llama 4 / Llama 405B variants (Meta) — Best open-source frontier performance

excellent for self-hosting

- Grok 3 / Grok 4 (xAI) — Excellent reasoning

real-time knowledge via X

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

writing quality

- Tool use

function calling

- Privacy

open behavior

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

always tailor to the process):

- Track KPIs (e.g.

20% time savings

If AI is viable

recommend which AI engines are best suited and why.

- Call out misconceptions

dependencies

| Data quality | 3 | Mostly clean

some inconsistency |

Summarize the overall feasibility score (weighted average)

then issue your verdict with clear reasoning.

Interpretation: Clear patterns exist

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

| 4 – High Feasibility | Clear logic

consistent data

When scoring

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

| 5 – Excellent Feasibility | Predictable process

well-defined data

After the interview

provide a structured assessment.

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

time saved