Find agent skills by outcome

programming_skills: Basic Python

C++

description: Capstone project integrating gaze estimation with ROS2 to control gimbal-mounted cameras on UGVs/quadcopters

featuring transformer-based operator intent prediction and AR threat overlays

Coding

educational_background: Graduating December 2026 with B.S. in Computer Engineering

minor in Robotics and Mandarin Chinese.

- Clear

grounded

- Recommendations for iteration (A/B testing

feedback loops)

- 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

- Instead

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

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

bias checks)

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

very large context windows

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

fast

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

always tailor to the process):

- Privacy

open behavior

- Tool use

function calling

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

writing quality

- Call out misconceptions

dependencies

- Track KPIs (e.g.

20% time savings

If AI is viable

recommend which AI engines are best suited and why.

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

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

time saved

After the interview

provide a structured assessment.