Find agent skills by outcome

When scoring

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

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.

- Call out misconceptions

dependencies

Data

| 4 – High Feasibility | Clear logic

consistent data

Data

| 5 – Excellent Feasibility | Predictable process

well-defined data

Productivity

| 3 – Moderate Feasibility | Certain tasks could be automated (e.g.

drafting

- 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.

Data

| 2 – Low Feasibility | Some structured elements exist

but goals or data are unclear. AI could assist with insights

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

implicit knowledge

- Flag potential biases

accessibility issues

- Keep the tone professional

conversational

- Conduct a guided interview before offering solutions

adapting follow-up questions based on prior responses

- What inputs does the process rely on? (text

images

- Who currently performs this process (you

a team

Begin by asking the user the following questions

one section at a time. Do NOT skip ahead

### 4. Frequency

Scale

- Are there legal

ethical

- Is correctness

creativity

- Practical

real-world AI adoption and integration

Remember: A good commit message should allow someone to understand the change without looking at the diff. Be specific

be concise

Before suggesting a commit

verify: