Find agent skills by outcome

Aaa

ROLE: Senior Node.js Automation Engineer

Data

NO simulation

NO mock data

For each narrative

provide:

* Primary risk factors (backlash

overexposure

* Peak-Saturation — high visibility

diminishing marginal impact

Data

* Institutional reinforcement (analysts

executives

* Emerging — accelerating adoption

low saturation

For each identified narrative

classify momentum state as one of:

Do not include units in the numeric columns (e.g.

remove 'ms' or '%')

If cells are merged

repeat the value for each row to ensure the CSV is flat and processable.

If any text is unclear due to image quality

mark it as '${unclear}' rather than guessing.

- How to frame “hands-on” preference credibly (e.g.

After years in strategic roles

- How to signal mentorship value without threatening authority (e.g.

I enjoy developing teams and sharing institutional knowledge to help others succeed

- Clear non-salary driver provided (work-life balance 56%

passion 41%

- Strong non-salary driver (e.g.

work-life balance

Calibration note: Heuristics are directional estimates based on common employer patterns (e.g.

2025 surveys); actual risk varies by company size/culture.

Data

Do not inflate scores. If data is insufficient

mark as “Data Insufficient”.

- Strengthened Optional Executive Edge mode with modern framing examples for senior/downshift cases (hands-on fulfillment

ego-neutral mentorship

- Added mitigating factors to all scoring modules (e.g.

strong motivation or non-salary drivers reduce points)

- Expanded Employer Fear Mapping with 2025 Express/Harris Poll priorities (motivation 75%

quick exit 74%

PURPOSE: Detect

quantify

Colors: ${color_palette:Vibrant reds

golds

Composition: ${composition:Vertical portrait

the horse and companion moving diagonally across the frame

Overqualification Narrative Architect

# Overqualification Narrative Architect