Issue 01 · Spring 2026Career Path
← Projects

Project · BUILDING

Career Path Command Center

A full-stack career operating system for tracking projects, evidence, skills, and career-ladder alignment.

— support

A reading of readiness

39/ 60

Career Path Command Center scores strongest when its evidence supports swe bridge, fullstack data engineering, ai engineering prep. Current verdict is support with 39 total points.

Rubric radarCareer alignment 9 of 10, Reviewer clarity 8 of 10, Technical depth 8 of 10, Evidence quality 3 of 10, Portfolio value 3 of 10, Next-step leverage 8 of 10.CAREER ALIGNMENTREVIEWER CLARITYTECHNICAL DEPTHEVIDENCE QUALITYPORTFOLIO VALUENEXT-STEP LEVERAGE988338
Career alignment 9 of 10, Reviewer clarity 8 of 10, Technical depth 8 of 10, Evidence quality 3 of 10, Portfolio value 3 of 10, Next-step leverage 8 of 10
AxisScore (of 10)
Career alignment9
Reviewer clarity8
Technical depth8
Evidence quality3
Portfolio value3
Next-step leverage8

The piece, in three frames

Problem solved

The portfolio spans several directions and needs a system that makes the progression coherent.

Current weakness

Needs a deployed app, database schema, tests, and portfolio-safe demo data.

Next action

Ship a Supabase-backed MVP with scoring and project evidence tracking.

In: swe bridge, fullstack data engineering, ai engineering prep. Status — building.

Tracked next actions

00

No tracked next actions yet.

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Exhibits

02

Featured exhibit

Exhibit · test

Domain tests

Vitest coverage for career domain scoring and repository boundaries.

  1. Implementation plan

    Type · architecture · Public safe

    Backend-first implementation plan with frontend handoff boundaries.

    In: swe bridge, fullstack data engineering.

  2. Domain tests

    Type · test · Public safe

    Vitest coverage for career domain scoring and repository boundaries.

    In: swe bridge.

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What is still missing

  1. Add a public-safe repository link.
  2. Add reviewer-readable documentation.
  3. Add deployment or API-run evidence for full-stack/data-engineering proof.
  4. Add validation or evaluation evidence for AI-engineering preparation.
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Data source · seed

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