/career-development

name: career-development
description: "Analyzes skill gaps against your tracked jobs or a single posting, then builds a prioritized learning plan with real, web-searched resources. Activates on: upskill, skill gap, what should I learn, /upskill."
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash
  - Glob
  - Grep
  - WebSearch
  - WebFetch

Purpose

This file is the Plane 1 knowledge anchor for the Career Development workflow (ARCH-0040). As a Plane 1 skill it lives under `.claude/skills/career-development/` and provides structured knowledge — it does not execute binaries. When activated, it compares the candidate's current skills against demand (either every job in the tracker, or one target posting), produces a prioritized gap heatmap, finds real learning resources via web search, sequences them into a study order, and persists a markdown report under `upskill/`. It operates under the no-fabrication rule (ARCH-0007), the read-before-write invariant, and never invents learning resources.

This skill backs the `/upskill` command (`.claude/commands/upskill.md`) and the dashboard's Upskill surface, which reads the reports this skill writes (`upskill/report-*.md`).

Trigger Phrases

• "/upskill" (with or without a job URL)
• "What should I learn for <role/company>?"
• "Where are my skill gaps?"
• "Analyze my upskilling needs"
• Any phrase about skill gaps, learning plans, or closing the distance to a target role

Companion Files

| File | When to Read | |------|--------------| | `../job-application-assistant/01-candidate-profile.md` | Always — the source of the candidate's *current* skills, education, and experience to diff against | | `../job-application-assistant/04-job-evaluation.md` | For the candidate's stated strong/weak areas and career goals, to weight and frame gaps |

This skill also reads, outside the skill directory:

| Source | Mode | Purpose | |--------|------|---------| | `job_search_tracker.csv` (repo root) | Aggregate | Role, company, and `fit_rating` per tracked application | | `upskill/report-YYYY-MM-DD.md` (most recent) | Aggregate | Previous report, for delta comparison (REQ-3010) | | Job posting (URL or pasted text) | Targeted | The single posting to analyze |

---

Workflow

The skill runs the same six analytical stages in both modes; what differs is the input and how priority is computed.

Stage 0 — Mode selection & data loading (REQ-3001, REQ-3002, REQ-3003)

Decide the mode from the invocation:

• **Aggregate** — no argument. Analyzes all tracked jobs.
• Read `job_search_tracker.csv`; for each row extract role, company, and `fit_rating`.
• If the tracker is missing or has only its header row, do **not** write an empty

report — tell the user honestly: *"You have no tracked applications yet. Run `/search` and `/apply` first, or give me a job URL for a targeted analysis (`/upskill <url>`)."*

• Read the candidate profile (`01-candidate-profile.md`) for current skills.
• Glob `upskill/report-YYYY-MM-DD.md` and load the most recent for delta (REQ-3010).
• **Targeted** — `/upskill <url>` or pasted posting text. Analyzes one posting.
• Fetch the posting with `WebFetch`. If the fetch fails, ask the user to paste the

posting text (DEC-011) — never abort.

• Extract: title, company, required skills, preferred skills, responsibilities, domain

context.

• Read the candidate profile. **Do not** read the tracker in targeted mode.

The report's date is today's date (`YYYY-MM-DD`), available from context.

Stage 1 — Pass 1: hard-skill diff (REQ-3004)

Extract required and preferred **technical** skills from the source(s) and diff against the profile.

• **Aggregate:** build a skill-frequency map weighted by fit —

`gap_weight(skill) = sum over postings mentioning it of ((100 − fit_rating) / 100 × occurrence)`. Lower-fit jobs contribute more (the candidate is further from those roles).

• **Targeted:** list required skills before preferred, equal weight, alphabetical within

each group.

• Remove any skill already present in the profile in **any form** — match generously:

"Python" covers "Python scripting"; "AWS" covers "Amazon Web Services".

Stage 2 — Pass 2: LLM synthesis (REQ-3005)

Reason holistically about gaps mechanical diffing misses. Consider and tag each:

• `[domain]` — domain/industry knowledge gaps
• `[soft]` — soft-skill gaps (leadership, stakeholder management, communication)
• `[tooling]` — tooling/process gaps (CI/CD, IaC, agile practices)
• `[credential]` — certifications/credentials. Flag a credential gap only when **multiple

postings** list it as preferred (aggregate) or it is explicitly required (targeted).

No duplication with Pass 1.

Stage 3 — Gap heatmap (REQ-3006, REQ-3011)

Combine Pass 1 + Pass 2 into a prioritized table, printed to the terminal **before** the learning plan:

| Priority | Skill / Area | Type | Gap Source | |----------|-------------|------|------------|

• Priority levels: **Critical · High · Medium · Low**.
• Aggregate: priority follows the fit-weighted frequency score (higher score → higher

priority).

• Targeted: required → Critical/High, preferred → Medium, inferred (Pass 2) → Medium/Low.
• **Low** gaps appear in the heatmap for completeness but receive **no** learning-plan

entry unless the user asks (REQ-3011).

Stage 4 — Learning plan (REQ-3007)

For every **Critical** and **High** gap (and **Medium** gaps too if the total gap count is fewer than 5), produce a learning entry:

• 2–3 resources, each with **name, URL, and a one-line reason**.
• **Resources must come from a real `WebSearch`** — never fabricated (ARCH-0007). Include

the current year in queries so results are fresh.

• Resource preference order: hands-on courses > lecture-only; official docs for tooling;

books for domain knowledge.

• A **study direction** tailored to the candidate's background: what they can skip given

what they already know, and where to start.

• A **time estimate**.
• G