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

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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.
• Group entries by theme (e.g. Cloud & Infrastructure, MLOps, Domain Knowledge).

If a genuine resource cannot be found for a gap, say so plainly rather than inventing one.

Stage 5 — Study order (REQ-3008)

Suggest a numbered, dependency-aware sequence:

#	Topic	Type	Estimated Time	Note (dependencies)

Ordering rules: dependencies first → Critical before High before Medium → quick wins early → domain knowledge last. Show the total estimated time at the bottom.

Stage 6 — Persist the report (REQ-3009) & delta (REQ-3010)

Always write the report, even if the user seems satisfied with the terminal output.

• Aggregate → upskill/report-YYYY-MM-DD.md
• Targeted → upskill/report-YYYY-MM-DD-<company-slug>-<role-slug>.md

Slugs are lowercase, hyphen-separated, alphanumeric. The report contains, in order:

1. Delta (aggregate only, when a previous report exists): Gaps closed — skills from the previous heatmap now present in the profile; New gaps — skills in the current heatmap not in the previous report. Omit this section in targeted mode or when no previous report exists.
2. Gap heatmap (Stage 3)
3. Learning plan (Stage 4)
4. Study order (Stage 5)

Reports are personal output: written under upskill/, never modified after creation, and git-ignored. The dashboard's Upskill surface renders them as-is.

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Contract

• No fabrication (ARCH-0007): every learning resource is real and web-searched; every URL resolves. An empty tracker or unfetchable posting yields an honest message, not a fabricated or empty report.
• Read-before-write: read the profile (and previous report in aggregate mode) before writing anything.
• Generous skill matching: a gap is only a gap if the profile lacks it in every form.
• Modes never cross streams: targeted mode ignores the tracker; aggregate mode does not fetch a posting.
• Career framing: use 04-job-evaluation.md's stated goals and strong/weak areas to frame priorities — a gap that blocks the candidate's stated direction outranks an incidental one.