Job Skill Gap Upskill Planner

Upskill

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Overview

/upskill analyses jobs you have tracked and your current profile to identify skill gaps, then produces a heatmap of those gaps and a learning plan with concrete, web-searched study resources and a recommended study order.

Invocation

• /upskill — aggregate mode: analyses all jobs in job_search_tracker.csv, merged with ranked postings (rank_score >= 45) from job_scraper/seen_jobs.json
• /upskill <URL> — targeted mode: analyses a single job posting fetched from the URL

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Step 1: Detect Mode

Check whether the user provided a URL argument:

• If the invocation was /upskill with no argument → aggregate mode
• If the invocation was /upskill <URL> → targeted mode, store the URL for Step 2

In targeted mode, derive a slug from the job title and company for the report filename (e.g. guardsix-senior-ai-engineer). You will fetch the posting in Step 2.

Step 2: Load Data

Aggregate mode

1. Read job_search_tracker.csv. Extract all rows. The columns are: date, company, sector, role, role_type, channel, status, contact_person, fit_rating, notes, cv_file, cover_letter_file, source, deadline
2. For each row, note the role, company, and fit_rating. The fit_rating column is a 0–100 score where 100 = perfect fit. You will use it to weight gaps — a lower fit rating means the role exposed more gaps.
3. Read job_scraper/seen_jobs.json. Keep entries with "status": "ranked" and rank_score >= 45 — the Moderate Fit floor from 04-job-evaluation.md (below that, a job is Weak/Poor Fit and would otherwise dominate the heatmap with jobs the user shouldn't chase). For each kept entry, note its title, company, rank_score, and — when present — its recorded gaps. An entry with no gaps field (ranked before gap persistence existed) is skipped, counted, and reported once in the terminal: "N ranked jobs were scored before gap persistence and contribute nothing; /rank --all re-scores them." Never back-fill a missing gaps field by guessing from the title.
4. Read .claude/skills/job-application-assistant/01-candidate-profile.md to get the candidate's current skills and experience.
5. Check upskill/ for the most recent aggregate report file (report-YYYY-MM-DD.md) — if one exists, note its date and load it for the diff in Step 8.

Targeted mode

1. Use WebFetch to retrieve the job posting from the URL.
2. Extract: job title, company, required skills, preferred skills, responsibilities, and any domain context.
3. Read .claude/skills/job-application-assistant/01-candidate-profile.md for the candidate's current skills.
4. No tracker data is used in targeted mode.

Step 3: Pass 1 — Hard Skill Diff

Extract required and preferred technical skills from each job source:

Aggregate mode

This mode now merges two sources — tracker rows (Step 2.1) and ranked postings from seen_jobs.json (Step 2.3) — so the same job is never double-counted and recorded gaps are preferred over inferred ones:

1. Dedupe. Match tracker rows against ranked entries on case-insensitive company + role (casefold + strip on both fields) — the same match /notion-sync's Step 2 describes. A job present in both counts once.
2. Recorded gaps beat inferred skills. For any job that has a recorded gaps array (from a ranked entry, or from a tracker row that matched one), use those gap bullets directly as the skill list for that job instead of inferring from role/sector/notes. For a ranked-only job with no gaps (already skipped and counted in Step 2.3) or a tracker-only row, fall back to inferring likely required skills from role, sector, and notes — optionally WebFetch the row's source URL for more detail, but skip if the URL is missing or dead.
3. One weight per job, both 0–100 on the same scale: (100 - fit_rating) / 100 for tracker rows, (100 - rank_score) / 100 for ranked-only rows. If a job is in both (Step 3.1 matched it), prefer the tracker's numeric fit_rating for the weight. A blank or non-numeric fit_rating (rows /outcome creates for applications made outside the workflow never got a fit evaluation) contributes no weight: fall back to a matched ranked entry's rank_score when Step 3.1 found one, otherwise skip the row, count it, and report the count once in the terminal — the same treatment Step 2.3 gives a missing gaps field, and for the same reason. Never treat a blank as 0: that reads as weight 1.0, the maximum, and lets the one job the framework knows nothing about dominate the heatmap.
4. Score. Build a skill frequency map: for each extracted skill (recorded gap bullet or inferred skill), count how many jobs mention it, then multiply each job's contribution by its weight from Step 3.3. Track whether each contribution came from a recorded gap or an inferred one, for Step 5's provenance column.

Final score for each skill: sum of (weight × occurrence) across all jobs.

Targeted mode

Extract the explicit required and preferred skills from the fetched posting. Each skill gets equal weight (no fit weighting needed since there is only one job). List required skills before preferred skills, then sort alphabetically within each group.

Diff against profile

Remove any skill from the list that is already present in the candidate profile (01-candidate-profile.md). Be generous — if the profile mentions a skill in any form (e.g. "Python" covers "Python scripting"), remove it.

What remains is the hard skill gap list. In aggregate mode, rank by score descending. In targeted mode, list required skill gaps before preferred skill gaps, then sort alphabetically within each group.

Step 4: Pass 2 — LLM Synthesis

Now reason holistically about gaps that the hard skill diff would miss. Consider:

• Domain knowledge gaps: Does the candidate lack familiarity with the industry, domain, or problem space the jobs operate in? (e.g. cybersecurity, climate tech, quantitative finance)
• Soft skill gaps: Do the job descriptions emphasise ways of working, communication styles, or leadership expectations that the profile does not address?
• Tooling and process gaps: Frameworks, cloud services, methodologies (e.g. MLOps practices, CI/CD, agile at scale) that appear across jobs but are absent from the profile
• Credential or certification gaps: If multiple postings list a certification as preferred, flag it

Tag each synthesised gap as one of: [domain], [soft], [tooling], or [credential].

Do not duplicate gaps already captured in Pass 1. Only add what was missed.

In targeted mode, treat all synthesised gaps as arising from a single posting. Credential gaps can still be flagged if the single posting lists them as preferred or required.

Step 5: Build Gap Heatmap

Combine Pass 1 and Pass 2 results into a single prioritised table. Assign priority as follows:

• Critical: Hard skills with high frequency/weight scores, or domain gaps that appear across most tracked jobs
• High: Hard skills with moderate scores, or soft/tooling gaps that appear consistently
• Medium: Lower-frequency hard skills, or synthesised gaps that appeared in fewer roles
• Low: One-off mentions or minor nice-to-haves

Format (aggregate mode's Gap Source cell shows provenance — how many contributions were recorded gaps from Step 3's merge vs. inferred from role/sector/notes):

In targeted mode, the Gap Source cell keeps its existing form (e.g. "required" / "preferred" / "LLM synthesis") — provenance only applies where aggregate mode's merge produced it.

Print this table to the terminal as an intermediate output before continuing to the learning plan.

In targeted mode, assign priority based on the job's own language: required skills → Critical or High, preferred skills → Medium, inferred gaps from LLM synthesis → Medium or Low.