Documentation Index
Fetch the complete documentation index at: https://mintlify.com/SudharakaA/ai-job-search/llms.txt
Use this file to discover all available pages before exploring further.
/upskill turns your job search data into a career development plan. It compares what your profile says you know against what the jobs you are tracking actually require, identifies the gaps a keyword diff would miss (domain knowledge, ways of working, tooling practices, credentials), and generates a prioritized heatmap and a concrete learning plan, complete with web-searched resources, study direction tailored to your existing background, and time estimates. Reports are saved to disk so you can track your progress over time.
Syntax
| Mode | Behavior |
|---|---|
/upskill | Aggregate mode: analyzes all jobs in job_search_tracker.csv |
/upskill <url> | Targeted mode: analyzes a single job posting fetched from the URL |
The four-step analysis
Hard skill diff (Pass 1)
Aggregate mode: Extracts required and preferred technical skills from every job row in
job_search_tracker.csv, builds a skill frequency map, and applies a fit weight. Gaps from lower-fit-rated jobs contribute more to the score, since those roles exposed the largest distance between your profile and the requirement. The final score for each skill is the sum of (fit_weight × occurrence) across all jobs.Targeted mode: Extracts explicit required and preferred skills from the fetched posting. Required skills are listed before preferred skills, sorted alphabetically within each group.In both modes, any skill already present in 01-candidate-profile.md is removed from the list. Partial matches count.LLM synthesis (Pass 2)
Reasons holistically about gaps the keyword diff would miss. Four gap types are considered:
- Domain knowledge
[domain]: industry, sector, or problem-space familiarity the jobs assume - Soft skills
[soft]: ways of working, communication styles, or leadership expectations that appear across postings but are absent from your profile - Tooling and practices
[tooling]: frameworks, cloud services, or methodologies (MLOps, CI/CD, agile at scale) that recur in job descriptions - Credentials
[credential]: certifications listed as preferred or required across multiple postings
Gap heatmap
Combines both passes into a single prioritized table and prints it to the terminal before the learning plan is generated. Priority tiers are assigned as follows:
In targeted mode, priority is derived from the job’s own language: required skills map to Critical or High, preferred skills to Medium, and synthesized gaps to Medium or Low.
- Critical: high-frequency hard skills or domain gaps appearing across most tracked jobs
- High: moderate-frequency hard skills or consistent soft/tooling gaps
- Medium: lower-frequency hard skills or gaps appearing in fewer roles
- Low: one-off mentions or minor nice-to-haves
| Priority | Skill / Area | Type | Gap Source |
|---|---|---|---|
| Critical | Kubernetes | Hard | 4/5 jobs, score 3.2 |
| High | Security domain knowledge | Domain | LLM synthesis |
| High | CI/CD pipelines | Tooling | LLM synthesis |
| Medium | AWS (advanced) | Hard | 2/5 jobs, score 1.1 |
Learning plan with web-searched resources
For every Critical and High gap (and Medium gaps if fewer than 5 total gaps exist),
/upskill runs a web search to find current, highly-rated study resources. Queries include the current year to avoid stale results. It selects 2-3 resources per gap (preferring hands-on courses over lectures, official docs for tooling gaps, books for domain gaps), writes a study direction tailored to your existing background, and estimates time to working proficiency.Resources are grouped by theme rather than listed alphabetically. Example themes: Cloud and Infrastructure, MLOps, Domain Knowledge, Security, Soft Skills and Ways of Working, Certifications.Example entry:Suggested Study Order
After the learning plan,/upskill adds a recommended sequence table. The ordering rules are:
- Dependencies first: if topic B requires topic A (for example, “AWS networking” requires “AWS fundamentals”), place A before B and note the dependency
- Critical before High before Medium within a dependency tier
- Quick wins early: a fast Medium gap can be placed early to build momentum
- Domain knowledge last: domain and soft gaps benefit from being studied alongside real projects
Report files
Reports are saved automatically after every run.| Mode | Filename |
|---|---|
| Aggregate | upskill/report-YYYY-MM-DD.md |
| Targeted | upskill/report-YYYY-MM-DD-<company-slug>-<role-slug>.md |
upskill/report-2026-04-20-guardsix-senior-ai-engineer.md
Diff against previous report
In aggregate mode, if a previous report exists inupskill/, /upskill adds a “Since Last Report” section at the top of the new report:
- Gaps closed: skills that were in the previous heatmap and are now present in your profile
- New gaps: skills in the current heatmap that were not in the previous report
/upskill never fabricates study resources. Every resource cited in the learning plan comes from an actual web search result. Queries include the current year to keep recommendations fresh.Related commands
/scrape
Add more jobs to your tracker to improve aggregate gap analysis
/expand
Close gaps by surfacing skills already in your documents and online presence
/setup
Update your profile after completing courses to clear gaps from future reports
/apply
Apply to jobs while your learning plan is in progress