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From career-navigator
Analyzes job application outcome data to identify high-converting strategies, surface search performance signals, and update ExperienceLibrary weights via analyst agent.
npx claudepluginhub tmargolis/career-navigator --plugin career-navigatorHow this skill is triggered — by the user, by Claude, or both
Slash command
/career-navigator:pattern-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Invoke the `analyst` agent to run an outcome pattern analysis on the user's application history.
Benchmarks job search pipeline performance against industry norms for conversion rates, response timelines, ATS scores, and compensation.
Compares 2-4 assessment files from job applications across folders to analyze candidate performance patterns and generate strategic hiring recommendations.
Evaluates job postings (JD text or URL) against your profile with A-F match score, archetype analysis, compensation research, positioning strategy, and interview prep.
Share bugs, ideas, or general feedback.
Invoke the analyst agent to run an outcome pattern analysis on the user's application history.
Read {user_dir}/CareerNavigator/tracker.json. If the applications array is empty or has fewer than 3 entries with a resolved outcome (phone_screen, interview, offer, rejected, or inactive):
"You don't have enough outcome data yet for pattern analysis — I need at least a few applications with known results. Keep logging updates via
/career-navigator:track-applicationand run this again once you have more history."
Otherwise, proceed.
Hand off to the analyst agent with:
CareerNavigator/tracker.jsonartifacts-index.jsonCareerNavigator/ExperienceLibrary.jsonThe agent will:
performance_weights in CareerNavigator/ExperienceLibrary.jsonweight_update_log entry for each changesearch_performance summary to tracker.jsonAfter the agent completes, report what changed:
Pattern analysis complete.
ExperienceLibrary weights updated
{n} unit(s) increased — {role titles, brief rationale}
{n} unit(s) decreased — {role titles, brief rationale}
{n} unit(s) unchanged (insufficient data)
Search performance summary written to tracker.json
Top converting role types: {list}
Top converting industries: {list}
Signals to avoid: {list}
Data confidence: {Preliminary / Directional / Moderate / High} ({n} applications with outcomes)
If weights could not be updated for any unit due to insufficient data, note it. Do not suppress this — the user should know when the dataset is too small to support a change.
"Run
/career-navigator:reportfor the full analyst report, or/career-navigator:tailor-resumeto use the updated weights in your next resume."