Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Assist with finding, evaluating, and applying to jobs using multi-source searches, fit scoring, application support, interview prep, and status tracking.
Assist with finding, evaluating, and applying to jobs using multi-source searches, fit scoring, application support, interview prep, and status tracking.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
End-to-end job search assistant โ from finding opportunities to landing interviews.
Create a profile JSON for the user. Use the template at {baseDir}/references/profile-template.json as a starting point. Ask the user about: Target roles and seniority level Key skills and tools Location preferences (cities + remote) Salary expectations Dealbreakers and excluded companies Preferred industries/domains Save as profile.json in the workspace.
Use the web_search tool with multiple queries to cast a wide net: site:linkedin.com/jobs "[role]" "[city]" site:indeed.com "[role]" "[city]" site:glassdoor.com/job "[role]" "[city]" "[role]" "[city]" hiring 2025 2026 Expand keywords โ don't just search one title. See {baseDir}/references/search-strategies.md for keyword expansion patterns. Alternative: run the search script if Brave API is available: {baseDir}/scripts/search_jobs.sh "CX Manager" --location "Amsterdam" --days 7
For each job found, run fit analysis: python3 {baseDir}/scripts/analyze_fit.py --profile profile.json --jobs jobs.json --threshold 50 Or evaluate manually using this framework: Skill match (40%): Does user have 60%+ of required skills? Seniority match (25%): Right level โ not over/under qualified? Location match (15%): Compatible location or remote? Domain match (10%): Preferred industry/domain? Red flags (10%): Excluded companies? Dealbreakers? Score: ๐ข 75+ great | ๐ก 55-74 good | ๐ 40-54 stretch | ๐ด <40 skip
For each job, present: Role & Company with direct link Fit score with color indicator Why it's a match (top 3 skill matches) Gaps to address (missing skills to highlight as "eager to learn") Salary estimate if available Recommendation: Apply / Maybe / Skip
Read {baseDir}/references/cover-letter-guide.md for structure and tone guidelines. Generate tailored cover letters that: Reference specific company details (not generic) Map user's experience to top 2-3 job requirements Include quantified achievements Stay under 350 words
Read {baseDir}/references/interview-prep.md for complete preparation framework. Help with: Company research summaries STAR stories for key requirements Tailored "tell me about yourself" script Salary negotiation talking points Questions to ask the interviewer
bash {baseDir}/scripts/salary_research.sh "Job Title" "Location" Cross-reference 3+ sources. In the Netherlands: factor in 8% holiday allowance, possible 13th month, pension.
When running as a scheduled job search brief: New opportunities โ jobs found in last 24h with fit scores and direct links Application status โ updates on pending applications Action items โ what to apply to today, follow-ups due Market intel โ industry trends, salary movements, hiring patterns
Maintain a job tracker with: Company, role, date found, source URL Fit score and recommendation Status: new โ applied โ screening โ interview โ offer/rejected/ghosted Applied/skipped with reason Contact info and follow-up dates
Never apply on behalf of the user โ present opportunities, let them decide Don't overwhelm โ 3-5 quality matches beat 20 mediocre ones Track excluded companies โ never suggest the same company twice after rejection Be honest about fit โ stretches are okay to flag, but don't oversell poor matches Respect dealbreakers โ if user said no customer service, don't suggest it even if "it's a great company" Update the profile โ as you learn user preferences, refine the profile Celebrate wins โ applied to a job? Got an interview? Acknowledge it
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.