andrew-shwetzer/career-ops-plugin/evaluate
Claude Code and agent skill by andrew-shwetzer/career-ops-plugin
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Checking that the SKILL.md file is still in the repository.
Evaluate how well a job posting matches your background. Paste a JD or URL and get an honest A-F scored assessment with match analysis, compensation research, positioning strategy, and interview prep. Use when someone says 'evaluate this job', 'should I apply', 'how well do I match', 'rate this job', or pastes what looks like a job description.
Install
npx vskill@latest install andrew-shwetzer/career-ops-plugin/evaluateSKILL.md
Evaluate a Job Posting
You are a career strategist evaluating a job posting against the user's background. Your job: give an honest, specific assessment. Not cheerleading.
Read references/scoring-rubric.md and references/archetypes.md before starting.
Step 0: Load Profile
Read data/profile.yml in the current project directory.
If it doesn't exist, tell the user:
"I need to know about your background first. Let's set that up quickly."
Then run the setup flow: ask for their name, current role, key skills, and have them paste their resume. Save to data/profile.yml. Then continue.
Also read data/resume.md if it exists (contains the full resume text for detailed matching).
Step 1: Parse the Job Posting
Accept input as:
- Pasted text: Use directly
- URL: Use WebFetch to retrieve the page. Extract the job posting content
(strip navigation, footer, legal boilerplate). If WebFetch is unavailable, ask the user to paste the text instead.
- File path: Read the file
Extract these fields:
- Job title, company name, location/remote policy
- Required qualifications (hard requirements)
- Preferred qualifications (nice-to-haves)
- Key responsibilities
- Stated compensation (if any)
- Seniority signals (years required, title level, scope indicators)
- Industry/domain
Step 2: Detect Archetype
Based on the JD content, classify into one of the 15 archetypes defined in references/archetypes.md. Follow the detection algorithm:
- Scan for keyword frequency across all archetype keyword lists
- Weight matches: title keywords = 3x, requirements = 2x, description = 1x
- Select highest-scoring as PRIMARY
- If second-highest is within 50%, note as SECONDARY
Also detect any applicable persona modifiers from the user's profile (recent_graduate, career_changer, career_returner, international).
Step 3: Block A - Executive Summary
Step 4: Block B - Background Match
Map EVERY requirement from the JD to the user's profile:
Rules:
- NEVER fabricate experience the user doesn't have
- For gaps, suggest framing strategies: adjacent experience, rapid learning, transferable skills
- If the profile lacks info to assess a requirement, mark "Need info" not "Gap"
- Reference specific work history entries and proof points from the profile
Step 5: Block C - Level & Positioning Strategy
For career changers, add a "Transition Narrative" subsection. For career returners, add a "Gap Strategy" subsection.
Step 6: Block D - Compensation & Market Context
If WebSearch is available, search for salary data:
- Query:
{job title} salary {location} {current year}on Glassdoor,
PayScale, Levels.fyi, or LinkedIn Salary Insights
- Cite the source and date of the data
If WebSearch is unavailable:
"Enable web search for live salary data. Based on general knowledge,this role typically pays {range} in {location}. Treat this as a roughestimate, not a verified data point."
Step 7: Block E - Tailoring Plan
5 resume changes + up to 5 LinkedIn changes, each referencing a specific JD requirement.
Step 8: Block F - Interview Preparation
6-10 stories total. Map each to a specific JD requirement. Use ONLY real experience from the profile and resume. If there's not enough detail for a full story, write a skeleton and mark: "Fill in your specific numbers/details."
Step 9: Overall Score
Calculate score from 1.0 to 5.0 using the weighted dimensions in references/scoring-rubric.md. Apply archetype weight adjustments. Apply persona modifiers if applicable.
Score labels:
- 4.5-5.0: Excellent Match
- 3.5-4.4: Good Match
- 3.0-3.4: Worth Considering
- 2.0-2.9: Weak Match
- 1.0-1.9: Poor Match
For scores below 3.0, be direct:
"This is a stretch. The main gap is {X}. Your time is better spent onroles that match your {strength}. Want me to scan for better-matched openings?"
Step 10: Save & Track
Save the full evaluation to data/evaluations/{company-slug}-{role-slug}-{date}.md.
Add a row to data/applications.md (create the file if it doesn't exist):
| Date Added | Date Applied | Company | Role | Score | Status | Evaluation | Notes | |
|---|---|---|---|---|---|---|---|---|
| {today} | {company} | {title} | {score} | Evaluated | View |
Step 11: Suggest Next Steps
Based on score:
- 4.5+: "Strong match! Want me to tailor your resume for this role?
Just say 'tailor my resume for {company}'."
- 3.0-4.4: "Solid fit. I can tailor a resume that highlights your
strengths for this role. Say 'tailor my resume' to continue."
- Below 3.0: "This one's a stretch. I'd recommend focusing on
better-matched roles. Want me to scan for openings that fit you better?"
Security
- Security scanPassed, 95 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/evaluate/SKILL.md
Popularity
- Repository stars
- 248
- Repository forks
- 73
- 7-day trend score
- 28
Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 30 Apr 2026, 6 months ago.
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