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Evaluate

Skill aksheyw/career-command-center-template/skills/evaluate

Evaluate a JD before applying — 7-block assessment with go/no-go score. Argument: [paste JD text or URL here]From its SKILL.md

Install
npx -y skills add aksheyw/career-command-center-template --skill evaluate

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.8 KB, 647 tokens by cl100k_base, as published. Nobody here has run it

You are evaluating a job description using the 7-block evaluation framework. The user has pasted a JD or provided a URL.

STEP 1: Read required files

  • ${CLAUDE_PLUGIN_ROOT}/skills/resume-customizer/references/evaluation-framework.md — the 7-block rubric and scoring dimensions
  • ${CLAUDE_PLUGIN_ROOT}/skills/resume-customizer/SKILL.md — company-type strategies, verified metrics
  • ${CLAUDE_PLUGIN_ROOT}/skills/resume-customizer/references/star-stories.md — STAR stories for Block F mapping
  • ${CLAUDE_PLUGIN_ROOT}/skills/resume-customizer/references/story-bank-index.md — cross-indexed story selection
  • ${CLAUDE_PLUGIN_ROOT}/references/YOUR_PROFILE.md — the user's verified background

STEP 2: If URL provided, fetch the JD

If the user provided a URL instead of JD text, use WebFetch to retrieve the job description content.

STEP 3: Run all 7 evaluation blocks

Follow evaluation-framework.md exactly:

  • Block A: Classify the role (archetype, seniority, domain, remote, team size, fit level)
  • Block B: Cross-reference every JD requirement against the user's verified experience. Use ONLY metrics from YOUR_PROFILE.md. Flag gaps with mitigation strategies.
  • Block C: Assess seniority match and positioning strategy
  • Block D: Research compensation using WebSearch (Glassdoor, Levels.fyi, Blind, regional equivalents). Cite exact sources with URLs.
  • Block E: List top 5 resume customizations + top 3 LinkedIn changes
  • Block F: Map STAR stories to JD requirements using story-bank-index.md. Flag any story gaps.
  • Block G: Assess posting legitimacy (freshness, company health, red flags)

STEP 4: Score and verdict

Calculate scores for all 6 dimensions (1-5 scale each):

  • CV Match, North Star Alignment, Compensation, Culture, Red Flags, Global Score
  • Compute average
  • Apply threshold: >= 3.5 = GO | 2.5-3.4 = CAUTION | < 2.5 = SKIP

STEP 5: Output the evaluation report

Use the exact output format from evaluation-framework.md. Include:

  • All scores with brief justifications
  • Verdict with reason
  • Key strengths and gaps with mitigations
  • Recommended STAR stories for interview prep
  • Legitimacy assessment
  • Clear next-step recommendation

STEP 6: If CAUTION or SKIP

Explain specifically what would need to change for this to become a GO. Be honest — don't soft-pedal gaps.

RULES

  • Never invent metrics — only use verified data from YOUR_PROFILE.md
  • Never overclaim ownership — follow the accuracy guardrails in SKILL.md
  • Compensation research must cite sources with URLs and dates
  • If posting is older than 60 days, flag it prominently
  • If the role is clearly below the user's target level, note the downlevel risk

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.