Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/aksheyw/career-command-center-template/evaluatenpx skills add aksheyw/career-command-center-template --skill evaluategit clone --depth 1 https://github.com/aksheyw/career-command-center-templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aksheyw/career-command-center-template/evaluate)<a href="https://agentmods.dev/skills/aksheyw/career-command-center-template/evaluate"><img src="https://agentmods.dev/badge/skills/aksheyw/career-command-center-template/evaluate.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00028 | $0.00679 |
| Opus 5 | $0.00014 | $0.00340 |
| Sonnet 5 | $0.00006 | $0.00136 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
evaluate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 62 lines · 28 tokens per session scan A e3a3f1534109
evaluate is a skill published in the GitHub repository aksheyw/career-command-center-template (1 stars, last pushed 6d ago), licensed MIT. It adds 28 tokens to every session and 679 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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