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 skills add andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2- --skill evaluategit clone --depth 1 https://github.com/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-Wrote 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/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/evaluate)<a href="https://agentmods.dev/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/evaluate"><img src="https://agentmods.dev/badge/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/evaluate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/evaluate"><img src="https://agentmods.dev/badge/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/evaluate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00075 | $0.01902 |
| Opus 5 | $0.00037 | $0.00951 |
| Sonnet 5 | $0.00015 | $0.00380 |
| Haiku 4.5 | $0.00007 | $0.00190 |
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 13d 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
## A. Executive Summary
| Field | Value |
|---|---|
| **Archetype** | {detected archetype} |
| **Domain** | {industry/sector} |
| **Seniority** | {Entry / Mid / Senior / Lead / Director / VP / C-Suite} |
| **Location** | {city, state or Remote} |
| **TL;DR** | {one sentence: is this worth pursuing and why/why not} |
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.
- 13d ago First seen · 228 lines · 75 tokens per session scan A bfa0d01675ee
evaluate is a skill published in the GitHub repository andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2- (490 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 1,902 once invoked, about $0.0004 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 2026-08-30.
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