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 researchgit 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-/research)<a href="https://agentmods.dev/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/research"><img src="https://agentmods.dev/badge/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/research/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-/research"><img src="https://agentmods.dev/badge/skills/andrew-shwetzer/career-ops-plugin-do-not-fork-currently-updating-v2-/research.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 | $0.00064 | $0.00828 |
| Opus 5 | $0.00032 | $0.00414 |
| Sonnet 5 | $0.00013 | $0.00166 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
research 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Research
Build an intelligence brief on a target company.
Step 1: Gather Data
Use WebSearch to find:
- Company basics: What they do, size, founded, HQ, funding/revenue
- Recent news (last 6 months): Product launches, layoffs, acquisitions, leadership changes, funding rounds
- Culture signals: Glassdoor rating + recurring themes, any "best places to work" lists or notable controversies
- Team/department: Who leads the department you'd join? Likely hiring manager? Team size?
- Tech/tools/methodology: What does this team use? (Check job postings, tech blog, team member LinkedIn profiles via web search)
If WebSearch is unavailable:
"I can share what I know about {company}, but for the latest info (recent news, Glassdoor reviews, team changes), enable web search in your settings. Here's what I can tell you from general knowledge:"
Then provide what you know, clearly labeled as potentially outdated.
Step 2: Find Contacts
Search for likely hiring contacts:
- Hiring manager (head of the relevant department)
- Recruiter (search "{company} recruiter {department}")
- Team members (potential peers for informational outreach)
For each contact found: Name, Title, and where you found them.
Note: Do NOT scrape LinkedIn profiles directly. Use web search results and public company pages only.
Step 3: Check for Existing Evaluation
Read data/evaluations/ for any evaluation at this company. If found,
reference it to add context to the brief.
Step 4: Output
## Company Brief: {Company Name}
### Overview
| Field | Detail |
|---|---|
| **Industry** | {industry} |
| **Size** | {employee count range} |
| **Founded** | {year} |
| **HQ** | {location} |
| **Revenue/Funding** | {if available} |
| **Website** | {URL} |
### Recent News (Last 6 Months)
- {headline} ({source}, {date})
- ...
(If no news found: "No major recent news found.")
### Culture Snapshot
**Glassdoor:** {rating}/5 ({review count} reviews)
**Positive themes:** {what employees like}
**Negative themes:** {common complaints}
**Work style:** {remote/hybrid/in-office, hours culture}
### Key Contacts
| Name | Title | Source |
|---|---|---|
| {name} | {title} | {where found} |
### Interview Intelligence
- **Company values/mission:** {what they emphasize}
- **Current priorities:** {what they're working on now}
- **Smart questions to ask:**
1. {question based on recent news or strategy}
2. {question about team/culture}
3. {question about role's impact}
- **Topics to handle carefully:** {any sensitive items}
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 · 107 lines · 64 tokens per session scan A 53553abf99bd
research 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 64 tokens to every session and 828 once invoked, about $0.0003 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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