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 palashjain95/jobhunter --skill company-researchgit clone --depth 1 https://github.com/palashjain95/jobhunterWrote 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/palashjain95/jobhunter/company-research)<a href="https://agentmods.dev/skills/palashjain95/jobhunter/company-research"><img src="https://agentmods.dev/badge/skills/palashjain95/jobhunter/company-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/palashjain95/jobhunter/company-research"><img src="https://agentmods.dev/badge/skills/palashjain95/jobhunter/company-research.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00067 | $0.00889 |
| Opus 5 | $0.00034 | $0.00445 |
| Sonnet 5 | $0.00013 | $0.00178 |
| Haiku 4.5 | $0.00007 | $0.00089 |
Grade A, and why
company-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 11d 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/company-research
Usage
/company-research <company name>
/company-research <company name> <role title>
Inputs
- Company name and role title (from user)
- Optional: interview type (recruiter-screen / technical / HM / final)
- knowledge/profile.md — to map company priorities to candidate
- knowledge/stories/ — to identify relevant stories
If URL provided, fetch the company website and careers page.
Produce
1. Company Snapshot
What they do (one sentence), business model, stage (headcount, revenue, funding), key customers, founding team.
2. Recent News + Signals (last 90 days)
Product launches, funding, leadership changes, press, controversies. What is the company focused on RIGHT NOW?
3. The Role in Context
Why is this role open? What problem does this hire solve? What does year 1 success look like?
4. Candidate's Angle
Which stories from knowledge/stories/ map to their priorities? What unique insight can the candidate bring?
5. Competitive Landscape
Main competitors, differentiation, biggest threat, industry tailwinds vs headwinds.
6. Culture + Values Intel
Public messaging, Glassdoor/Blind/LinkedIn signals, what the interview process signals about values.
7. Smart Questions to Ask (8-10)
Grounded in actual research:
Q: [question] Why ask it: [what it signals about you] Best moment: [which interview stage / interviewer]
8. "What Do You Know About Us?" Answer
One paragraph. Grounded in research. Ends with why the candidate is excited about THIS moment in the company's journey.
Output Format
## [Company Name] — Intel Brief
### Snapshot
[One paragraph: what they do, business model, stage]
### Recent News (last 90 days)
- [news item — date]
- [news item — date]
**Right now they're focused on:** [key theme]
### This Role
**Why it's open:** [growth/replacement/new initiative]
**Year 1 success:** [what great looks like]
### Your Angle
- Story: "[story name]" maps to their priority of [X]
- Unique insight: [observation about their space]
### Competitive Landscape
| Competitor | Differentiation | Threat Level |
|------------|----------------|--------------|
| [name] | [how they differ] | High/Med/Low |
### Culture Intel
**Green flags:** [positive signals]
**Watch for:** [concerns or unknowns]
### Questions to Ask
1. [question] — *signals [what]*
### "What Do You Know About Us?"
> [One paragraph answer — ready to use]
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.
- 11d ago First seen · 114 lines · 67 tokens per session scan A 4550cf0215b8
company-research is a skill published in the GitHub repository palashjain95/jobhunter (2 stars, last pushed 5mo ago), licensed MIT. It adds 67 tokens to every session and 889 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 2026-08-31.
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