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 landedjobs/ai-job-hunt-os --skill company-researchergit clone --depth 1 https://github.com/landedjobs/ai-job-hunt-osWrote 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/landedjobs/ai-job-hunt-os/company-researcher)<a href="https://agentmods.dev/skills/landedjobs/ai-job-hunt-os/company-researcher"><img src="https://agentmods.dev/badge/skills/landedjobs/ai-job-hunt-os/company-researcher/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/landedjobs/ai-job-hunt-os/company-researcher"><img src="https://agentmods.dev/badge/skills/landedjobs/ai-job-hunt-os/company-researcher.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 | $0.00049 | $0.01040 |
| Opus 5 | $0.00024 | $0.00520 |
| Sonnet 5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
company-researcher 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 12d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Researcher
Produce the brief a well-connected friend would give: what the company actually does, whether it is financially and operationally credible, whether its AI is real, and what to say in the room. Candidates should diligence a startup like a junior investor because they are investing years of their career. High-profile startup failures repeatedly show that famous investor logos are weak evidence; operating artifacts are stronger evidence.
What you need
Company name (plus URL if obscure), the target role, and the stage: about to apply, screen scheduled, or onsite scheduled. Depth scales with stage. If you have web access, research live. If not, say so and ask the user to paste the about page, JD, and recent news, then work from that.
Process
Step 1: The business in plain words
What they sell, to whom, and what the buyer pays for. If you cannot state it in two sentences, that is a finding, and "how do you make money" becomes a great interview question.
Step 2: Survival math (borrow the VC lens)
- Runway estimate: last raise (amount, date) minus elapsed burn. Rough burn proxy: headcount × fully loaded cost, plus compute for AI companies. State it as an estimate with the inputs shown.
- Capital efficiency: if revenue is real, the diagnostic question is "how much net burn per dollar of net new ARR?" Do not apply a universal stage benchmark without a current, comparable source. The user can ask a softened version: "How has the plan changed since the last raise?"
- A company 18+ months past its last raise with no revenue story hires differently than one fresh off a round. Say which pattern this is.
- If cash, burn, or revenue are unknown, write "unknown," recommend the user ask, and never invent runway. Diligence reduces risk; it does not eliminate private-company opacity.
Step 3: The four-artifact AI-washing test
Classify AI-native vs AI-forward vs AI-washed using artifacts, not branding (regulators now fine companies for fake AI claims; candidates should apply the same skepticism):
- Engineering blog: do they publish real technical decisions?
- Eval methodology: do they describe how they measure model quality?
- Model/data ownership: is it honestly stated (own models, fine-tunes, or API layer)? API-layer is fine; hiding it is not.
- Production customer outcome: named customers with concrete results?
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.
- 12d ago First seen · 55 lines · 49 tokens per session scan A 54e06bbc9711
company-researcher is a skill published in the GitHub repository landedjobs/ai-job-hunt-os (1 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 1,040 once invoked, about $0.0002 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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