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 muggl3mind/career-manager --skill company-researchgit clone --depth 1 https://github.com/muggl3mind/career-managerWrote 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/muggl3mind/career-manager/company-research)<a href="https://agentmods.dev/skills/muggl3mind/career-manager/company-research"><img src="https://agentmods.dev/badge/skills/muggl3mind/career-manager/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/muggl3mind/career-manager/company-research"><img src="https://agentmods.dev/badge/skills/muggl3mind/career-manager/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 | $0.00040 | $0.00767 |
| Opus 5 | $0.00020 | $0.00383 |
| Sonnet 5 | $0.00008 | $0.00153 |
| Haiku 4.5 | $0.00004 | $0.00077 |
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 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Research
Generate a consistent company dossier for job search decision-making.
Trigger
User says: "research [company]", "look into [company]", "what do you know about [company]", or agent identifies a company worth investigating.
Output Format (ALWAYS this structure)
1) Overview
- Name, website, industry
- Size (employees, include numeric estimate/range), stage (startup/public/PE-backed)
- HQ location, remote policy
- Founded, key milestones
- Executives & key contacts table:
| Name | Title | Notes | |
|---|---|---|---|
| CEO | ... | linkedin.com/in/... | Background |
| CTO/VP Eng | ... | ... | ... |
| Hiring Manager (if identifiable) | ... | ... | ... |
- Role-relevant outreach targets (ALWAYS include 2-5 people when available):
| Name | Title | Why relevant to this role | |
|---|---|---|---|
| ... | ... | Hiring owner / cross-functional partner / team lead | ... |
2) Signals
- Glassdoor/employee review snapshot (rating, pros/cons themes, CEO approval)
- Financial health (funding, profitability signals, layoffs/hiring freezes)
- Latest news table (last 90 days):
| Date | Headline | Source | Relevance |
|---|---|---|---|
| ... | ... | ... | High/Med/Low |
3) Fit
- Match to target roles (Y/N + why)
- Comp range estimate
- Culture fit signals
- Growth trajectory
- Recommendation: PURSUE / RESEARCH MORE / PASS
4) Risks
- Top red flags and uncertainty notes
- Data freshness concerns
- Validation gaps (what to verify before applying)
Data Sources (in order)
- web_search (company name + "glassdoor reviews")
- web_search (company name + "funding crunchbase")
- web_search (company name + "news" last 90 days)
- web_search (company name + "executives leadership team")
- Company careers page (for role details)
- target-companies.csv (for existing research)
After Research
These steps happen automatically after every dossier. Do not ask the user.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 85 lines · 40 tokens per session scan A bd1b88e0603c
company-research is a skill published in the GitHub repository muggl3mind/career-manager (24 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 767 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 2026-08-30.
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