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 matteotitta/genesys-skills --skill company-contextgit clone --depth 1 https://github.com/matteotitta/genesys-skillsWrote 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/matteotitta/genesys-skills/company-context)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/company-context"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/company-context/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/matteotitta/genesys-skills/company-context"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/company-context.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 | $0.00024 | $0.01888 |
| Opus 5 | $0.00012 | $0.00944 |
| Sonnet 5 | $0.00005 | $0.00378 |
| Haiku 4.5 | $0.00002 | $0.00189 |
Grade A, and why
company-context 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 9d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company context
Extract firmographics, traction signals, funding, team composition, tech stack, hiring activity, and decision-makers for a target company. Produces a markdown artifact with qualification score (0-25), ICP fit assessment, red flag analysis, and optional Apollo account brief. Output drops into any client or prospect folder and feeds discovery prep, competitor research, positioning, and proposal scoping.
When to run
Invoke for "company research", "company background", "qualify this prospect", "discovery call prep", "account brief", or whenever the user provides a company URL for research.
Do NOT invoke for competitor analysis (/competitor-research), product messaging extraction (/messaging), ICP personas (/icp-behavioural), or casual website checks.
Skill chain: this is a root/gateway skill. Common downstream chains in the premium reference.
Inputs
Required: Company identifier — website URL, LinkedIn URL, or company name. If name is ambiguous (e.g., "Atlas", "Beam"), confirm with user before proceeding.
Optional (improve quality):
- LinkedIn company URL — sharper team size + org structure
- Specific questions — focus research on areas of interest
- Discovery call date — adds urgency context
Substrate: Exa-first per .claude/rules/exa-protocol.md. Primary tools company_research_exa and web_search_exa. MCP fallback chain in the premium reference. Cite per ontology: [VERIFIED: exa_search, {url}, accessed {YYYY-MM-DD}].
Apify bulk-mode fallback (added 2026-05-01)
Imported via: /steal analysis 2026-05-01 (.claude/discovery/0526-apify-linkedin-actors-steal-analysis.md).
For ABM-scale company-context sweeps (>50 accounts in one run), Apollo's per-credit cost compounds — and Apollo doesn't index every company in the long tail. Bulk fallback:
| Tool | Use case | Cost |
|---|---|---|
dev_fusion/Linkedin-Company-Scraper |
Bulk LinkedIn company URL → firmographics (name, industry, size, website, employee count, description, specialties) | $8/1k flat |
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.
- 9d ago First seen · 133 lines · 174 tokens per session scan A 6d2bd137c2da
company-context is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 1,888 once invoked, about $0.0001 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 2026-09-03.
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