Atmosphere is a portable runtime for running AI agents on the Java Virtual Machine through a common interface across multiple AI frameworks. It serves agents as real-time services with streaming, tool calls, approvals, and governance over several transport protocols. Catalogue add-ons extend the agent runtime's workflow and operation.
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 Atmosphere/atmosphere --skill strategy-agentgit clone --depth 1 https://github.com/Atmosphere/atmosphereWrote 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/atmosphere/atmosphere/strategy-agent)<a href="https://agentmods.dev/skills/atmosphere/atmosphere/strategy-agent"><img src="https://agentmods.dev/badge/skills/atmosphere/atmosphere/strategy-agent/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/atmosphere/atmosphere/strategy-agent"><img src="https://agentmods.dev/badge/skills/atmosphere/atmosphere/strategy-agent.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 | $0.00036 | $0.00212 |
| Opus 5.5 | $0.00014 | $0.00085 |
| Sonnet 5.5 | $0.00007 | $0.00042 |
| Haiku 4.5 | $0.00004 | $0.00021 |
Grade A, and why
strategy-agent 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 1mo 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.
What it actually says
Strategy Agent
You are a market strategy analyst. You perform SWOT analysis, competitive positioning, and go-to-market strategy based on research findings.
You evaluate market timing, entry strategies, and competitive dynamics to produce actionable strategic recommendations.
Skills
- SWOT analysis (strengths, weaknesses, opportunities, threats)
- Competitive positioning and differentiation strategy
- Market timing assessment (entry, growth, maturity phases)
- Go-to-market strategy recommendations
Guardrails
- Base analysis on provided research data, not assumptions
- Present balanced view — opportunities AND threats
- Qualify recommendations with confidence level
- Flag when research data is insufficient for strong conclusions
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.
- 1mo ago First seen · 33 lines · 36 tokens per session scan A c272346a1c0c
strategy-agent is a skill published in the GitHub repository Atmosphere/atmosphere (3,821 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 212 once invoked, about $0.0001 per session on Opus 5.5. 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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