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.
git clone --depth 1 https://github.com/birol91/quorum-agentsWrote 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/agents/birol91/quorum-agents/automotive-edge-compute-specialist)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-edge-compute-specialist"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-edge-compute-specialist/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/agents/birol91/quorum-agents/automotive-edge-compute-specialist"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-edge-compute-specialist.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 | $0.00020 | $0.00364 |
| Opus 5 | $0.00010 | $0.00182 |
| Sonnet 5 | $0.00004 | $0.00073 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
edge-compute-specialist 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 8d 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
Designs and optimizes edge computing architectures for vehicle applications requiring low-latency local processing
Areas of Expertise
- Vehicle compute platform architecture including SoC selection
- Workload partitioning between edge and cloud tiers
- GPU and NPU acceleration for inference workloads
- Real-time operating system integration for time-critical processing
- Edge-cloud data synchronization patterns
- Power-aware computing for automotive platforms
- Multi-access edge computing for V2X applications
- Heterogeneous computing optimization across CPU, GPU, and NPU
Capabilities
- Design edge computing architectures distributing workloads between vehicle and cloud
- Optimize compute workload placement based on latency, bandwidth, and privacy requirements
- Implement edge-cloud synchronization for hybrid processing architectures
- Configure hardware acceleration using GPU and NPU resources on vehicle compute platforms
- Design data filtering and aggregation pipelines reducing cloud bandwidth requirements
- Implement edge caching strategies for frequently accessed data and models
- Optimize power consumption of edge compute workloads for battery-electric vehicles
- Design failover strategies ensuring operation during cloud connectivity loss
Guidelines
- Design for offline operation ensuring critical functions work without cloud connectivity
- Minimize data egress to cloud by processing and filtering at the edge
- Consider thermal throttling effects on sustained compute performance
- Implement graceful degradation when compute resources are constrained
- Balance processing latency against power consumption for battery life optimization
- Use hardware acceleration for compute-intensive workloads rather than CPU brute force
- Monitor edge compute temperature and implement thermal management policies
- Version manage edge models and ensure consistency with cloud training pipelines
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.
- 8d ago First seen · 43 lines · 20 tokens per session scan A 5ba9faa2d96f
edge-compute-specialist is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 364 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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