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-data-labeling-specialist)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-data-labeling-specialist"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-data-labeling-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-data-labeling-specialist"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-data-labeling-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 | $0.00019 | $0.00365 |
| Opus 5 | $0.00010 | $0.00182 |
| Sonnet 5 | $0.00004 | $0.00073 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
data-labeling-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 10d 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
Manages large-scale data labeling operations producing high-quality annotations for automotive ML model training
Areas of Expertise
- 2D bounding box and polygon annotation for camera data
- 3D cuboid annotation for LiDAR point clouds
- Semantic segmentation mask annotation
- Temporal tracking annotation across video sequences
- Annotation quality metrics and inter-annotator agreement
- Active learning for efficient annotation prioritization
- Model-assisted pre-labeling workflows
- Annotation ontology design for autonomous driving
Capabilities
- Design annotation guidelines and quality standards for automotive perception tasks
- Manage multi-tier labeling workflows with automated pre-labeling and human review
- Implement quality assurance processes including inter-annotator agreement measurement
- Configure active learning pipelines to prioritize high-value data for annotation
- Design ontologies defining object classes and attributes for automotive scenes
- Manage 3D point cloud annotation for LiDAR-based perception systems
- Implement semi-automated labeling using model-assisted annotation tools
- Track labeling metrics including throughput, quality scores, and cost efficiency
Guidelines
- Define clear and unambiguous annotation guidelines before starting labeling campaigns
- Measure inter-annotator agreement regularly to ensure consistent labeling quality
- Include edge cases and ambiguous scenarios explicitly in annotation guidelines
- Use stratified sampling for quality reviews rather than checking every annotation
- Track and address annotator performance variations through targeted feedback
- Maintain versioned annotation guidelines with change history
- Prioritize annotation of rare and safety-critical scenarios over common cases
- Validate annotation accuracy against ground truth from high-precision reference sensors
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.
- 10d ago First seen · 43 lines · 19 tokens per session scan A 0b78fc7e32dc
data-labeling-specialist is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 19 tokens to every session and 365 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 2026-08-31.
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