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 agentmods add skills/sharpai/deepcamera/dataset-managementnpx skills add SharpAI/DeepCamera --skill dataset-managementgit clone --depth 1 https://github.com/SharpAI/DeepCameraWhat 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 | $0.00019 | $0.00565 |
| Opus 5 | $0.00010 | $0.00282 |
| Sonnet 5 | $0.00004 | $0.00113 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
annotation-data 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 yesterday.
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
Annotation Data Management
Manages annotation datasets for Aegis Annotation Studio. Handles dataset CRUD, label management, COCO-format export, and Kaggle upload.
Protocol (stdin/stdout JSONL)
Aegis → Skill
{"command": "list_datasets", "request_id": "req_001"}
{"command": "get_dataset", "name": "my_dataset", "request_id": "req_002"}
{"command": "save_dataset", "name": "my_dataset", "labels": [...], "request_id": "req_003"}
{"command": "delete_dataset", "name": "my_dataset", "request_id": "req_004"}
{"command": "save_annotation", "dataset": "my_dataset", "frame_id": "f1", "annotations": [...], "request_id": "req_005"}
{"command": "list_labels", "dataset": "my_dataset", "request_id": "req_006"}
{"command": "export_coco", "dataset": "my_dataset", "request_id": "req_007"}
{"command": "get_stats", "dataset": "my_dataset", "request_id": "req_008"}
{"command": "stop"}
Skill → Aegis
{"event": "annotation", "type": "ready", "request_id": "", "data": {"version": "1.0.0"}}
{"event": "annotation", "type": "datasets", "request_id": "req_001", "data": [...]}
{"event": "annotation", "type": "dataset", "request_id": "req_002", "data": {...}}
{"event": "annotation", "type": "saved", "request_id": "req_005", "data": {"frame_id": "f1", "count": 3}}
{"event": "annotation", "type": "exported", "request_id": "req_007", "data": {"path": "/path/to/coco.json"}}
What ships with it
4 files 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.
- yesterday First seen · 52 lines · 19 tokens per session scan A c9d454dd5d48
annotation-data is a skill published in the GitHub repository SharpAI/DeepCamera (3,025 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 565 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 2026-08-30.
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