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 AndyZhuang/Opentest --skill egocentric_view_to_structured_loggit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/egocentric_view_to_structured_log)<a href="https://agentmods.dev/skills/andyzhuang/opentest/egocentric_view_to_structured_log"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/egocentric_view_to_structured_log/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/andyzhuang/opentest/egocentric_view_to_structured_log"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/egocentric_view_to_structured_log.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 | $0.00063 | $0.03664 |
| Opus 5 | $0.00032 | $0.01832 |
| Sonnet 5 | $0.00013 | $0.00733 |
| Haiku 4.5 | $0.00006 | $0.00366 |
Grade A, and why
egocentric_view_to_structured_log 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 11d 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 — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Egocentric View to Structured Log
Overview
egocentric_view_to_structured_log transforms raw first-person XR headset footage into a machine-readable experiment timeline. It processes the egocentric video stream frame-by-frame (or at configurable intervals), applies VLM or action-recognition models to infer what the operator did — pipetting, vortexing, adding reagent, loading centrifuge, labeling tube — and emits a structured log with timestamp, action type, object(s) involved, spatial location, and optional result or observation. The output is Markdown (human-readable timeline) or JSON (for programmatic consumption), suitable for ELN attachment, protocol compliance cross-reference, generate_scientific_method_section input, or audit trail documentation in the LabOS "from video to paper" pipeline.
When to Use This Skill
Use this skill when any of the following conditions are present:
- Experiment timeline documentation: A researcher needs a chronological record of what was done during an experiment — "at 14:23, added buffer to tube A1; at 14:25, vortexed; at 14:30, loaded centrifuge" — without manual note-taking.
- ELN or Benchling attachment: An electronic lab notebook entry requires an attached experiment log; the skill produces a Markdown or JSON file suitable for upload.
- Protocol compliance cross-reference: The structured log serves as ground truth for
protocol_video_matching— compare log events against protocol steps to detect deviations. - Methods section provenance:
generate_scientific_method_sectionconsumes the log to document the exact sequence of actions performed, with timestamps and objects. - Post-hoc experiment reconstruction: An experiment failed or produced unexpected results; the log enables step-by-step review to identify potential causes (e.g., "reagent added at 14:23, but protocol says add at 14:20 — 3 min delay").
- Training and assessment: A trainee's run is logged; the timeline is reviewed by a supervisor for feedback on sequence, timing, and technique.
- Audit trail for GLP/GMP: Regulated workflows require a timestamped record of every action; the log provides a structured, tamper-evident audit trail (when combined with video hash).
- Multi-operator coordination: When multiple people work at the same bench, the log can be tagged by operator (if face/ID available) or left anonymous for aggregate timeline.
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.
- 11d ago First seen · 274 lines · 63 tokens per session scan A 71a06a626212
egocentric_view_to_structured_log is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 63 tokens to every session and 3,664 once invoked, about $0.0003 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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