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 robot_protocol_step_generatorgit 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/robot_protocol_step_generator)<a href="https://agentmods.dev/skills/andyzhuang/opentest/robot_protocol_step_generator"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/robot_protocol_step_generator.svg" alt="Measured on agentmods" 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 | $0.00062 | $0.03823 |
| Opus 5 | $0.00031 | $0.01912 |
| Sonnet 5 | $0.00012 | $0.00765 |
| Haiku 4.5 | $0.00006 | $0.00382 |
Grade A, and why
robot_protocol_step_generator 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 7d 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 — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robot Protocol Step Generator
Overview
robot_protocol_step_generator bridges human-written protocol documentation and robot-executable code. It ingests natural language descriptions ("Add 50 µL of primer to each well in column A") or PDF/Markdown protocol text, parses them with LLM or rule-based extraction to identify liquid handling parameters (volume, source, destination, well layout), temperature settings, incubation durations, and transfer patterns, and emits either Python code for Opentrons Protocol API or PyLabRobot, or a structured JSON instruction list that can be executed by a generic robot controller. The skill enables rapid protocol translation from SOPs, protocols.io entries, or manuscript Methods sections into runnable automation — reducing the gap between written procedures and automated execution in the LabOS anywhere-lab vision.
When to Use This Skill
Use this skill when any of the following conditions are present:
- Protocol-to-robot translation: A researcher has a written protocol (PDF, Word, Markdown, protocols.io) and wants to run it on an Opentrons OT-2/Flex or PyLabRobot-compatible robot without manually writing Python.
- Natural language protocol input: The user describes a procedure in plain language — "transfer 100 µL from plate 1 column 1 to plate 2 column 1" — and the agent must generate executable steps.
- Methods section to automation: A manuscript Methods section or supplementary protocol is the source; the skill extracts the procedure and produces robot code for replication.
- Protocol variant generation: A base protocol exists; the user requests a variant (different volumes, different plate layout, different dilution scheme) and the skill generates the modified code.
- Deck layout inference: Protocol text describes reagents and plates; the skill infers a reasonable deck layout and labware positions for Opentrons/PyLabRobot.
- Serial dilution or plate replication: Complex patterns (e.g., "1:2 serial dilution across columns 1–8") are parsed and converted to loop-based or explicit transfer sequences.
- Multi-step protocol chaining: A protocol has distinct phases (PCR setup, thermocycling, cleanup); the skill produces a single Python file or JSON with ordered steps for each phase.
- Simulation-first workflow: Generate code for PyLabRobot ChatterboxBackend or Opentrons simulator to validate before running on physical hardware.
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.
- 7d ago First seen · 276 lines · 62 tokens per session scan A 9802f7524ef3
robot_protocol_step_generator is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 5mo ago), licensed MIT. It adds 62 tokens to every session and 3,823 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.
Other skills, from other repositories
neuroskill-bci
Use live BCI cognitive and mood state from NeuroSkill.
ruview-advanced-sensing
Advanced RuView capabilities — RuvSense multistatic sensing (attention-weighted fusion, geometric diversity, persistent field model), cross-viewpoint fusion across multiple nodes, RF tomography (ISTA L1 solver, voxel grids), longitudinal biomechanics drift, pre-movement intention signals, adversarial signal detection…
ruview-applications
Run RuView sensing applications — presence/occupancy, breathing & heart rate, activity & fall detection, 17-keypoint pose estimation (WiFlow), sleep monitoring & apnea screening, environment mapping, Mass Casualty Assessment (MAT), and the 3D point-cloud fusion demo. Use when someone wants to actually do something…
lab-hardware-cad
Design custom laboratory hardware as parametric build123d models and export fabrication-ready STEP, STL, and DXF files - microfluidic chips and molds, optomechanical mounts and breadboard adapters, cuvette and microplate holders, tube racks, animal-behavior rigs, and 3D-printed instrument fixtures. Use when a research…
opentrons-integration
Author, review, migrate, simulate, and troubleshoot official Opentrons Python Protocol API v2 protocols for Flex and OT-2 robots. Use for robot-specific liquid handling, deck and labware setup, pipettes, modules, runtime parameters, liquid classes, and Opentrons App analysis. Use pylabrobot instead when one workflow…
pylabrobot
Develop and review PyLabRobot lab-automation resources, liquid-handling plans, offline simulations, and supported-device integrations. Use for PyLabRobot protocols or API questions; keep physical execution behind an explicit operator safety gate.