scientific-research-question-generator
Generates a research question from an experiment aim, ensuring it is specific, non-binary, and includes variables.
Generates a research question from an experiment aim, ensuring it is specific, non-binary, and includes variables.
Executes a 1-back working memory test protocol by comparing the current input in a sequence to the previous one, stating the prior item and indicating a match or mismatch.
Use when assessing post-release production health with DORA metrics, root cause analysis, defect prediction, or cross-phase feedback loops in the QCSD Production phase.
Move testing activities earlier in the development lifecycle to catch defects when they're cheapest to fix. Use when implementing TDD, CI/CD, or early quality practices.
Use when enforcing CI/CD quality gates before release, running regression analysis, detecting flaky tests, or assessing deployment readiness in the QCSD Verification phase.
Advanced exploratory testing techniques with Session-Based Test Management (SBTM), RST heuristics, and test tours. Use when planning exploration sessions, investigating bugs, or discovering unknown quality risks.
Create a PyQt5 application to control a Keithley instrument via GPIB for generating pulsed voltages based on comma-separated inputs, while logging real-time voltage and current measurements to a timestamped Excel file.
Unvarnished technical criticism combining Linus Torvalds' precision, Gordon Ramsay's standards, and James Bach's BS-detection. Use when code/tests need harsh reality checks, certification schemes smell fishy, or technical decisions lack rigor. No sugar-coating, just surgical truth about what's broken and why.
Generates a Python script to fine-tune a DistilBert model on a JSONL dataset containing 'question' and 'answer' columns. The script uses manual label mapping (avoiding sklearn), includes progress logging, error handling, and model evaluation.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Sample hello world tool via uloop CLI. Use when you need to test the MCP tool system or see an example of custom tool implementation.
Sample hello world tool via uloop CLI. Use when you need to test the MCP tool system or see an example of custom tool implementation.
Manage project-centric research workflows, claims, evidence, notes, and experiments. Use when the task should be persisted into ResearchClaw's structured research graph instead of remaining a transient chat reply.
Log experiments, list past runs, and compare experiment metadata or metrics. Use when the user wants lightweight experiment tracking inside ResearchClaw.
Manage swamp model data — list data artifacts, view version history, delete expired versions, and run garbage collection. Use when working with swamp model data lifecycle, retention policies, or version cleanup. Triggers on "swamp data", "model data", "data list", "data get", "data versions", "garbage collection", "gc", "data gc", "data retention", "data lifecycle", "version history", "data cleanup", "prune data", "expire data", "ephemeral data".
Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.
Refresh Obsidian dashboard and daily notes from current experiment state
Check status of running autonomous experiment loops