Structure repetitive data analyses with a five-phase orchestration framework.
npx skills add https://github.com/bcmcpher/my-skills --skill analysis-plan
Please help me install this Agent Skill. Skill: analysis-plan Source: https://github.com/bcmcpher/my-skills/tree/main/plugins/modular-analysis/skills/analysis-plan Command: npx skills add https://github.com/bcmcpher/my-skills --skill analysis-plan
Design and document a repeatable analysis workflow by predefining constants, inputs, and orchestration logic to avoid scope drift and improve reproducibility.
Draft the unit of work sentence and initialize your A/B dimensions to begin outlining the orchestrator.
High-intent search queries and answers about installing and using this skill.
Plan reproducible data analysis workflows by defining constants, data loading, atomic functions, outputs, and an orchestrator. This phased framework prevents scope drift and ensures deterministic results across multi-dimensional parameter sweeps.
Structure a simulation sweep by initializing arbitrary dimensions for parameters and cohorts, then defining a formal orchestrator. This approach scales across dimensions and guarantees maintainable, repeatable implementations for statistical studies.
An orchestration framework improves reproducibility for feature extraction pipelines by enforcing clear contracts and a phased design process. Predefining inputs and orchestration logic prevents scope drift and ensures reliable execution.
Yes, this analysis planning framework supports scripts in any language. It provides a language-agnostic structure for iterative analyses, ensuring reliable implementations across statistical studies and image processing pipelines.
Without a formal orchestration framework, iterative analyses suffer from scope drift and unreliable execution. Lacking predefined constants and clear contracts reduces reproducibility and makes multi-dimensional experiments difficult to maintain.
Start outlining an orchestrator by drafting the unit of work sentence and initializing your A/B dimensions. This establishes the foundational logic needed to scale workflows across arbitrary dimensional combinations.
surus-latGenerate synthetic benchmark datasets as JSONL input-output pairs from task specifications.
bcmcpherRefactor sequential analysis scripts into a five-layer modular architecture with run_one orchestration.
haizelabsConvert raw agent transcripts into structured JudgeInput and ai_annotation data.
4lermanBuild scalable data pipelines with Python, SQL, Spark, and Airflow.
