---

name: analysis-workflow description: "Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data as references instead of copies, and verifies outputs before completion." license: Apache-2.0 wisp: schema_version: 1 domains: [bioinformatics] research_stages: [observation, analysis, validation] roles: [analyst, validator] evidence_types: [project-data, omics, computational] outputs: [analysis-module] side_effects: code_execution

Reproducible Analysis Modules

Use this skill for a scientific workflow with two or more analysis stages or when a stage produces scripts plus result files. It defines project organization and methods capture; load figure-style as well whenever a stage creates or revises a plot.

1. Plan module boundaries

Before writing outputs, list the modules and the dependency edges between them. Use stable ASCII names. Conventional acronyms such as QC, PCA, DEG, and GSEA may stay uppercase; otherwise prefer a short kebab-case name.

Respect a compatible layout that already exists. Do not reorganize unrelated user files merely to impose this convention.

2. Default module layout

Create only directories the module actually needs:

<module>/
├── scripts/
├── input/
├── output/
│   ├── figures/
│   └── tables/
└── README.md

• scripts/ contains the executable source for this module.
• input/ contains small module-specific inputs or a manifest/reference to the canonical data. Do not duplicate a large dataset by default.
• output/figures/ contains rendered figures from this module only.
• output/tables/ contains machine-readable results from this module only.
• README.md is the module's reproducibility record and methods source.

Shared immutable/raw data may live in project-level data/. A downstream module references an upstream output by a project-relative path; it does not silently copy or rename that output.

3. Make outputs attributable

Every output must have one producing script or recorded command. Use deterministic filenames that identify the analysis and content. Keep temporary files outside the final output directories or name them clearly as temporary.

Script persistence and process lifetime are separate concerns. Wisp's python and r runtimes retain variables and loaded objects across calls and can execute saved scripts. shell and run_in_context execute commands in fresh processes. Choose according to the user's workflow, state reuse, script requirements, and task lifecycle, using the selected environment in either case.

When an analysis depends on an expensive object already loaded in a Python or R runtime:

• keep the reproducible analysis in a project-local .py or .R file;
• execute that file with the python/r tool's script_path in the same runtime, declaring the input bindings with required_objects;
• keep heavyweight loading in a separate bootstrap script or explicit loader cell; analysis scripts consume the loaded object and must not reload it;
• use run_in_context, python file.py, or Rscript only for a deliberately fresh, state-independent batch execution.

For standalone execution, record the script path and exact command. For runtime execution, record the script path and returned source hash/runtime generation in the module README. For clean-room replay, an optional batch wrapper may load the data once and then call the same analysis functions; it is not the default hot-iteration path.

Before completing a module, verify:

1. every declared output exists and is non-empty;
2. every table can be parsed in its declared format;
3. every figure was rendered and visually inspected using figure-style;
4. README input and output paths resolve from the project root;
5. reported thresholds and parameters match the actual script.

4. Update README.md at module completion

Create or update these sections:

# <Module>

## Purpose
<scientific question and role in the workflow>

## Inputs
- `<project-relative path>` — source, upstream module, checksum or version when available

## Methods
<method in prose, including transformations, statistical tests, correction method,
thresholds, seeds, and other result-changing parameters>

## Software and data sources
- R/Python package: exact version
- External API/database: release or access date
- Wisp/model/runtime metadata: exact recorded value when available

## Commands and scripts
- `<project-relative script>` — how it was executed

## Outputs
- `<project-relative path>` — meaning and format

## Limitations
<assumptions, exclusions, and unresolved reproducibility gaps>

Write methods from executed code and recorded parameters, not from a generic template. Do not claim a package, database, model, OS, or version that was not actually used or observed.

5. Capture exact versions without dumping the world

Record direct dependencies used by the module:

• R: packageVersion("<package>") for named packages and sessionInfo() for the runtime context.
• Python: importlib.metadata.version("<distribution>"); use the project lock file when it is the authoritative environment record.
• External databases/APIs: release identifier when available, otherwise access date plus endpoint/source.
• Wisp version and model profile: use runtime/session metadata only when it is available. Write unavailable rather than guessing.

Do not paste an entire global pip freeze into every module. If a complete environment export is useful, save it once as a separate artifact and link it from the README.

6. Finish the workflow

After all modules pass their checks, summarize the dependency chain and link the module READMEs. Treat those READMEs as the first-version source of truth. Generate a root METHODS.md only when the user asks for it or a deterministic project tool can derive it from the module records; do not maintain a second hand-edited copy that can drift.