research-viz

name

research-viz

description

Reproducible Python research-visualization workflow for tabular data (csv/xlsx/tsv/txt). Produces a self-documenting `visualizations/` folder with a pandas+numpy parser (quality checks, missing-data handling), a matplotlib+seaborn plot generator, and a streamlit/altair interactive viewer — each behind a small shell wrapper. Use this skill whenever the user wants to "visualize / explore / plot / chart / dashboard" a dataset, asks for "research visualizations", to "make a streamlit app from this data", to "clean up this CSV and chart it", points at a `data/` folder of tabular files, says "summarize this dataset visually", or hands over an .xlsx/.csv/.tsv and wants to see what's in it. Also use whenever the user opens a folder that already contains `visualizations/info/context.md` — the skill is designed to be resumable across agent sessions via that context file.

research-viz

Build (and resume) a reproducible Python research-visualization workspace against a tabular dataset. The deliverable is always a visualizations/ folder next to the user's data/, with three callable entry points (parse_input.sh, generate_plot.sh, interactive_page.sh) and human + agent-readable context in visualizations/info/.

There are six subskills inside this skill, and any single user request usually triggers one of them. Each lives in its own folder under agents/<name>/AGENT.md — read the matching AGENT.md before running the subskill.

Subskill	Trigger	Read
parser	"parse / clean / load / read this", or any other subskill is requested but intermediate_data/ is empty	agents/parser/AGENT.md
plot_gen	"scatter of …", "violin per …", "correlation heatmap", "small multiples of …", "make me a figure"	agents/plot_gen/AGENT.md
interactive	"let me filter by …", "make a dashboard", "I want to brush / zoom", "show me a streamlit app"	agents/interactive/AGENT.md
style_infer	user uploads a paper / figure / brand guide and says "match this style", or specifies palette/typography/plot-type preferences in plain text	agents/style_infer/AGENT.md
significance_test	user asks for "t-test of X vs Y", "ANOVA across groups", "stat test", "p-values", "is this difference significant"	agents/significance_test/AGENT.md
domain_viz	user asks for visualizations outside the standard chart types and pointing at a specialized python package — EEG/fMRI/brain (mne, nilearn), molecular (py3Dmol), networks (networkx+pyvis), gene tracks (pyGenomeTracks), etc.	agents/domain_viz/AGENT.md

Pick the subskill from the user's request. If the user is starting from raw files, run parser first; plot_gen and interactive both consume the parser's outputs (selected via intermediate_data/parsed_index.json's canonical_csv field) by default. If a visualizations/info/style_guide.md exists (produced by style_infer), every plot_gen and interactive run must read and follow it before generating anything new. significance_test and domain_viz are typically next-step prompts you offer the user, not things you run unprompted.

Hard rule: bake every project-specific decision into the .py files

The python scripts you produce are the project's recipe. After your session ends the user must be able to run bash visualizations/parse_input.sh, bash visualizations/generate_plot.sh --all, and bash visualizations/interactive_page.sh and reproduce byte-for-byte the same artifacts you produced — without re-prompting any agent, without remembering CLI flags, without copy-pasting --strategy '{"col":"median"}' from chat.

This means every choice you made during the session must be written into the python file as the default, not passed as a runtime flag. Specifically:

• Imputation strategies the user agreed to: edit parser.py's PROJECT_STRATEGIES dict so bash parse_input.sh runs non-interactively and applies them automatically.
• Custom dtype coercions, drop rules, datetime parsing, unit conversions, column renames: write them into parser.py (a clearly-named function near the top, e.g. apply_project_specific_cleaning(df)).
• Intermediate folder reorganization (when data/ is awkward and you laid out a cleaner tree under intermediate_data/): encode the mapping in parser.py (e.g. a PROJECT_REORGANIZE function that maps each source path → desired parsed path), so the same reorganized layout falls out on every rerun.
• Plot recipes: every plot the user accepted goes into plot_gen.py's PROJECT_RECIPES dict (slug, prompt, data source, hue/palette/log-scale/figsize, etc.). Wire them up so bash generate_plot.sh --recipe <slug> reruns one and --all regenerates the lot.
• Streamlit pages: each accepted exploration is a .py file under streamlit/pages/ with the project-specific filters / charts / colors hardcoded. No runtime configuration needed.
• Colors / themes / styling: define a project palette near the top of plot_gen.py (or in helpers/utils.py) and reference it everywhere — don't sprinkle hex codes inline.

The scaffolding under assets/scaffolding/ is a starting template, not the final state. Different projects will end up with very different parser.py and plot_gen.py contents — that's the point. When the user later wants to change something (a different imputation strategy, a new plot, a different palette), an agent edits the python files; the user keeps using the same shell wrappers.

While a session is in progress: feel free to run things on the user's behalf via direct python calls or by passing flags. But before you wrap up, migrate every choice into the python files so the wrappers are self-contained going forward.

Hard rule: trim the python files to only what this project uses

The scaffolded parser.py and plot_gen.py are starting templates that contain branches for every supported case (every plot kind, every missing-data strategy, multi-file modes, the prompt grammar parser, etc.). The version you deliver must not.

Once the project's behavior is settled:

• Delete every code path that doesn't run for this project. If the user only ever wants scatter plots, remove the violin / box / hist / line / heatmap branches from build_plot() (and the prompt-grammar lookup that picks between them — replace it with a direct call). If only median and drop_row are ever used, drop the unused branches in apply_strategy(). If the data is always a single file, remove the --combine plumbing entirely.
• Delete the unused imports that fall out of those removals (re, seaborn-when-only-matplotlib-is-used, etc.). Run the script once to confirm nothing's referenced after the trim.
• Inline what no longer needs to be configurable. If PROJECT_RECIPES ends up with one recipe, the recipe-dispatch / --recipe / --all machinery is overkill — collapse to a single function and have the wrapper call it. The user's mental model should be: "this script does the thing I want, and the code inside is exactly the code that produced the thing."
• Drop the boilerplate "this is a starting point" docstrings that referenced the original scaffold. Replace them with a short docstring describing what this parser/plot_gen actually does for this project.

The principle: a human reading scripts/parser.py should see a tight, project-specific script — not a generic library with most of the code unreachable for their dataset.

Hard rule: comment the surviving code

Every function that remains, and every non-trivial step inside it, gets a concise comment explaining what it's doing for this project specifically. Aim for one short line per logical step — enough that a non-expert can follow what's happening without reading pandas docs.

Good (concise, project-specific):

# Drop rows where the patient gave no consent (consent column is "Y"/"N").
df = df[df["consent"] == "Y"]

# Convert visit_date from "DD-MM-YYYY" strings to datetime; rows that fail to parse
# are kept as NaT and handled later by the drop_row strategy on visit_date.
df["visit_date"] = pd.to_datetime(df["visit_date"], format="%d-%m-%Y", errors="coerce")

Bad (generic, what-not-why):

# convert column to numeric  ← obvious from the call
df["age"] = pd.to_numeric(df["age"])

Bad (overlong essay):

# Here we use pd.to_datetime which is pandas' standard datetime parser. It accepts
# a format argument that follows the strftime conventions, see the pandas docs at
# ...
df["visit_date"] = pd.to_datetime(...)

Comment the intent and why this dataset needs it, not the syntax. The combination of "trimmed code" + "explains what's happening per step" is what makes the file readable as a project recipe rather than a generic tool.

Hard rule: be honest about what you can see

Several subskills (style_infer, the screenshot-feedback loop in plot_gen / interactive) involve looking at images. If the model running this skill doesn't have multi-modal capabilities, say so plainly and don't hallucinate image content. Concretely:

• When the user uploads a paper / figure / screenshot for style_infer: still copy the file verbatim into visualizations/info/style_refs/, but tell the user honestly you can't read it. Ask them to describe the relevant style features (palette, typography, plot kinds), or offer to exit the subskill.
• When the user sends a screenshot of an existing visualization with feedback ("upper right corner is wrong", "more padding in the middle"): if you can see images, look at the actual screenshot before interpreting the feedback — don't reason from the user's text alone. If you can't see images, say so and ask the user to describe what's wrong, or offer to exit.
• Never guess at what's in an image. A frank "I can't see this — please describe it or exit" is the right response, not a confident hallucination.

Hard rule: data/ is read-only

The skill never writes to, renames, or deletes anything inside data/. data/ is the user's source of truth — treat it as read-only at every step. All cleaned, reshaped, imputed, or otherwise transformed outputs go into intermediate_data/.

If the layout under data/ is awkward (e.g. mixed file types in one folder, opaque names like final_FINAL_v3.csv, or one giant flat dump that should be grouped by subject/session), do not reorganize the source. Instead, design a cleaner layout inside intermediate_data/ and write the transformed outputs there. Note the mapping (source → reorganized path) in info/context.md and in parsed_index.json (each per-file entry already records source_file and parsed_path).

Naming convention for intermediate outputs

Every file the parser (or any later transform) writes into intermediate_data/ must use a meaningful suffix that names the kind of transformation applied, joined to the source dataset name with a double underscore:

<original_dataset_name>__<stage>.<ext>

Examples (tabular case — the default):

• penguins__parsed.csv — basic load + quality check + missing-data strategies applied
• penguins__long.csv — pivoted from wide to long
• penguins__imputated.csv — imputation pass distinct from __parsed
• penguins__zscored.csv — standardized columns
• penguins__filtered_adults.csv — row filter applied

The extension follows the format the data actually needs. CSV is the right choice for the tabular data the standard parser handles, but for domain_viz workflows the format is whatever the domain package expects — forcing everything into CSV is silly and lossy when an established binary format exists. Use the native format and keep the same naming pattern:

• subj01__parsed.fif — mne Raw / Epochs / Evoked written via .save(...)
• subj01__alpha_band.fif — band-passed and saved as fif
• bold_run1__resampled.nii.gz — nilearn 4-D NIfTI image after resampling
• complex_AB__aligned.pdb — aligned protein structure
• cells__filtered.h5ad — scanpy AnnData
• volume__downsampled.zarr — napari / zarr chunked array

Pick the format the next step in the pipeline expects to load — never force a round-trip through CSV unless that's genuinely the cleanest representation. Record the format choice (and the reason, if non-obvious) in info/project_specific_knowledge.md and ensure each entry in parsed_index.json lists the actual parsed_path with extension.

Never use generic names like parsed_results.csv or cleaned.csv. The dataset prefix lets a human (or a future agent) glance at the folder and immediately see where each file came from; the suffix tells them what was done. When several stages stack on the same dataset, chain suffixes only if it materially helps (penguins__parsed__long.csv); otherwise keep the latest stage as the suffix and rely on parsed_index.json for the lineage.

The two reserved names produced automatically by the bundled tabular parser are:

• <dataset>__parsed.csv — per-file output, one per input file (mirrors data/ layout, or a better layout you chose for intermediate_data/).
• combined__parsed.csv — only when the user explicitly asks to stack files (--combine concat or --combine both); has a __source__ column tagging each row's origin.

---

The big picture: this skill is resumable

A user may share their visualizations/ folder with another agent (or come back to it themselves a week later). That folder is the source of truth, not this conversation. The info/ folder is the handoff layer — context.md is the recent-activity log, style_guide.md carries visual decisions, project_specific_knowledge.md carries domain learnings, how_to_use.md is the human-facing guide. Read all of them at the start; update the relevant supporting files during the work; write context.md last as the closing entry.

Be cautious: users edit files outside the agent loop. The context file can drift from reality. The protocol below handles that.

Step 0 — Resumption protocol (run before doing anything else)

1. Locate ./visualizations/info/. If the whole visualizations/ doesn't exist, it's a fresh start — skip to Step 1 (Pre-flight).
2. Read every .md in info/ before doing anything else. The set you'll find:
   • context.md — recent activity log, project-at-a-glance, drift signals.
   • style_guide.md — always present (the scaffold ships a placeholder template). If the file's "Status" line says 🟡 Placeholder, no style decisions have been recorded yet and plot_gen is using its defaults; if 🟢 Active, follow the palette / typography / plot-type preferences + per-plot overrides exactly. Read this before writing or modifying any plot_gen / streamlit code, regardless of whether context.md flags it.
   • project_specific_knowledge.md (if present) — domain-specific things a previous agent learned (e.g. how to use mne for EEG topomaps). May reference info/knowledge/<topic>.md for longer notes — read those if the user's request touches that topic.
   • how_to_use.md — the human-facing guide. Skim so you don't contradict it; update it when the wrappers' behavior actually changes.
3. Quick on-disk reconciliation: ls visualizations/scripts/, ls visualizations/plots/, ls visualizations/intermediate_data/, ls visualizations/streamlit/pages/, ls visualizations/significance/ (if present), ls visualizations/info/style_refs/ (if present), ls visualizations/info/knowledge/ (if present) — and check data/ for new or removed files. Compare to what context.md claims.
4. If on-disk state diverges from context.md (new files, missing files, modified scripts), surface the drift to the user in one short message before acting: "context.md says X was the latest plot, but I see Y/Z on disk and parser.py was modified — should I (a) update context.md to match reality, (b) re-run parsing, (c) ignore?" Then proceed based on the answer.

While the work proceeds: update the relevant info/*.md as you go — style_guide.md when the user expresses a styling preference, project_specific_knowledge.md when you learn something domain-specific, how_to_use.md when behavior of the wrappers changes. Don't batch these to the end; they're work-product, not just documentation.

At the close of the session: write context.md last — append one concise entry per meaningful action (parser ran, plots produced, streamlit app updated, style_guide built or revised, significance test added, project_specific_knowledge appended). See assets/scaffolding/info/context.md for the format and tone. A future agent should be able to reconstruct what was done and where to find it from context.md alone, then load the supporting info/*.md files for depth.

---

Step 1 — Pre-flight: folder structure

The expected layout is:

.
├── data/                       (the user's raw files — provided)
└── visualizations/             (this skill produces this)
    ├── README.md
    ├── info/
    │   ├── context.md                    (recent activity log — written last)
    │   ├── how_to_use.md                 (human-facing guide)
    │   ├── style_guide.md                (always present — placeholder template until style_infer fills it in)
    │   ├── style_refs/                   (always present; ships with a README; fills with verbatim reference papers / figures / brand guides when style_infer runs)
    │   ├── project_specific_knowledge.md (created by domain_viz when a non-standard package was learned)
    │   └── knowledge/                    (only when project_specific_knowledge.md gets too long)
    ├── parse_input.sh
    ├── generate_plot.sh
    ├── interactive_page.sh
    ├── scripts/
    │   ├── parser.py
    │   ├── plot_gen.py
    │   └── helpers/utils.py
    ├── intermediate_data/      (writable; data/ is never modified)
    │   ├── parsed_index.json   (always; manifest of what got parsed)
    │   ├── <dataset>__parsed.csv         (one per input file; suffix names the stage)
    │   ├── <dataset>__long.csv           (example follow-on transform)
    │   ├── combined__parsed.csv          (only with --combine concat / both)
    │   └── <subdirs mirroring data/, OR a cleaner layout you designed if data/ is awkward>
    ├── plots/                  (subfolder per plot prompt)
    ├── significance/           (created by significance_test — .txt tables of test results)
    └── streamlit/
        ├── index.py
        └── pages/

Confirm before scaffolding (raise these questions with the user — concise, prefer recommendations over open-ended asks):

• Where is the data? Is there a data/ directory at the cwd? If yes, use it. If no, ask the user where their raw files live (single absolute path or a list). Do not guess.
• Pilot mode? If data/ is large or contains many sessions/patients/clients, suggest the user split out a small pilot_data/ (a handful of representative files) and point the skill at that first. The wrappers honor DATA_DIR=..., so a typical workflow is: scaffold against pilot_data/, iterate on plots/dashboards until they look right, then re-run with DATA_DIR=$(pwd)/data. Surface this option whenever the data dir looks heavy (>~1 GB or hundreds of files) — don't force it on small datasets.
• Existing visualizations/? If yes, follow Step 0 (resumption); do not clobber.
• If fresh: create the full tree by running python <skill-path>/scripts/scaffold.py <target-dir> --data-dir <data-path>. The scaffold script copies templates from assets/scaffolding/ and seeds the docs (README.md, info/context.md, info/how_to_use.md) with the data path. Read scripts/scaffold.py if you need to vary behavior.

---

Step 2 — Pre-flight: Python environment

Decide the env before writing or running any Python.

1. Detect the active interpreter and version: which python3 && python3 --version. If a venv is already active in the current shell ($VIRTUAL_ENV) or there's a project-local .venv/, prefer that.

2. Check whether the packages the chosen subskill needs are importable, and what version is installed. Don't just check presence — APIs in our stack (streamlit kwargs, seaborn ci= vs errorbar=, altair add_selection vs add_params, statannotations test signature, etc.) drift across releases, and an ImportError at write-time is much cheaper than a TypeError at the user's runtime. The minimum sets:

   • parser: pandas, numpy, openpyxl (for xlsx)
   • plot_gen: pandas, numpy, matplotlib, seaborn (+ statannotations if a recipe overlays significance brackets)
   • interactive: pandas, streamlit, plus altair if altair charts are requested
   • significance_test: pandas, numpy, scipy (+ pingouin if used)

   See references/env-management.md for a one-shot version-detection snippet and a cheat sheet of known version-sensitive APIs in this stack — read it before reaching for any "this kwarg should exist" assumption. When the installed version is older than the cutoff for an API you wanted to use, write the legacy form rather than crashing on the user's machine.

3. If anything is missing, ask the user how to install (do not install silently). Offer four choices:

   • install into the current env (pip install ...)
   • create a fresh venv at visualizations/.venv and install there (skill will then update the .sh wrappers to source it)
   • create a fresh conda env (conda create -n <name> python=3.x ...)
   • skip — user will install themselves; pause until they confirm

See references/env-management.md for the exact commands, the version-sensitive-API table, and the snippet that needs to go into parse_input.sh / generate_plot.sh / interactive_page.sh when a venv-based install is chosen.

---

Step 3 — Run the requested subskill

Read the matching agents/<name>/AGENT.md before running. Each subskill file documents triggers, behavior, what to bake in, what to trim, and which references to consult.

• parser → agents/parser/AGENT.md
• plot_gen → agents/plot_gen/AGENT.md
• interactive → agents/interactive/AGENT.md
• style_infer → agents/style_infer/AGENT.md

Common ordering:

1. If the user's request implies styling intent (uploaded a paper/figure/brand guide, or stated explicit visual preferences), run style_infer first so subsequent plots inherit the look from info/style_guide.md.
2. If intermediate_data/ is empty, run parser before any plot/dashboard work.
3. Then plot_gen and/or interactive for the actual visualization. Both must read info/style_guide.md when it exists.

---

Step 4 — Always close the loop in info/

The session closes in this order: supporting files first, context.md last.

1. Update the supporting info/*.md files with anything that came out of the session:
   • style_guide.md — any styling preference the user expressed (project-wide or per-plot, even something as small as "for the petal scatter, use square markers instead of dots"). Style preferences belong in the guide, not buried in a single recipe's extra field, so future plots stay consistent.
   • project_specific_knowledge.md — anything you learned about a domain-specific package or visualization technique that wasn't in your priors (the domain_viz subskill drives this).
   • how_to_use.md — only when the shell wrappers' behavior actually changed (e.g. a venv was created, a new subpage was added, a new subfolder is now part of the layout).
2. Then write context.md — append one concise entry covering the whole session:
   • what was requested (one sentence),
   • which subskill(s) ran,
   • which files changed (relative paths),
   • one or two gotchas a future agent should know (e.g. "luminosity column had 12% missing, used median imputation"; "streamlit page falls back to pre-rendered PNGs because dataset is 4M rows"; "added project_specific_knowledge.md for mne topomaps").

Keep context.md concise. The point is continuation — not a changelog. If the file grows past ~200 lines, summarize older entries into a "Summary so far" section at the top and trim.

---

Quick references

When you need depth on a specific topic, read the appropriate file (don't preload them):

• references/missing-data-strategies.md — strategies, when each is appropriate, common pitfalls.
• references/plotting-patterns.md — research-plot recipes in seaborn + matplotlib.
• references/streamlit-patterns.md — caching, multi-page apps, altair selections, large-data fallbacks.
• references/env-management.md — venv vs. conda, install commands, how to wire the venv into the .sh wrappers.
• references/figure-design-guidelines.md — the ten-rules-for-better-figures cheat sheet (Rougier et al. 2014). General design guidelines, not gates; useful when discussing chart choice with the user, when style_infer is making default choices, and when sanity-checking output.

The scaffolding under assets/scaffolding/ is the single source of truth for the initial file contents — when in doubt, copy from there and adapt rather than writing from scratch.