docs: command flow for analysis runs in README; keep WORKFLOW in sync

- README: new 'Command flow' section — dependency-ordered pipeline (setup →
  batch generation → univariate → multivariate → LDA/cluster visualizations →
  classify → cross-version compare → JSON/model export) with actual output
  paths per step, using data/synthetic_1.4.0/ as the example
- README: document chart production — univariate_analysis --img publishes the
  img/ summary charts; visualize_clusters added to the flow
- README: fix -b path in 'Adding a new run' (bicorder.json lives at repo root)
- WORKFLOW: script list and any-readings-CSV command block updated to match

analysis/README.md

@@ -14,19 +14,19 @@ Readings are organized under `data/<type>_<version>/`, each self-contained with
 Runs:
 
 - **`data/synthetic_1.2.6/`** — 411 synthetic LLM-generated readings scored against bicorder v1.2.6 (see detailed procedure below). Renamed from `synthetic_20251116` to make the bicorder version it was generated against explicit, since the gradient structure changes between versions.
+- **`data/synthetic_1.4.0/`** - Same as above, but scored on v1.4.0, which has some changes to the gradients.
 - **`data/manual_20260320/`** — manual readings collected at [git.medlab.host/ntnsndr/protocol-bicorder-data](https://git.medlab.host/ntnsndr/protocol-bicorder-data), continuously expanding
 
 ### Adding a new run (e.g. re-running on edited gradients)
 
 1. Make a new run directory keyed by bicorder version, e.g. `data/synthetic_1.4.0/`.
-2. Point `bicorder_batch.py` at the shared input and the new output:
+2. Point `bicorder_batch.py` at the shared input and the new output (`-b` defaults to `../bicorder.json` in the repo root):
    ```bash
    python3 scripts/bicorder_batch.py data/protocols_edited.csv \
-       -o data/synthetic_1.4.0/readings.csv \
-       -b bicorder.json \
+       -o data/synthetic_1.4.0/readings.csv -b ../bicorder.json \
        -m <model> -a "<analyst>" -s "<standpoint>"
    ```
-3. The output `readings.csv` now records a `bicorder_version` column, so every run is self-describing about which gradient structure produced it. Run the downstream analysis (`multivariate_analysis.py`, `classify_readings.py`, etc.) against the new `readings.csv` with the new run directory as `--output`.
+3. The output `readings.csv` now records a `bicorder_version` column, so every run is self-describing about which gradient structure produced it. Run the downstream analysis (`univariate_analysis.py`, `multivariate_analysis.py`, `classify_readings.py`, etc.) against the new `readings.csv` with the new run directory as `--output`. The analysis scripts are version-agnostic: `classify_readings.py` auto-matches training data to the recorded version, and `compare_analyses.py` aligns renamed gradients across runs.
 
 ### Syncing the manual dataset
 
@@ -54,6 +54,87 @@ Many manual readings use the shortform bicorder (9 key dimensions rather than al
 
 ---
 
+## Command flow
+
+The analysis pipeline for a run, using `data/synthetic_1.4.0/` as the example. Commands run from this directory with the virtualenv activated. The flow is version-agnostic: each `readings.csv` records its `bicorder_version`, and every script reads the gradient structure from the file itself (shared helpers in `scripts/bicorder_common.py`), so the same sequence applies to any run and any future version.
+
+### 0. Setup (once)
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+### 1. Generate the readings (LLM batch diagnostic)
+
+Queries the LLM for every gradient of every protocol, one chat each. Interrupted runs can be continued by re-running with `--resume`, which skips completed work. (For the manual dataset, `scripts/sync_readings.sh data/manual_20260320` replaces this step.)
+
+```bash
+python3 scripts/bicorder_batch.py data/protocols_edited.csv -o data/synthetic_1.4.0/readings.csv \
+    -m <model> -a "<analyst>" -s "<standpoint>"
+```
+
+→ `data/synthetic_1.4.0/readings.csv` — 411 protocols × 23 gradients, plus provenance columns (`bicorder_version`, `analyst`, `standpoint`)
+
+### 2. Univariate analysis (averages and distributions)
+
+```bash
+python3 scripts/univariate_analysis.py data/synthetic_1.4.0/readings.csv
+
+# add --img to also publish the three summary charts into img/ (the charts linked in this README's analysis narrative)
+python3 scripts/univariate_analysis.py data/synthetic_1.2.6/readings.csv --img
+```
+
+→ `analysis/plots/` (protocol averages, distribution histogram, gradient averages), `analysis/data/` (per-protocol and per-gradient tables), and `analysis/reports/univariate_summary.txt` (mean, median, midpoint deviation, skewness)
+
+### 3. Multivariate analysis (clustering, PCA, correlation, feature importance)
+
+```bash
+python3 scripts/multivariate_analysis.py data/synthetic_1.4.0/readings.csv
+```
+
+→ `analysis/plots/`, `analysis/data/` (including `kmeans_clusters.csv`, **required by steps 4–5**), and `analysis/reports/analysis_summary.txt`. Use `--analyses clustering pca` to run a subset, and `--min-coverage 0.8` for sparse (shortform) datasets.
+
+### 4. Cluster visualizations (LDA + overlaid dimensional reductions)
+
+```bash
+# Cluster separation histogram and projection onto the discriminant axis
+python3 scripts/lda_visualization.py data/synthetic_1.4.0/readings.csv
+
+# Clusters overlaid on PCA / t-SNE / UMAP scatter plots
+python3 scripts/visualize_clusters.py data/synthetic_1.4.0/readings.csv
+```
+
+→ `analysis/plots/lda_cluster_separation.png` (plus separation statistics on the console) and `pca_2d_clustered.png` / `tsne_2d_clustered.png` / `umap_2d_clustered.png`
+
+### 5. Classify all readings
+
+```bash
+python3 scripts/classify_readings.py data/manual_20260320/readings.csv
+```
+
+Most typically applied to manual/shortform readings, with a synthetic run as training data. The training CSV is auto-selected to match the readings' recorded bicorder version (falls back to the most recent run and warns when versions differ, aligning renamed gradients); `--training` overrides. → `analysis/classifications.csv` (cluster, confidence, completeness, recommended form)
+
+### 6. Compare runs (including across bicorder versions)
+
+```bash
+python3 scripts/compare_analyses.py data/synthetic_1.4.0/readings.csv data/synthetic_1.2.6/readings.csv
+```
+
+Canonicalizes old gradient names and compares only shared dimensions, so runs from different versions align. Console report: Euclidean distance, RMSE, correlation; with several comparison files it ranks them. With no arguments it executes the legacy model-audit comparison (see *Manual and alternate model audit* below).
+
+### 7. Per-protocol JSONs and model export (optional)
+
+```bash
+python3 scripts/convert_csv_to_json.py data/synthetic_1.4.0/readings.csv
+python3 scripts/export_model_for_js.py data/synthetic_1.4.0/readings.csv
+```
+
+→ `json/` (one bicorder.json-spec reading per protocol); `bicorder_model.json` in this directory, read by `bicorder-app` at build time (requires step 3 first; see `INTEGRATION_GUIDE.md`)
+
+---
+
 ## Purpose
 
 This analyses has several purposes:
@@ -120,7 +201,7 @@ python3 scripts/bicorder_batch.py data/protocols_edited.csv -o data/synthetic_1.
 python3 scripts/bicorder_batch.py data/protocols_edited.csv -o data/synthetic_1.2.6/readings_gemma3-12b.csv -m gemma3:12b -a "Gemma3:12b" -s "A careful ethnographer and outsider aspiring to achieve a neutral stance and a high degree of precision" --start 1 --end 10
 ```
 
-A Euclidean distance analysis (`python3 scripts/compare_analyses.py`) found that the `gpt-oss` model was closer to the manual example than the others. It was therefore selected to be the model used for conducting the bicorder diagnostic on the dataset.
+A Euclidean distance analysis (`python3 scripts/compare_analyses.py`, with paths given as arguments: a reference CSV followed by comparison CSVs) found that the `gpt-oss` model was closer to the manual example than the others. It was therefore selected to be the model used for conducting the bicorder diagnostic on the dataset.
 
 ```
 Average Euclidean Distance:
@@ -137,16 +218,26 @@ python3 scripts/bicorder_batch.py data/protocols_edited.csv -o data/synthetic_1.
 
 The result was a CSV-formatted list of protocols (`data/synthetic_1.2.6/readings.csv`, n=411).
 
+The same command, with changed version names, was used to produce the `synthetic_1.4.0` readings.
+
 ### Further analysis
 
 #### Basic averages
 
+For any run, `scripts/univariate_analysis.py` computes per-protocol and per-gradient averages, the summary statistics (mean, median, deviation from the midpoint, skewness), and the plots below, saving them to the run's `analysis/` directory:
+
+```bash
+python3 scripts/univariate_analysis.py data/synthetic_1.4.0/readings.csv
+```
+
 Per-protocol values are meaningful for the bicorder because, despite varying levels of appropriateness, all of the gradients are structured as ranging from "hardness" to "softness"---with lower values associated with greater rigidity. The average value for a given protocol, therefore, provides a rough sense of the protocol's hardness. 
 
 Basic averages appear in `data/synthetic_1.2.6/readings-analysis.ods`.
 
 #### Univariate analysis
 
+The charts linked below are the repo-level copies in `img/`, reproduced from a run with `python3 scripts/univariate_analysis.py <readings.csv> --img` (these were produced from the 1.2.6 dataset).
+
 First, a plot of average values for each protocol: 
 
 ![Protocol averages plot](img/protocol_averages.png)
@@ -178,7 +269,7 @@ Expectations:
 * There are some gradients whose values are highly correlated. These might point to redundancies in the bicorder design.
 * Some correlations might be revealing about connections in the characteristics of protocols, but these should be considered carefully as they may be the result of design or LLM interpretation.
 
-Claude Code created a `multivariate_analysis.py` tool to conduct this analysis. Usage:
+I created a `multivariate_analysis.py` tool to conduct this analysis. Usage:
 
 ```bash
 # Run all analyses (default)