Vis Lens Temporal

Temporal Dynamics Visualization Lens

Philosophical Mode: Temporal Primary Question: "Are training dynamics shown clearly and honestly?" Focus: Axis Scaling (linear vs log), Smoothing Disclosure, Epoch/Step Alignment, Run Aggregation (mean + variance bands), Early-Stopping Markers, Wall-Clock vs Step-Count X-Axis

Arguments

/autoskillit:vis-lens-temporal [context_path] [experiment_plan_path]

• context_path (optional positional arg 1) — Absolute path to a lens context file containing IV/DV tables, H0/H1 hypotheses, controlled variables, and success criteria. If provided, read this file before beginning analysis to obtain structured context. If omitted, discover context by exploring the CWD.
• experiment_plan_path (optional positional arg 2) — Absolute path to the full experiment plan. If provided, read for complete experimental methodology and design. If omitted, locate the experiment plan by exploring the CWD.

When to Use

• Reviewing training curves, learning curves, or any metric-vs-step/epoch plots
• Checking whether x-axis units are consistent across compared runs
• Evaluating whether smoothing is disclosed and appropriate
• Planning multi-run aggregation with variance bands
• User invokes /autoskillit:vis-lens-temporal

Critical Constraints

NEVER:

• Modify any source code files
• Do not litter the codebase with useless comments, TODO markers, or explanatory annotations — the skill output and diagram speak for themselves
• Create files outside {{AUTOSKILLIT_TEMP}}/vis-lens-temporal/
• Omit the CRITICAL flag when n_seeds == 1 for training curves — single-seed variance is unquantifiable
• Apply smoothing without disclosing the smoothing window or method
• Mix epoch-count and step-count x-axes on the same multi-run comparison without alignment

ALWAYS:

• CRITICAL: if n_seeds == 1 for any training curve, flag as CRITICAL — single-seed training curves cannot demonstrate stability or convergence robustness

• Disclose smoothing: state the EMA α or window size in the figure caption or axis label

• When comparing runs with different batch sizes or learning rate schedules, align on wall-clock time OR total gradient steps (not raw epochs), and document the choice

• Use log-scale y-axis when loss spans more than one order of magnitude

• Mark early-stopping epoch/step as a vertical dashed line with label

• BEFORE creating any diagram, LOAD the /autoskillit:mermaid skill using the Skill tool — this is MANDATORY

• If the Skill tool cannot be used (disable-model-invocation) or refuses this invocation, do NOT proceed with diagram creation. Abort this step and omit the diagram from output.

• Write output to {{AUTOSKILLIT_TEMP}}/vis-lens-temporal/vis_spec_temporal_{YYYY-MM-DD_HHMMSS}.md (relative to the current working directory)

• After writing the file, emit the structured output token as literal plain text with no markdown formatting on the token name (the adjudicator performs a regex match):

  diagram_path = /absolute/path/to/{{AUTOSKILLIT_TEMP}}/vis-lens-temporal/vis_spec_temporal_{...}.md
  

Analysis Workflow

Step 0: Parse optional arguments

If positional arg 1 (context_path) is provided and the file exists, read it to obtain IV/DV tables, H0/H1 hypotheses, controlled variables, and success criteria. If positional arg 2 (experiment_plan_path) is provided and exists, read the experiment plan for full methodology. Use this structured context as the foundation for Steps 1–4; skip the CWD exploration for these fields if the context file supplies them.

Step 1: Inventory Training Curves

Scan experiment plan, context file, and codebase for:

Learning and Loss Curves

• Find all learning curves, loss curves, metric-vs-step plots
• Look for: train_loss, val_loss, reward, accuracy_vs_epoch, loss_curve, plt.plot

Seed Count

• Find n_seeds for each training run
• Look for: n_seeds, num_seeds, seeds, SEEDS, random_state, runs

Smoothing Calls

• Detect whether smoothing is applied and whether it is disclosed
• Look for: smooth, ema, rolling_mean, gaussian_filter, savgol_filter, EMA α parameters

X-Axis Type

• Determine whether x-axis is epoch-count, step-count, or wall-clock time
• Look for: epochs, steps, global_step, time_elapsed, x-axis labels

Early Stopping

• Detect early-stopping usage and whether it is marked on the plot
• Look for: early_stopping, patience, best_epoch, EarlyStopping

Step 2: Determine Axis Scaling

For each loss or metric curve, check the range:

• If loss spans more than one order of magnitude (max/min > 10): recommend log-scale y-axis
• If loss is bounded (e.g., accuracy 0–1): linear scale is acceptable
• Document the recommendation with the detected range

Step 3: Alignment Check

For all multi-run comparisons:

• Verify that all compared runs use the same x-axis unit (epoch vs step vs time)
• Flag mismatches as WARNING: "Runs use mixed x-axis units — align on gradient steps or wall-clock time"
• Check batch size and learning rate schedule consistency across compared runs

Step 4: Emit yaml:figure-spec Blocks

For each figure, emit one yaml:figure-spec fenced block with the stat_overlay variance band filled in. Then LOAD /autoskillit:mermaid and create a temporal flow diagram showing x-axis unit → scaling choice → smoothing annotation → variance band → verdict.

Output Template

# Temporal Dynamics Spec: {System / Experiment Name}

**Lens:** Temporal Dynamics (Temporal)
**Question:** Are training dynamics shown clearly and honestly?
**Date:** {YYYY-MM-DD}
**Scope:** {What was analyzed}
**n_seeds detected:** {N}

## Temporal Audit Summary

| Figure | n_seeds | x_axis | y_scale | smoothing | early_stop_marked | Status |
|--------|---------|--------|---------|-----------|-------------------|--------|
| {fig-01} | 1 | epoch | linear | none | no | CRITICAL |
| {fig-02} | 5 | step | log | EMA α=0.9 | yes | OK |

## Figure Specs

```yaml
# yaml:figure-spec — canonical schema (spec_version: "1.0")
figure_id: "fig-02-loss-curve"
figure_title: "Training Loss vs Gradient Steps"
spec_version: "1.0"
chart_type: "line"
chart_type_fallback: "scatter"
perceptual_justification: "Log-scale y-axis spans 2 orders of magnitude; variance band shows run stability."
data_source: "results/loss_curves.csv"
data_mapping:
  x: "global_step"
  y: "train_loss"
  color: "run_id"
  size: ""
  facet: ""
layout:
  width_inches: 6.0
  height_inches: 4.0
  dpi: 300
stat_overlay:
  type: "band"
  measure: "CI95"
  n_seeds: 5
annotations: ["log-scale y; EMA α=0.9 disclosed; early-stop at step 4200"]
anti_patterns: ["ap-missing-variance-band"]
palette: "okabe-ito"
format: "pdf"
target_dpi: 300
library: "matplotlib"
report_section: "Section 3 Training"
priority: "P1"
placement_tier: "main"
conflicts: []
metadata:
  created_by: "vis-lens-temporal"
  reviewed_by: ""
  last_updated: "{YYYY-MM-DD}"

Temporal Dynamics Diagram

%%{init: {'flowchart': {'nodeSpacing': 50, 'rankSpacing': 60, 'curve': 'basis'}}}%%
flowchart TB
    %% CLASS DEFINITIONS %%
    classDef cli fill:#1a237e,stroke:#7986cb,stroke-width:2px,color:#fff;
    classDef stateNode fill:#004d40,stroke:#4db6ac,stroke-width:2px,color:#fff;
    classDef handler fill:#e65100,stroke:#ffb74d,stroke-width:2px,color:#fff;
    classDef output fill:#00695c,stroke:#4db6ac,stroke-width:2px,color:#fff;
    classDef detector fill:#b71c1c,stroke:#ef5350,stroke-width:2px,color:#fff;

    subgraph XAxis ["X-AXIS UNIT"]
        X1["epoch / step / wall-clock<br/>━━━━━━━━━━<br/>{alignment status}"]
    end

    subgraph Scale ["Y-AXIS SCALING"]
        Y1["linear / log<br/>━━━━━━━━━━<br/>{loss range: {min}–{max}}"]
    end

    subgraph Smooth ["SMOOTHING"]
        S1["none / EMA / rolling<br/>━━━━━━━━━━<br/>{α or window disclosed: yes/no}"]
    end

    subgraph Variance ["VARIANCE BAND"]
        V1["CI95 / SD band<br/>━━━━━━━━━━<br/>n_seeds = {N}"]
    end

    subgraph Verdict ["VERDICT"]
        VD1["{OK / WARNING / CRITICAL}<br/>━━━━━━━━━━<br/>{reason}"]
    end

    X1 --> Y1
    Y1 --> S1
    S1 --> V1
    V1 --> VD1

    class X1 stateNode;
    class Y1 cli;
    class S1 handler;
    class V1 output;
    class VD1 detector;

Color Legend:

---

## Pre-Diagram Checklist

Before creating the diagram, verify:

- [ ] LOADED `/autoskillit:mermaid` skill using the Skill tool
- [ ] Using ONLY classDef styles from the mermaid skill (no invented colors)
- [ ] Diagram will include a color legend table
- [ ] Every CRITICAL (n_seeds == 1) training curve is flagged
- [ ] Every smoothing call has its parameters disclosed in the figure spec
- [ ] Early-stopping markers are noted for all curves with early stopping