Vis Lens Reproducibility

Replicative Reproducibility Visualization Lens

Philosophical Mode: Replicative Primary Question: "Can the figures be reproduced from the data and code?" Focus: Data availability (public / restricted / embargoed), preprocessing parameter disclosure (bin widths, smoothing windows, normalization), plotting library and version pinning, random seed documentation, per-figure code reference (script or notebook cell)

Arguments

/autoskillit:vis-lens-reproducibility [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

• Auditing figure reproducibility before public release
• Checking whether preprocessing parameters are fully disclosed
• Verifying that random seeds are documented for stochastic plots
• Linking each figure to the script or notebook cell that generates it
• User invokes /autoskillit:vis-lens-reproducibility

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-reproducibility/
• Treat "code available on request" as equivalent to public availability
• Omit random seed documentation for any figure derived from stochastic processes

ALWAYS:

• Check data availability status for every figure (public/restricted/embargoed)

• Document bin widths for histograms, smoothing windows for time-series, normalization parameters for heatmaps

• Pin plotting library name and version (matplotlib 3.8.2, seaborn 0.13.0, etc.)

• Record the random seed(s) used for any stochastic component (sampling, bootstrapping, noise injection)

• Provide a per-figure code reference: script path or notebook cell identifier

• 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-reproducibility/vis_spec_reproducibility_{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-reproducibility/vis_spec_reproducibility_{...}.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: Data Availability Inventory

For each figure:

• Identify the data source (file path, dataset name, external URL)
• Classify availability: PUBLIC (DOI / URL), RESTRICTED (license required), EMBARGOED (not yet released)
• FLAG FAIL if data is restricted/embargoed with no access plan stated

Step 2: Preprocessing Parameter Audit

For each figure, identify all preprocessing steps that affect visual output:

• Histograms: bin width or bin count; normalization (density vs count vs probability)
• Time-series / learning curves: smoothing window type (rolling mean, EMA) and window size
• Heatmaps: normalization method (min-max, z-score, none); colormap clipping range
• Scatter/line with aggregation: aggregation function (mean, median) and grouping
• FLAG WARNING for any preprocessing parameter not documented

Step 3: Library and Version Audit

Scan the codebase for plotting imports:

• Record: import matplotlib, import seaborn, import plotly, etc.
• Check pyproject.toml or requirements.txt for pinned versions
• FLAG WARNING if plotting library version is not pinned

Step 4: Random Seed Audit

For each figure involving a stochastic component:

• Identify source of randomness: bootstrapping, subsampling, t-SNE/UMAP, noise injection
• Verify random_state, seed, np.random.seed, or equivalent is documented per figure
• FLAG FAIL if any stochastic figure has no documented seed

Step 5: Per-Figure Code Reference

For each figure:

• Identify the script or notebook cell that generates it
• Record: file path + function or cell ID
• FLAG WARNING if a figure has no traceable code reference

Step 6: Emit yaml:figure-spec Blocks

For each figure, emit one yaml:figure-spec fenced block with data_source and annotations fields capturing reproducibility metadata. Then LOAD /autoskillit:mermaid and create a diagram showing: data availability → preprocessing → library version → seed documentation → code reference → verdict.

Output Template

# Replicative Reproducibility Spec: {System / Experiment Name}

**Lens:** Replicative Reproducibility (Replicative)
**Question:** Can the figures be reproduced from the data and code?
**Date:** {YYYY-MM-DD}
**Scope:** {What was analyzed}

## Reproducibility Audit Summary

| Figure | Data Available | Preprocessing Documented | Library Pinned | Seed Documented | Code Reference | Status |
|--------|---------------|--------------------------|----------------|-----------------|----------------|--------|
| fig-01 | PUBLIC | PASS | PASS | N/A | scripts/plot_main.py | OK |
| fig-02 | RESTRICTED | WARNING | FAIL | PASS | notebooks/ablation.ipynb#cell-7 | FAIL |

## Figure Specs

```yaml
# yaml:figure-spec — canonical schema (spec_version: "1.0")
figure_id: "fig-01-main-result"
figure_title: "Model A achieves state-of-the-art on all benchmarks"
spec_version: "1.0"
chart_type: "bar"
chart_type_fallback: "table"
perceptual_justification: "Bars communicate exact values; error bars show CI95 over 5 seeds."
data_source: "results/main.csv (DOI: 10.xxxx/xxxxx)"
data_mapping:
  x: "benchmark"
  y: "score"
  color: "model"
  size: ""
  facet: ""
layout:
  width_inches: 6.0
  height_inches: 4.0
  dpi: 300
stat_overlay:
  type: "error_bar"
  measure: "CI95"
  n_seeds: 5
annotations: ["data: public (DOI); preprocessing: none; library: matplotlib==3.8.2; seeds: 0,1,2,3,4; code: scripts/plot_main.py:plot_main_result()"]
anti_patterns: []
palette: "okabe-ito"
format: "pdf"
target_dpi: 300
library: "matplotlib==3.8.2"
report_section: "Section 4 Results"
priority: "P0"
placement_tier: "main"
conflicts: []
metadata:
  created_by: "vis-lens-reproducibility"
  reviewed_by: ""
  last_updated: "{YYYY-MM-DD}"

Reproducibility 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;
    classDef phase fill:#6a1b9a,stroke:#ba68c8,stroke-width:2px,color:#fff;

    subgraph Data ["DATA AVAILABILITY"]
        D1["source: {path / DOI}<br/>━━━━━━━━━━<br/>public / restricted / embargoed"]
    end

    subgraph Preproc ["PREPROCESSING"]
        P1["bin width / window: {N}<br/>normalization: {method}<br/>━━━━━━━━━━<br/>documented: PASS / FAIL"]
    end

    subgraph Library ["LIBRARY VERSION"]
        L1["matplotlib=={version}<br/>━━━━━━━━━━<br/>pinned: PASS / WARNING"]
    end

    subgraph Seeds ["RANDOM SEEDS"]
        S1["seeds: {list}<br/>━━━━━━━━━━<br/>documented: PASS / FAIL / N/A"]
    end

    subgraph CodeRef ["CODE REFERENCE"]
        C1["script: {path}:{function}<br/>━━━━━━━━━━<br/>traceable: PASS / WARNING"]
    end

    subgraph Verdict ["VERDICT"]
        V1["{OK / WARNING / FAIL}<br/>━━━━━━━━━━<br/>{reason}"]
    end

    D1 --> P1
    P1 --> L1
    L1 --> S1
    S1 --> C1
    C1 --> V1

    class D1 stateNode;
    class P1 handler;
    class L1 cli;
    class S1 output;
    class C1 phase;
    class V1 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 restricted/embargoed dataset is flagged as FAIL or WARNING
- [ ] Every histogram bin width and time-series smoothing window is audited
- [ ] Every stochastic figure has its seeds documented or flagged