talont-org/vis-lens-caption-annot

Annotative Caption Visualization Lens

Philosophical Mode: Annotative Primary Question: "Are figure captions and axis labels fully self-contained?" Focus: Declarative titles (state the result, not "Figure N: Results"), axis labels with units, error definition in legend (CI95 / std / SEM), baseline references cited in caption, sample sizes disclosed per condition, venue-specific caption format

Arguments

/autoskillit:vis-lens-caption-annot [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 captions before paper submission
• Checking whether axis labels carry units and direction information
• Verifying error bars are defined in the legend or caption
• Ensuring baseline conditions are referenced explicitly
• Disclosing sample size per plotted condition
• User invokes /autoskillit:vis-lens-caption-annot

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-caption-annot/
• Use a non-declarative title like "Results" or "Figure 3: Performance" — titles must state the takeaway (e.g., "Model A outperforms baseline on all benchmarks")
• Omit units from axis labels (e.g., write "Latency (ms)", not "Latency")

ALWAYS:

• Check every figure title for declarative language

• Verify every axis label carries a unit or categorical scale description

• Confirm error bars / shaded regions are defined (CI95, ±1 std, IQR, etc.) in the legend or caption

• Flag any figure that references a baseline without naming it explicitly

• Report sample size per plotted condition (n=X seeds, N=X subjects, etc.)

• 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-caption-annot/vis_spec_caption_annot_{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-caption-annot/vis_spec_caption_annot_{...}.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 Figure Titles

For every figure in the experiment plan or codebase:

• Collect the current title string
• Classify: DECLARATIVE (states a result) vs DESCRIPTIVE (labels the figure)
• FAIL if title is purely descriptive (e.g. "Ablation Results", "Performance Comparison")
• PASS if title states the key finding (e.g. "Removing component X degrades accuracy by 8 pp")

Step 2: Axis Label and Unit Audit

For every axis in every figure:

• Confirm the label is present (no unlabeled axes)
• Confirm units are stated for continuous quantities: time (ms/s), memory (MB/GB), accuracy (%), loss (nats/bits), etc.
• Confirm categorical axes name the grouping variable
• FLAG WARNING for any axis missing a unit on a continuous quantity

Step 3: Error and Uncertainty Definition Audit

For every figure that shows error bars, shaded regions, or confidence intervals:

• Identify the error representation: CI (90/95/99%), ±1 std, ±1 SEM, IQR, min/max
• Verify the definition appears in the legend or caption text
• FLAG FAIL if error bars are shown but not defined

Step 4: Baseline and Sample Size Disclosure

• Identify every "baseline" referenced in a figure or caption
• Verify the baseline name and source are stated (e.g. "GPT-4 (OpenAI, 2023)")
• For each plotted group/condition, verify sample size n is disclosed (seeds, subjects, runs)
• FLAG WARNING for any undisclosed baseline or missing n

Step 5: Emit yaml:figure-spec Blocks

For each figure, emit one yaml:figure-spec fenced block with the annotations field describing the caption audit result. Then LOAD /autoskillit:mermaid and create a diagram showing: title audit → axis audit → error definition → baseline/n disclosure → verdict.

---

Output Template

# Annotative Caption Spec: {System / Experiment Name}

**Lens:** Annotative Caption (Annotative)
**Question:** Are figure captions and axis labels fully self-contained?
**Date:** {YYYY-MM-DD}
**Scope:** {What was analyzed}

## Caption Audit Summary

| Figure | Declarative Title | Axis Units | Error Defined | Baseline Named | Sample n | Status |
|--------|-------------------|-----------|---------------|----------------|----------|--------|
| fig-01 | FAIL (descriptive) | PASS | PASS | N/A | PASS | FAIL |
| fig-02 | PASS | PASS | PASS | PASS | PASS | OK |

## Figure Specs

```yaml
# yaml:figure-spec — canonical schema (spec_version: "1.0")
figure_id: "fig-02-ablation-comparison"
figure_title: "Removing component X degrades accuracy by 8 pp"
spec_version: "1.0"
chart_type: "bar"
chart_type_fallback: "table"
perceptual_justification: "Grouped bar chart shows per-component contribution directly."
data_source: "results/ablation.csv"
data_mapping:
  x: "variant"
  y: "accuracy_delta_pp"
  color: "component"
  size: ""
  facet: ""
layout:
  width_inches: 5.0
  height_inches: 3.5
  dpi: 300
stat_overlay:
  type: "error_bar"
  measure: "CI95"
  n_seeds: 5
annotations: ["Declarative title PASS; axis labels with units PASS; CI95 defined in legend PASS; n=5 seeds per condition PASS"]
anti_patterns: []
palette: "okabe-ito"
format: "pdf"
target_dpi: 300
library: "matplotlib"
report_section: "Section 5 Ablation"
priority: "P1"
placement_tier: "main"
conflicts: []
metadata:
  created_by: "vis-lens-caption-annot"
  reviewed_by: ""
  last_updated: "{YYYY-MM-DD}"

Caption Annotation 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 Titles ["TITLE AUDIT"]
        T1["title = {text}<br/>━━━━━━━━━━<br/>declarative: PASS / FAIL"]
    end

    subgraph Axes ["AXIS LABEL AUDIT"]
        A1["x-axis: {label} ({unit})<br/>y-axis: {label} ({unit})<br/>━━━━━━━━━━<br/>units present: PASS / FAIL"]
    end

    subgraph Errors ["ERROR DEFINITION"]
        E1["error type: {CI95/std/SEM}<br/>━━━━━━━━━━<br/>defined in legend: PASS / FAIL"]
    end

    subgraph Disclosure ["BASELINE & SAMPLE n"]
        D1["baseline: {name}<br/>n per condition: {N}<br/>━━━━━━━━━━<br/>disclosed: PASS / FAIL"]
    end

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

    T1 --> A1
    A1 --> E1
    E1 --> D1
    D1 --> V1

    class T1 stateNode;
    class A1 handler;
    class E1 cli;
    class D1 output;
    class V1 detector;

Color Legend: | Color | Category | Description | |-------|----------|-------------| | Dark Teal | Title | Declarative title audit | | Orange | Axes | Axis label and unit audit | | Dark Blue | Error | Error bar definition check | | Teal | Disclosure | Baseline and sample size disclosure | | Red | Verdict | OK / WARNING / FAIL assessment |

---

## 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 figure with a non-declarative title is flagged as FAIL
- [ ] Every axis missing units on a continuous quantity is flagged as WARNING
- [ ] Every figure with undefined error bars is flagged as FAIL