Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
VTL Image Analysis
Measure compositional structure in AI-generated images using the Visual Thinking Lens (VTL) framework. Detects default-mode bias (center lock, radial collaps...
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Measure compositional structure in AI-generated images using the Visual Thinking Lens (VTL) framework. Detects default-mode bias (center lock, radial collaps...
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Package facts
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- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- references/vtl-metrics.md, scripts/vtl_regen.py, scripts/vtl_probe.py, README.md, operators.yaml, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- rusparrish
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Documentation
VTL Image Analysis
Use this skill whenever a user asks to analyze, diagnose, or improve a generated image's composition. Also invoke it proactively after image generation if the user has requested better compositional quality.
When to Use
User says "analyze this image", "why does this look generic/flat/boring" User asks to improve a generated image's composition After generating an image with openai-image-gen or similar skills User asks why their prompts aren't producing interesting layouts
Step 1 — Measure
Run the probe script on the image: python3 scripts/vtl_probe.py <image_path> This returns JSON. Example: { "valid": true, "mask_status": "PASS", "delta_x": -0.027, "delta_y": 0.008, "r_v": 0.875, "rho_r": 12.4, "dRC": 0.40, "dRC_label": "mass-dominant", "k_var": 1.12, "infl_density": 0.16, "flags": ["CENTER_LOCK"] }
HARD STOP — Refusal Gate
Before reporting any results, check valid and mask_status. If valid is false OR mask_status is "FAIL": "VTL measurement failed: [error message]. The image does not have sufficient structural signal for reliable compositional analysis. Try a different image or one with more defined edges and contrast." Stop here. Do not report coordinates. Do not generate re-prompts. If mask_status is "WARN": "VTL measurement returned low-confidence results (sparse structural signal). Coordinates are reported but treat them as indicative, not definitive." Then continue with the caveat attached to all outputs. This refusal is non-negotiable. Fabricating a compositional reading from a failed measurement produces false diagnosis. The framework is deterministic by design — an uncertain measurement is reported as uncertain, not smoothed over.
Step 2 — Report Coordinates
Report the five coordinates plainly: VTL ANALYSIS ──────────────────────────────── Placement Δx={delta_x} Δy={delta_y} Void rᵥ={r_v} Packing ρᵣ={rho_r} Radial dRC={dRC} [{dRC_label}] Tension k_var={k_var} FLAGS: {flags or NONE}
Step 3 — Generate Re-Prompt (if flags present)
Run the regen script with the user's original prompt and the metrics output: python3 scripts/vtl_regen.py \ --prompt "USER'S ORIGINAL PROMPT" \ --metrics <path_to_metrics.json> \ --out prompts.json This selects operators from operators.yaml based on which flags fired and returns up to 3 prompt variants. Report the selected variant as the primary recommendation and offer the alternatives. If no flags fired, report: "No default-mode patterns detected. Coordinates are within normal range."
Operator Logic
Operators live in operators.yaml. They are rule-based — triggers are evaluated deterministically against the metric values. The AI does not invent or modify operators. If a trigger fires, the patch is applied. If not, it isn't. Do not override operator logic. Do not substitute your own re-prompt language for what the operator specifies. The operators are the prescription layer — they are the operator's responsibility, not the AI's improvisation. If the user wants to modify re-prompt behavior, direct them to edit operators.yaml.
Notes
Metrics describe compositional coordinates, not quality. CENTER_LOCK is not "bad" — it's a signal that the model defaulted. A portrait photographer choosing center composition is authorship. An AI doing it on every prompt regardless of content is prior behavior. VTL measures the difference. dRC requires radial eligibility. If mass centroid is very close to frame center, dRC is labeled "dual-center" — report the label, not a number interpretation. Full metric definitions: references/vtl-metrics.md Full framework: https://github.com/rusparrish/Visual-Thinking-Lens Author: Russell Parrish — https://artistinfluencer.com
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Scripts1 Config
- SKILL.md
- README.md
- references/vtl-metrics.md
- scripts/vtl_probe.py
- scripts/vtl_regen.py
- operators.yaml