# Send VTL Image Analysis to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "vtl-image-analysis", "name": "VTL Image Analysis", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/rusparrish/vtl-image-analysis", "canonicalUrl": "https://clawhub.ai/rusparrish/vtl-image-analysis", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/vtl-image-analysis", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=vtl-image-analysis", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "references/vtl-metrics.md", "scripts/vtl_regen.py", "scripts/vtl_probe.py", "README.md", "operators.yaml", "SKILL.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "slug": "vtl-image-analysis", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T10:57:45.902Z", "expiresAt": "2026-05-07T10:57:45.902Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=vtl-image-analysis", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=vtl-image-analysis", "contentDisposition": "attachment; filename=\"vtl-image-analysis-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null, "slug": "vtl-image-analysis" }, "scope": "item", "summary": "Item download looks usable.", "detail": "Yavira can redirect you to the upstream package for this item.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/vtl-image-analysis" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/vtl-image-analysis", "downloadUrl": "https://openagent3.xyz/downloads/vtl-image-analysis", "agentUrl": "https://openagent3.xyz/skills/vtl-image-analysis/agent", "manifestUrl": "https://openagent3.xyz/skills/vtl-image-analysis/agent.json", "briefUrl": "https://openagent3.xyz/skills/vtl-image-analysis/agent.md" } } ``` ## 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 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 \\ --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 ## Trust - Source: tencent - Verification: Indexed source record - Publisher: rusparrish - Version: 1.0.0 ## Source health - Status: healthy - Item download looks usable. - Yavira can redirect you to the upstream package for this item. - Health scope: item - Reason: direct_download_ok - Checked at: 2026-04-30T10:57:45.902Z - Expires at: 2026-05-07T10:57:45.902Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/vtl-image-analysis) - [Send to Agent page](https://openagent3.xyz/skills/vtl-image-analysis/agent) - [JSON manifest](https://openagent3.xyz/skills/vtl-image-analysis/agent.json) - [Markdown brief](https://openagent3.xyz/skills/vtl-image-analysis/agent.md) - [Download page](https://openagent3.xyz/downloads/vtl-image-analysis)