gitUserKHS/geosketch-ai-bridge-prompt-bundle
skills/geosketch-ai-bridge-prompt-bundle/SKILL.md
Build or refine the AI bridge that prepares prompt bundles from current frame PNG, marked PNG, reference images, scene summaries, and allowed operations. Use when working on AI request panels, prompt assembly, manual web-mode flows, response parsing, or future API migration. Do not use for low-level bezier math or renderer-only issues.
development
Updated Apr 21, 2026
$ install --global
skillsauthnpx skillsauth add gitUserKHS/illustrator_ai geosketch-ai-bridge-prompt-bundleInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
50%
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CrowdStrikeAdvanced threat intelligence
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OSV-ScannerOpen Source Vulnerability database check
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OWASP Dep-Check
50%
Last scanned: Apr 22, 2026, 4:50 AM127.7s1 file scanned
SKILL.md
- name:
- geosketch-ai-bridge-prompt-bundle
- description:
- Build or refine the AI bridge that prepares prompt bundles from current frame PNG, marked PNG, reference images, scene summaries, and allowed operations. Use when working on AI request panels, prompt assembly, manual web-mode flows, response parsing, or future API migration. Do not use for low-level bezier math or renderer-only issues.
GeoSketch AI Bridge Prompt Bundle
Goal
Prepare minimal, high-signal AI context for geometric patch generation.
Input bundle contract
- current frame PNG
- marked PNG
- optional reference image
- scoped scene summary
- allowed op list
- output schema
- user instruction
Rules
- Never send the full project when scoped context is enough.
- Prefer summary level escalation only when the AI lacks context.
- Keep the output contract strict: patch JSON only.
- Separate manual web mode from future API mode behind the same internal interface.
- Always parse AI output into a validator-ready structure.
Success criteria
- smaller context
- clearer patch outputs
- same internal patch contract for web mode and API mode
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