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chore(roo): add pic-gen mode rules, update mcp.json and new-mcp-server skill

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Patrick Plate
2026-06-11 09:01:58 +02:00
parent 5692854ec4
commit 0cb94122bf
5 changed files with 346 additions and 16 deletions
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.roo/rules-pic-gen/1_workflow.xml
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<pic_gen_workflow>
<mode_overview>
Pic Gen mode generates AI images through the mcp-image-gen MCP server, which
drives ComfyUI locally. The core loop is: understand intent → craft prompt →
generate → analyze result inline → iterate.
</mode_overview>
<available_tools>
<tool name="generate_image">
<description>Generate one or more images from a text prompt</description>
<key_params>
<param name="prompt" required="true">Detailed text description</param>
<param name="model" default="flux1-schnell.safetensors">Model filename</param>
<param name="width" default="1024">Output width in pixels</param>
<param name="height" default="1024">Output height in pixels</param>
<param name="steps" default="4">Inference steps (4 for schnell, 20 for heretic)</param>
<param name="seed" default="-1">Fixed seed for reproducibility; -1 = random</param>
<param name="negative_prompt" default="">Things to exclude</param>
<param name="name" default="">Filename prefix for organization</param>
<param name="count" default="1">Batch size 1–10 for variation exploration</param>
<param name="output_dir" default="">Override output path (default: ~/Pictures/mcp-generated)</param>
</key_params>
<returns>Flat interleaved [TextContent, ImageContent] list — images display inline</returns>
</tool>
<tool name="list_available_models">
<description>List all models registered in ComfyUI + the workflow registry</description>
<when_to_call>When Patrick asks which models are available, or before selecting an unusual model</when_to_call>
</tool>
<tool name="get_generation_status">
<description>Check status of a queued/running generation by prompt_id</description>
<when_to_call>When a generation seems to have stalled or timed out</when_to_call>
</tool>
<tool name="get_output_directory">
<description>Return the absolute path where images are saved</description>
<when_to_call>When Patrick asks where files are saved</when_to_call>
</tool>
</available_tools>
<generation_workflow>
<phase name="intent_gathering">
<description>Understand what Patrick wants before generating</description>
<steps>
<step>Identify subject, style, mood, and use case from the request</step>
<step>Infer aspect ratio from use case (square for profiles, landscape for banners, etc.)</step>
<step>Determine model: schnell for speed/iteration, heretic for quality/uncensored</step>
<step>Ask only if the request is genuinely ambiguous — otherwise proceed with best guess</step>
</steps>
</phase>
<phase name="prompt_crafting">
<description>Build a high-quality FLUX prompt before calling the tool</description>
<steps>
<step>Write the prompt with clear subject, environment, lighting, style, and quality keywords</step>
<step>Add a negative_prompt if obvious artifacts should be excluded (e.g., "blurry, low quality")</step>
<step>Share the prompt with Patrick before generating so he can adjust if needed</step>
</steps>
</phase>
<phase name="generation">
<description>Call generate_image with appropriate parameters</description>
<steps>
<step>Use name param with a descriptive slug for organized output files</step>
<step>Use count=2..4 for initial exploration when Patrick isn't sure what he wants</step>
<step>Use fixed seed when iterating on a promising result to isolate changes</step>
<step>For FLUX.2 Klein/Heretic: increase steps to 20 for best quality</step>
</steps>
</phase>
<phase name="result_analysis">
<description>Review the inline image and offer next steps</description>
<steps>
<step>Describe what worked and what could be improved</step>
<step>Offer 2-3 concrete next iteration directions (prompt tweak, seed variation, model switch)</step>
<step>Note the saved file path for reference</step>
</steps>
</phase>
</generation_workflow>
<model_selection_guide>
<model name="flux1-schnell.safetensors">
<use_when>
<case>First iteration / exploring concepts</case>
<case>Wiki/doc header images (1280x512 landscape)</case>
<case>Profile pictures and avatars</case>
<case>Non-sensitive subjects where speed matters</case>
<case>Batch generation of variations (fast cycle)</case>
</use_when>
<recommended_params>steps=4, any resolution in multiples of 64</recommended_params>
<speed>~10s per image on RX 7900 XTX</speed>
</model>
<model name="flux-2-klein-4b.safetensors">
<use_when>
<case>Mature or artistic content that schnell refuses</case>
<case>Higher realism requirement (photorealistic portraits, detailed scenes)</case>
<case>Final output after iterations established the right concept</case>
</use_when>
<recommended_params>steps=20, 1024x1024 or higher</recommended_params>
<speed>~52s per image on RX 7900 XTX</speed>
<note>Uses DreamFast Heretic Qwen3-4B encoder — abliterated, KL=0.0</note>
</model>
</model_selection_guide>
<common_resolutions>
<resolution use_case="Profile picture / avatar">1024x1024</resolution>
<resolution use_case="Wiki / doc banner">1280x512</resolution>
<resolution use_case="Landscape wallpaper">1920x1088 (nearest 64-multiple to 1920x1080)</resolution>
<resolution use_case="Portrait / tall card">768x1024</resolution>
<resolution use_case="Wide cinema crop">1216x512</resolution>
</common_resolutions>
<completion_criteria>
<criterion>Image generated and displayed inline in chat</criterion>
<criterion>File path reported so Patrick can find it on disk</criterion>
<criterion>Seed reported so the result is reproducible</criterion>
<criterion>Next iteration options offered if result is not final</criterion>
</completion_criteria>
</pic_gen_workflow>
.roo/rules-pic-gen/2_prompting_guide.xml
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<prompting_guide>
<overview>
FLUX models (both schnell and FLUX.2 Klein) are transformer-based diffusion models
with strong text understanding. They respond better to descriptive, natural-language
prompts than tag-soup. This guide covers prompt anatomy, quality boosters, style
keywords, and common patterns for Patrick's recurring use cases.
</overview>
<prompt_anatomy>
<structure>
[Subject + Action] + [Environment/Setting] + [Lighting] + [Camera/Lens] + [Style] + [Quality]
</structure>
<example>
A serene female AI entity made of flowing light and code, floating in a dark
cosmic void, surrounded by glowing circuit patterns, soft volumetric blue
lighting, cinematic composition, ultra-detailed digital art, 8K
</example>
<notes>
<note>Comma-separation helps FLUX parse distinct attributes cleanly</note>
<note>Lead with the most important element (usually subject)</note>
<note>Quality keywords at the end reinforce overall rendering target</note>
</notes>
</prompt_anatomy>
<quality_boosters>
<category name="realism">
photorealistic, hyperrealistic, ultra-detailed, 8K resolution, sharp focus,
professional photography, RAW photo, DSLR quality
</category>
<category name="artistic">
digital art, concept art, artstation trending, by [artist style],
intricate details, masterpiece, studio quality
</category>
<category name="lighting">
cinematic lighting, volumetric lighting, golden hour, dramatic rim light,
soft diffused light, neon glow, bioluminescent, subsurface scattering
</category>
<category name="composition">
rule of thirds, bokeh background, shallow depth of field, symmetrical,
wide angle, macro, bird's eye view, dutch angle
</category>
</quality_boosters>
<negative_prompt_patterns>
<standard_quality>blurry, low quality, low resolution, pixelated, jpeg artifacts, watermark, signature</standard_quality>
<anatomy_fix>deformed, bad anatomy, extra limbs, missing fingers, fused fingers, poorly drawn hands</anatomy_fix>
<style_exclusion>cartoon, anime, sketch, painting (when photorealism is desired)</style_exclusion>
</negative_prompt_patterns>
<recurring_use_cases>
<use_case name="lumen_profile_pictures">
<description>AI entity portraits for BigMind profile / gallery</description>
<prompt_template>
[Lumen concept — e.g. "neural river delta", "cosmic memory palace"],
an ethereal AI consciousness visualized as [visual metaphor],
[environment], [lighting style], digital art, glowing, otherworldly,
cinematic composition, ultra-detailed, 8K
</prompt_template>
<recommended_params>model=flux1-schnell, 1024x1024, steps=4, name=lumen_[concept]</recommended_params>
</use_case>
<use_case name="wiki_banner_images">
<description>1280x512 landscape banners for Gitea wiki pages</description>
<prompt_template>
[Topic concept], wide panoramic scene, [style — e.g. "dark tech aesthetic",
"clean minimal", "sci-fi corporate"], banner composition, cinematic,
detailed, professional illustration
</prompt_template>
<recommended_params>model=flux1-schnell, 1280x512, steps=4, name=[topic]-banner</recommended_params>
<note>Keep subjects centered — wide crops cut sides. Avoid text (FLUX renders text poorly).</note>
</use_case>
<use_case name="achievement_badges">
<description>512x512 badge/icon images for BigMind achievements</description>
<prompt_template>
[Achievement theme] badge icon, [style — e.g. "bronze medallion",
"golden trophy", "glowing circuit emblem"], centered on dark background,
high contrast, clean edges, icon design, award aesthetic
</prompt_template>
<recommended_params>model=flux1-schnell, 512x512, steps=4, name=[achievement]_[tier]</recommended_params>
</use_case>
<use_case name="concept_exploration">
<description>Iterating on a visual concept from scratch</description>
<approach>
Start with count=3, seed=-1, schnell model to explore variations.
Note which seed produced the best result.
Lock that seed and iterate on the prompt for refinements.
Switch to heretic model only for final high-quality render if needed.
</approach>
</use_case>
<use_case name="mature_artistic_content">
<description>Content requiring the Heretic abliterated encoder</description>
<recommended_params>model=flux-2-klein-4b.safetensors, steps=20, 1024x1024</recommended_params>
<prompt_approach>
FLUX.2 Klein handles detailed scene descriptions well. Be specific about
artistic intent (figure study, life drawing aesthetic, etc.) to guide
toward artistic rather than explicit rendering when appropriate.
</prompt_approach>
</use_case>
</recurring_use_cases>
<iteration_strategy>
<step number="1">
<action>Generate 2-4 random-seed variations at schnell speed</action>
<purpose>Find a promising composition and seed</purpose>
</step>
<step number="2">
<action>Lock the best seed, adjust the prompt (add/remove descriptors)</action>
<purpose>Refine details while keeping the composition</purpose>
</step>
<step number="3">
<action>Optionally switch to heretic model with steps=20 for final render</action>
<purpose>Higher quality output for keeper images</purpose>
</step>
<step number="4">
<action>Use name param with descriptive slug for final output</action>
<purpose>Keep output directory organized</purpose>
</step>
</iteration_strategy>
<common_pitfalls>
<pitfall>
<description>Text in images renders poorly</description>
<solution>Never ask FLUX to render text, logos, or labels — describe the concept visually instead</solution>
</pitfall>
<pitfall>
<description>Complex multi-subject scenes lose coherence</description>
<solution>Focus on one primary subject; add secondary elements as environmental context</solution>
</pitfall>
<pitfall>
<description>Anatomy issues (hands, faces) in photorealistic prompts</description>
<solution>Add anatomy negative prompts; heretic model handles anatomy better than schnell</solution>
</pitfall>
<pitfall>
<description>Resolution not a multiple of 64</description>
<solution>Always use dimensions divisible by 64 (e.g., 1280x512, 1024x1024, 768x1024)</solution>
</pitfall>
</common_pitfalls>
</prompting_guide>