Photography ai

Skill marktantongco/opencodelinux/skills/photography-ai

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Professional visual engineering framework for AI-powered image and video creation. Covers prompt engineering, photographic literacy, strategic negation, identity preservation, batch post-processing, and agent orchestration. Use for photorealistic generation, cinematic sequences, and visual production pipelines.

SKILL.md

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Photography AI - Professional Visual Engineering Skills Framework

A comprehensive, structured reference for AI-powered visual creation, covering prompt engineering, photographic literacy, strategic negation, identity preservation, post-processing, and agent orchestration.

Version: 3.0 | Last Updated: April 2026

Context

This skill is for anyone who uses AI to generate, refine, or orchestrate visual content. It treats AI image/video generation as a professional engineering discipline: systematic, physics-informed, quality-gated, and continuously improvable.

Use this skill when:

Generating photorealistic or stylized images with AI
Creating cinematic video sequences from text prompts
Building multi-step visual production pipelines
Designing AI agent workflows for creative teams
Troubleshooting AI generation artifacts (skin, hands, anatomy)

Instructions

Step 1: Understand the Skill Synergy Map

Skills compound. The six categories build on each other:

FOUNDATION
  Technical Prompt Engineering + Photographic Literacy
       |                     |
       v                     v
CONSISTENCY LAYER      REFINEMENT LAYER
  Strategic Negation    Post-Processing &
  + Identity Preserv.     Hybrid Workflows
       |                     |
       v                     v
         ORCHESTRATION LAYER
      AI Agent Design + Production Deploy

Step 2: Apply Technical Prompt Engineering

Structure prompts as blueprints, not keyword lists. Follow the Scaffold Method:

[Subject] + [Action] + [Lighting] + [Lens/Specs] + [Style] + [Quality]

Rules:

Front-load critical elements (AI weights early tokens more heavily)
Use precise photographic vocabulary over vague buzzwords
Use active voice for iterative edits ("remove the background", "add a red hat")
Stack semantic concepts in deliberate order to control interpretation hierarchy

Step 3: Apply Photographic Literacy

Use real-world physics terminology:

Concept	What to Prompt	Effect
Lighting patterns	Rembrandt, Butterfly, Rim, Split, Loop	Sculpt form, mood, dimension
Lens selection	85mm portrait, 35mm standard, 24mm wide	Control perspective and compression
Aperture control	f/1.4 shallow DOF, f/11 full sharpness	Control subject isolation
Advanced rendering	Subsurface scattering, ambient occlusion, ray tracing	Realistic material response
Anamorphic	Horizontal flares, elliptical bokeh, 2.39:1 ratio	Cinematic widescreen look

Step 4: Apply Strategic Negation

Tell the AI what NOT to include:

PROMPT:  visible pores, fine vellus hair, subtle skin variation
NEGATE:  (plastic skin:1.4), (airbrushed:1.2), (cartoon:1.3)

Step 5: Maintain Identity Preservation

For multi-generation consistency:

Seed locking: Fix initial noise pattern with --seed 12345
Reference tools: Use --cref (character) and --sref (style) references
Character weight: --cw 80 preserves face + clothing

Step 6: Post-Processing Workflow

Iterative refinement: Keep seed, change one variable at a time
Inpainting: Fix errors (hands, eyes) via targeted masked editing
External enhancement: Upscale with Topaz, color grade
Quality checklist: hands/feet anatomy, eye direction, lighting coherence

Constraints

NEVER treat AI generation as final output -- always plan for post-processing
NEVER skip the negative prompt step -- uncontrolled generation produces artifacts
NEVER use vague buzzwords when technical terms exist
NEVER forget to test at the target platform's native resolution
NEVER generate character series without seed locking or reference tools

Examples

Example 1: Professional Headshot

PROMPT: Corporate headshot of a CEO, confident expression with subtle warmth, corner lighting establishing authority, dark navy suit against library background, 85mm f/2.8 shallow depth of field, photorealistic, 4K native resolution

NEGATE: (plastic skin:1.4), (airbrushed:1.3), (symmetrical face:1.1), (cartoon:1.2)

Example 2: Cinematic Video Scene

PROMPT: Protagonist discovers crucial clue in dim library, camera: slow dolly zoom from wide establishing shot to tight close-up, lighting: golden hour backlight through window with practical desk lamp fill, 24fps 4K native ProRes

NEGATE: (facial drift:1.4), (background flicker:1.3), (inconsistent props:1.2)