agentsclimarketplace

Photography ai

Skill marktantongco/opencodelinux/skills/photography-ai

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.From its SKILL.md

Install
npx -y skills add marktantongco/opencodelinux --skill photography-ai

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.8 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

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:

ConceptWhat to PromptEffect
Lighting patternsRembrandt, Butterfly, Rim, Split, LoopSculpt form, mood, dimension
Lens selection85mm portrait, 35mm standard, 24mm wideControl perspective and compression
Aperture controlf/1.4 shallow DOF, f/11 full sharpnessControl subject isolation
Advanced renderingSubsurface scattering, ambient occlusion, ray tracingRealistic material response
AnamorphicHorizontal flares, elliptical bokeh, 2.39:1 ratioCinematic 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:

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

Step 6: Post-Processing Workflow

  1. Iterative refinement: Keep seed, change one variable at a time
  2. Inpainting: Fix errors (hands, eyes) via targeted masked editing
  3. External enhancement: Upscale with Topaz, color grade
  4. 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)

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.