CommunityImagegithub.com

Hand Tinted Photo Memory

Restyle a supplied photograph as a hand-tinted photo memory, preserving monochrome tones and delicately applying transparent color to clothing and faces while keeping photographic grain visible.

What is Hand Tinted Photo Memory?

The 'Hand Tinted Photo Memory' skill is designed for AI agents that support reference-image editing, such as those used in image generation tools. It guides the agent to transform a user-supplied photograph into a hand-tinted photo memory style. This technique first retains the monochrome tonal structure of the original photo, then applies a thin, transparent layer of color selectively to clothing and facial areas, allowing the underlying photographic grain to remain visible. The skill emphasizes strict preservation of the original composition, all observed subjects (people), their identities, and the photographic tonal modeling. It provides detailed, ordered instructions (including a 'recipe.yaml' file) for the agent to follow, ensuring that the output adheres to the specific medium mechanism of hand-tinted photographs. The style avoids creating generic filters or fabricating historical context, damage, or missing subjects. It includes comprehensive guidelines for input analysis, preservation constraints, and acceptance criteria to ensure the generated image maintains the integrity of the original photograph while achieving the desired aesthetic.

Works with~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/MuziGeek/photo-style-skills/tree/HEAD/skills/hand-tinted-photo-memory

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Preview

From the skill README

Hand Tinted Photo Memory preview
Hand Tinted Photo Memory preview

Documentation

What does Hand Tinted Photo Memory do?

First preserve the monochrome tones of the photograph, then lightly apply a thin hand-tinted color to clothing and the face, with the color layer not fully covering the silver grains.

A monochrome photographic print selectively tinted with delicate hand-applied transparent color. Preserve the original photographic composition and all observed people; apply tint within broad source-derived color regions.

This method is adapted from acesjohnny's CC-BY-4.0-licensed instructions. Attribution, original source, and modifications are recorded in provenance.json; the complete upstream license and copyright notices are in LICENSE-THIRD-PARTY.txt.

Original attribution: Copyright (c) 2026 acesjohnny Adapted by MuziGeek from acesjohnny / GPT Image Style Atlas. Original style prompt licensed CC BY 4.0; modifications recorded below.

Input Analysis and Preservation

  1. Inspect the supplied image; request an image only when none is available. Never substitute a repository example for the user's input.
  2. Inspect the real subjects, identity, counts, attachments, overlaps, light and framing. Distinguish preserved attributes from explicitly permitted transformations using this Skill's preservation and geometry rules; sample descriptions are not facts about user input.
  3. Use an available reference-image editing tool. Combine the observed facts with the ordered mechanisms in recipe.yaml; its model-family label does not require an installation script.
  4. Keep the input ratio by default; change composition or geometry only as requested by the user and expressly permitted below. The recipe's 4:5 ratio is a catalog-sample default, not permission to crop protected subjects.
  5. Compare output with input and correct observed failures. Report only visual checks actually completed; stronger styling must not override identity or scene integrity.

Style Mechanism

  • First preserve the monochrome tones of the photograph, then lightly apply a thin hand-tinted color to clothing and the face, with the color layer not fully covering the silver grains.
  • Identifying features: monochrome photo base, thin transparent color, visible grain.
  • Medium anchor: A monochrome photographic print selectively tinted with delicate hand-applied transparent color.
  1. medium — A monochrome photographic print selectively tinted with delicate hand-applied transparent color.
  2. composition — Preserve the original photographic composition and all observed people; apply tint within broad source-derived color regions.
  3. texture — Keep fine photographic grain visible beneath uneven translucent brush tint, with restrained edge feathering.
  4. palette — Use a warm monochrome base and subtle source-related color on skin, garments and a few meaningful objects.
  5. lighting — Retain the original photographic tonal modeling; the tint must not repaint anatomy or replace lighting.
  6. detail — Keep observed faces and hands photographically recognizable; add no scratches, damage, invented history or retro costumes.
  7. finish — Deliver one finished image at the input aspect ratio, without comparison panels, unrequested text, logos, signatures or watermarks.

Preservation Constraints

Apply these constraints to observed content; they never authorize inventing absent subjects.

  • Preserve all observed people and photographic tones; add only thin selective tint without fabricated damage or historical context.
  • Source subject count, recognizable identity, essential openings and connected anatomy.
  • The complete main subject within the original frame and the intended relative placement of objects.

Applicability and Geometry

  • Applies to: Group.
  • Preserve all observed people and photographic tones; add only thin selective tint without fabricated damage or historical context.
  • Structural changes not explicitly allowed above are outside this method. This is not permission to reproduce a named artist, brand or existing artwork.

Avoid

  • Invented people, duplicate main subjects, extra limbs, disconnected anatomy or accidental clipping.
  • Unrequested titles, logos, signatures, watermarks, fabricated labels or before-and-after panels.
  • A generic filter without the requested medium-specific physical or optical mechanism.

Acceptance

  • Keep fine photographic grain visible beneath uneven translucent brush tint, with restrained edge feathering.
  • Preserve all observed people and photographic tones; add only thin selective tint without fabricated damage or historical context.
  • The source subject remains legible with coherent connections and a visibly implemented medium-specific mechanism.
  • One complete finished image without unrelated lettering, brands, signatures or watermarks.

Recipe, Provenance and License

  • recipe.yaml: structured image-editing recipe.
  • provenance.json: original authorship, method context and example status.
  • LICENSE: MIT applies to MuziGeek's original contributions.
  • LICENSE-THIRD-PARTY.txt: acesjohnny · CC-BY-4.0. Adapted material retains these terms and attribution.
  • Example before/after images are AI-generated, not photographs of real people or evidence of physical craft production. Results vary by model and input.
  • MIT grants no rights to user inputs, third-party works, likenesses or trademarks, and does not override generation-service terms; example terms are recorded separately in provenance.

Example Status

ready: the source and effect have recorded real generation and approved visual reviews. COS-hosted examples also retain upload readback receipts; their image bytes are not bundled. Ready does not mean the website is released; image links may return 404 until the public media allowlist is published.

AI-generated SourceApproved Effect

Individual skills in this repo

This repo contains 4 individual skills — each has its own dedicated page.

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