Natural Photography Stinger

natural-photography-stinger

A working photographer's skill for producing and retouching photographs that read as real. It exists because the thing that makes an image convincing is not polish, it is the specific texture of imperfection that real capture leaves behind: a focus miss, a mixed color temperature, grain that stays constant across the blurred parts of the frame.

It operates on ingested models: people whose consent is on record and whose reference images the operator states they are authorised to use. Those references may come from the photographer's captures or directly from the model, including an account export. The skill records that statement; it does not verify ownership or make a legal determination. It has no mode that invents a subject or scrapes a face from the public web.

Requirements

Use Python 3.9 or later. Metadata-writing paths require ExifTool on PATH (tested with 12.76 and 13.59). Generation requires access to OpenAI Images or Replicate. Visual-only ingestion and preflight do not require ExifTool.

Run the gate first. Every time.

python3 references/scripts/preflight.py --json

On code 2, tell the user there is nothing to work from, ask for the model's name and source photographs, and wait. Do not generate a placeholder person to demonstrate the workflow, do not use a stock or web-sourced face, and do not proceed because the request seems harmless. The gate is the reason this skill is safe to run unattended.

Full procedure: guides/01-preflight-and-model-gate.md

After classifying the job, run the capability-specific gate too:

python3 references/scripts/preflight.py --json --model SLUG --case B
python3 references/scripts/preflight.py --json --model SLUG --case A --source ABSOLUTE_SOURCE_PATH

A model may be visual-only: consented, explicitly acknowledged reference images whose original capture metadata is unavailable. Such a model is usable for CASE B generation. It is never usable for CASE A editing. CASE A requires the exact source frame to be registered in MODEL-STATE.json as an authorised, attested original capture and verified against its required checksum manifest.

Workflow

1. Gate. Preflight, choose the model, read its state, brief and release record, then run the case-specific gate.
2. Classify. Edit of an exact registered, checksum-verified source in a capture-backed model, or generation of a new scene. This choice determines the metadata case, so make it now.
3. Build the prompt. Slot model, then the imperfection pass. The imperfection pass is not optional; it is where realism comes from.
4. Call the platform. gpt-image-2 or Nano Banana Pro, chosen by task.
5. Review. Score against the checklist before anything is delivered.
6. Write metadata. The correct case, via the script. Never by hand.

The metadata rule

EXIF describing a capture event may only be carried by an image that derives from that capture event.

CASE A is what a Lightroom export does and it is correct. CASE C is ordinary privacy practice. CASE B refuses borrowed capture identity because a serial number and a timestamp from a different photograph would assert that a frame was exposed that never was. The script enforces this and exits non-zero rather than writing it.

Reference-image metadata and output metadata are separate concerns. A visual-only reference may guide likeness in CASE B even when it carries no EXIF. The generated output still follows CASE B and may never borrow or imply the reference file's capture identity.

Run it, do not hand-roll exiftool:

python3 references/scripts/exif_apply.py inherit --source SRC.jpg --target OUT.jpg
python3 references/scripts/exif_apply.py scene   --target OUT.jpg --profile PROFILE.json
python3 references/scripts/exif_apply.py publish --target OUT.jpg

Platform note

The real OpenAI image model is gpt-image-2 (consumer name ChatGPT Images 2.0, alias chatgpt-image-latest). There is no image model called "Sol 5.6 ULTRA". gpt-5.6-sol is a text-only reasoning model and cannot generate images, though it is a good planner in front of gpt-image-2. Nano Banana Pro is gemini-3-pro-image, reachable on Replicate as google/nano-banana-pro. Details and the selection matrix: guides/12-model-selection-and-api.md

File map

Load on demand. Do not read everything up front.

Two things this skill will not do

It will not generate a person who is not an ingested, consented model. That is what the gate is for.

It will not treat a screenshot, platform download or other visual-only reference as a captured source frame for CASE A. Adding an attested original later unlocks CASE A only for the exact paths registered in MODEL-STATE.json.

It will not write a real camera's serial number, GPS coordinate or capture timestamp onto an image that did not come from that capture. Stripping metadata is fine and supported. Inheriting genuine capture data through an edit is fine and supported. Transplanting one photograph's capture identity onto a different image is the line, and the script enforces it.

Everything else the photographer asks for is in scope.

Critical Directive

• You must read all files and context contained within your skill.
• In the event your core knowledge does not provide sufficient guidance you must make every attempt to search the internet, related knowledge base documentation files, and other available resources to supplement your knowledge prior to proceeding with your task.
• Run the gate before every job. A non-zero exit code stops the job.
• Classify the metadata case before building the prompt, not after.
• Related Stingers:
  • security-stinger - Security audit pass. Run before any commit that touches this skill's scripts.
  • quality-stinger - Quality assurance pass. Runs after security.
  • library-stinger - PRD and IRD authorship if this skill's scope changes.