# Photo Review

> Photo review

- Skill: `campbellsmurphy/photo-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add campbellsmurphy/photo-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/campbellsmurphy/photo-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: campbellsmurphy (https://skillmd.com/u/campbellsmurphy)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/campbellsmurphy/photo-review

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# Photo review

Fan-out vision review of a photo folder. Supports four modes, run any combination:

- **cull**: flag rejects: blurry/out-of-focus, eyes-closed, near-identical burst shots, exact duplicates, badly exposed.
- **caption**: short description plus searchable tags per photo (people count, scene, setting, notable objects). Good for alt-text and organising.
- **organise**: propose event grouping plus date/event-based filenames and a folder structure.
- **best-of**: pick the top N keepers from the set for sharing or printing, with one-line reasons.

This skill expects heavy fan-out. Spawning many agents is the intended design. If your setup rate-limits or costs per agent, decide your own ceiling before starting.

## Procedure

1. **Get the folder.** If the user didn't give a path, ask. Confirm whether to recurse into subfolders.

2. **Enumerate and dedupe cheaply first** (saves vision tokens):
   ```
   python3 ~/.claude/skills/photo-review/list-images.py "<folder>" [--recursive]
   ```
   This returns per-image `{path, bytes, mtime, w, h, md5}` plus `exact_dup_groups`. Drop all-but-one of each exact-dup group before any vision work; note them in the report rather than re-reviewing.

3. **Batch.** Chunk the remaining images into batches of roughly 8 to 12 (vision context fills fast). Burst shots cluster by near-equal `mtime`: keep a burst in the same batch so one agent can compare them for near-duplicates and pick the sharpest.

4. **Fan out** one agent per batch (`Agent` tool, or `Workflow` with a pipeline for large sets). Each agent `Read`s its image paths (Read renders images visually) and returns STRUCTURED JSON for the requested mode(s). Per-image fields, as applicable:
   - `path`, `verdict` (keep | reject | maybe), `reasons` (blurry/eyes-closed/dup-of:<path>/exposure/…)
   - `caption`, `tags[]`
   - `suggested_name`, `event`
   - `score` (0 to 100 for best-of)

5. **Synthesize.** Merge agent results. For **best-of**, rank by score across all batches and take the top N. For **organise**, propose the folder/rename plan as a table. Always produce a single report.

6. **Apply only on confirmation.** Never move, rename, or delete files until the user approves the plan. When applying: prefer copy-to-`_keepers/` or rename-in-place; for deletes, move rejects to a `_rejects/` folder rather than `rm` so it's reversible. Show counts before and after.

## Notes
- HEIC reads fine via Read; if an agent can't, convert with `sips -s format jpeg in.heic --out /tmp/x.jpg`.
- `list-images.py` uses macOS `sips` for dimensions. On Linux, swap it for `identify` (ImageMagick) or Pillow; everything else is stdlib.
- Scaffolded generically: it works on any folder, and assumes no default path.
- Keep batches small and fan out wide rather than using few large agents. Parallel wins wall-clock and each agent stays within vision context.