photo-metadata

photo-metadata is a skill for Claude Code, Codex from jamditis/claude-skills-journalism. It costs 38 tokens per session (4,133 once invoked), scanned A, original, MIT.

A workflow for embedding photo information such as captions, credits, alternative text, licensing, GPS removal, AI-source labels, and C2PA credentials directly into image files.

In plain words
What is it for?
Use it to write EXIF, IPTC, and XMP metadata with ExifTool, prepare images for photo-management or news systems, and add provenance information.
Why use it?
It keeps important ownership, accessibility, location, and origin details with the image when it is copied, forwarded, or uploaded elsewhere.

Skill for Claude CodeCodex

Written for Claude Code and Codex: shipped in a Claude Code plugin, but also agents/openai.yaml present.

Part of the journalism-core plugin — 16 skills shipped together

Good fit Use it to write EXIF, IPTC, and XMP metadata with ExifTool, prepare images for photo-management or news systems, and add provenance information.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jamditis/claude-skills-journalism/photo-metadata
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add jamditis/claude-skills-journalism --skill photo-metadata
Clone the repo
git clone --depth 1 https://github.com/jamditis/claude-skills-journalism

Made for: Claude Code, Codex.

Or install journalism-core, the plugin that ships this one along with the rest of its 16 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for photo-metadata

README.md
[![agentmods](https://agentmods.dev/badge/skills/jamditis/claude-skills-journalism/photo-metadata/github.svg)](https://agentmods.dev/skills/jamditis/claude-skills-journalism/photo-metadata)
Your own site
<a href="https://agentmods.dev/skills/jamditis/claude-skills-journalism/photo-metadata"><img src="https://agentmods.dev/badge/skills/jamditis/claude-skills-journalism/photo-metadata/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for photo-metadata

Your own site · 80×15
<a href="https://agentmods.dev/skills/jamditis/claude-skills-journalism/photo-metadata"><img src="https://agentmods.dev/badge/skills/jamditis/claude-skills-journalism/photo-metadata.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,133 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.04133
Opus 5.5 $0.00015 $0.01653
Sonnet 5.5 $0.00008 $0.00827
Haiku 4.5 $0.00004 $0.00413

Measured 2mo ago against content hash 50a2b4f26148, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

photo-metadata scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (embed.py, test_embed.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

journalism-core/skills/photo-metadata/SKILL.md · 182 lines

How it starts

The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Photo metadata

Overview

Metadata embedded in an image file travels with the file. Photo CMSs (Photo Mechanic, Lightroom, Capture One) and wire intake systems read a photo's caption, credit, and rights from its IPTC and XMP blocks, not from a separate document or the email it arrived in. If the caption, credit, alt text, and license are not inside the file, they are gone the moment the photo is downloaded, forwarded, or re-uploaded.

One exiftool pass writes the EXIF, IPTC, and XMP layers together and leaves every other tag (camera settings, shot time) untouched. Modern software reads XMP first, legacy IPTC-IIM second, EXIF only for date and GPS, so write XMP everywhere and add IIM as a compatibility copy on JPEG/TIFF (HEIC, AVIF, and WebP have no IIM slot at all; see reference.md).

Two things changed since this workflow was "caption, credit, copyright." First, how an image was made now belongs in the metadata: the IPTC Digital Source Type field labels a camera photo versus an AI-generated one, and platforms (Meta, Google) and the EU AI Act increasingly read it. Second, cryptographic provenance (C2PA / "Content Credentials") is arriving on wire images and cameras, a signed layer exiftool can read but not write. Both are covered below.

A capable model already knows the field names. The hard part is not the mechanics, it is the judgment below. Lead with that.

When to use

  • Prepping press photos for a wire so partner newsrooms can search, credit, and republish them
  • Adding required photographer attribution and a reuse license before publishing or sharing
  • Labeling how an image was made, a straight photo, an AI-generated illustration, an AI-edited composite
  • Batch-tagging a shoot (a folder of images)
  • Making images accessible (embedded alt text) and rights-clear (copyright or Creative Commons)
  • Reading and sanity-checking the C2PA Content Credentials on an image that arrived from an agency

When not to use: editing pixels (this is metadata only); writing alt text for an HTML <img> (use accessibility-compliance); preserving web pages as evidence (use web-archiving); signing a Content Credential (exiftool can't, use c2patool, below).

Read the full file on GitHub · 182 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 182 lines · 38 tokens per session scan A 50a2b4f26148

Subscribe to this mod's changes

photo-metadata is a skill published in the GitHub repository jamditis/claude-skills-journalism (448 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 4,133 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

photo-metadata compared with skills from other repositories

photo-metadata next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
photo-metadatajamditis/claude-skills-journalismSkill4482d agoMITA38Claude Code, Codex
running-cluster-experimentschgagne/claude-skills-researchSkill5todayMITA72Claude Code, Codex
explaining-derivationschgagne/claude-skills-researchSkill5todayMITA109Claude Code, Codex
self-surveying-literaturechgagne/claude-skills-researchSkill5todayMITA136Claude Code, Codex
food-researchPangenomeAI/academic-skills-food-nutritionSkill412mo agoMITA154Claude Code
data-table-formatterur-grue/autopunk-media-skillsSkill432mo agoMITA46Claude Code
food-chemistryPangenomeAI/academic-skills-food-nutritionSkill412mo agoMITA110Claude Code