enterprise-press-batch

enterprise-press-batch is a skill for Claude Code from PicsArt/gen-ai-skills. It costs 18 tokens per session (2,258 once invoked), scanned A, original, MIT.

A workflow for producing complete press-release image packages, including hero images, executive headshots, product photos, event images, crops, metadata, and multiple resolutions.

In plain words
What is it for?
Use it for product launches, earnings announcements, executive changes, crisis responses, headshot updates, and event coverage distributed to services such as Reuters, AP, Getty, or corporate newsrooms.
Why use it?
It organizes the many versions needed for news wires, websites, print, and social media, including rights information and embargo handling. An embargo is a request not to publish material before a stated time.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is --concurrency 3 --output ./runs/press-2026-04-execs.

Part of the picsart plugin — 23 skills, 2 MCP servers shipped together

Good fit Use it for product launches, earnings announcements, executive changes, crisis responses, headshot updates, and event coverage distributed to services such as Reuters, AP, Getty, or corporate newsrooms.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/PicsArt/gen-ai-skills
agentmods
npx agentmods add skills/picsart/gen-ai-skills/enterprise-press-batch

Made for: Claude Code.

Or install picsart, the plugin that ships this one along with the rest of its 23 skills, 2 MCP servers.

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 enterprise-press-batch

README.md
[![agentmods](https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-press-batch/github.svg)](https://agentmods.dev/skills/picsart/gen-ai-skills/enterprise-press-batch)
Your own site
<a href="https://agentmods.dev/skills/picsart/gen-ai-skills/enterprise-press-batch"><img src="https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-press-batch/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 enterprise-press-batch

Your own site · 80×15
<a href="https://agentmods.dev/skills/picsart/gen-ai-skills/enterprise-press-batch"><img src="https://agentmods.dev/badge/skills/picsart/gen-ai-skills/enterprise-press-batch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,258 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.
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 $0.00018 $0.02258
Opus 5 $0.00009 $0.01129
Sonnet 5 $0.00004 $0.00452
Haiku 4.5 $0.00002 $0.00226

Measured 11d ago against content hash 3386ae1dc71d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

enterprise-press-batch 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 11d ago.

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.

skills/enterprise-press-batch/SKILL.md · 190 lines

How it starts

The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Enterprise Press Batch

Produce full press-release asset bundles — hero, executive headshots, product shots, event-ready horizontal and vertical crops — with embargo controls, wire-service naming conventions, EXIF rights metadata, and multi-resolution exports. Built for comms / PR teams shipping to Reuters, AP, Getty, and corporate newsrooms under embargo.


When to Use

  • Product launch, earnings announcement, exec change, crisis response — assets needed across wire + web + print + social in parallel
  • Embargoed announcement requiring strict asset handling until lift
  • Multi-publication distribution where each wire has different resolution + EXIF requirements
  • Executive headshot refresh across a leadership team (consistent lighting, backgrounds, crops)
  • Event kits: pre-event (concept), during-event (placeholder + real), post-event (recap)

Do not use for: social-only launches (use gen-ai-workflows launch-kit), single-asset news posts.


Prerequisites

Before the batch runs:

  1. Release type + embargo — announcement date/time, embargo lift, distribution list. Embargoed runs must write to a locked Drive folder.
  2. Asset scope — which of: 1 hero, N exec headshots, M product shots, event horizontal, event vertical, social derivatives. Count per type.
  3. Distribution targets — wire service specs (AP: 3000px long edge JPEG, Getty: TIFF, Reuters: sRGB embedded). Determines resolution + format per pack.
  4. Rights metadata — credit line, copyright owner, usage rights, caption, contact. Written to EXIF/IPTC.
  5. Approval chain — legal, comms lead, exec approval for headshots? Who signs off before embargo pack ships?
  6. Audit trail — does the archive need the original brief + manifest + results for seven years (SOX-adjacent)?

How to Run

Six steps. Embargo handling is non-negotiable.

  1. Scope the bundle — write the deliverable matrix: asset × pack (wire / web / print / social) × resolution. Costs scale by cells, not by source images.
  2. Generate / ingest sources — AI-generated heroes via gen-ai generate; real executive photography ingested via gen-ai upload. Enhance with topaz-upscale-image if source resolution is below print spec.
  3. Embargo lock — create a restricted archive location. Everything until lift lands here. Never ship intermediate drafts.
  4. Batch exports — one manifest per pack (wire / web / print / social), each with the correct resolution, format, and color profile. Watermark drafts; never watermark final approved wire assets.
  5. Stamp EXIF / IPTC — rights, credit, caption, embargo notice injected on every file. Use exiftool as a post-batch step.
  6. Deliver — at embargo lift, promote from the locked folder to the public press-kit URL. Archive the manifest + results.json + checksum manifest for the audit trail.

Read the full file on GitHub · 190 lines

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. 11d ago First seen · 190 lines · 18 tokens per session scan A 3386ae1dc71d

Subscribe to this mod's changes

enterprise-press-batch is a skill published in the GitHub repository PicsArt/gen-ai-skills (4 stars, last pushed 14d ago), licensed MIT. It adds 18 tokens to every session and 2,258 once invoked, about $0.0001 per session on Opus 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 2026-08-31.

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