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antipas

Agent-first Marketing Image Generation

by Antipas · GitHub ↗ · v0.3.0 · MIT-0
cross-platform ⚠ suspicious
263
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4
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Install in OpenClaw
/install rynjer-image-generation
Description
Generates and refines image prompts, estimates costs, and produces business-ready images with predictable pricing for agent workflows via Rynjer API.
Usage Guidance
This package implements the advertised image-generation flow and has a safe-looking mock mode, but there are important mismatches to review before enabling live mode: 1) The manifest declares no required env vars but the runtime/README require RYNJER_USE_LIVE, RYNJER_BASE_URL, and RYNJER_ACCESS_TOKEN for live operation — expect network calls to the default BASE_URL (https://rynjer.com) if you set live mode. 2) The IMPLEMENTATION.md describes an agent registration / owner-bind flow that issues a ryn_agent_v1_... key; understand and control who performs the owner bind so you don't inadvertently grant scopes. 3) Generation is paid: ensure any token you provide has minimal scopes and you understand pricing behavior (estimate vs generate). Recommendations before installing or running in live mode: - Start in mock mode (do not set RYNJER_USE_LIVE) to verify behavior locally. - If you enable live mode, only set RYNJER_ACCESS_TOKEN when you trust the Rynjer endpoint and have a token with minimal scopes. - Confirm the BASE_URL is the official service you expect; the code will use it directly. - Review the owner-bind/key creation process (out-of-band via UI) so you don't expose credentials to untrusted agents. - If you need stronger guarantees, ask the publisher to update registry metadata to declare the required env vars and clarify the exact auth steps and minimal scopes.
Capability Analysis
Type: OpenClaw Skill Name: rynjer-image-generation Version: 0.3.0 The rynjer-image-generation skill provides a professional and well-documented interface for image generation workflows, including prompt optimization, cost estimation, and result polling. The core logic in `src/mock-runtime.js` is clean and follows standard practices, using Node.js HTTPS requests to communicate with a dedicated backend (rynjer.com) while managing credentials via environment variables. No evidence of data exfiltration, malicious execution, or prompt injection was found in the code or the instructional markdown files.
Capability Assessment
Purpose & Capability
The skill's name/description (agent-first marketing image generation) matches the code and SKILL.md: prompt rewrite, cost estimate, generate, and poll are implemented. The runtime routes models and applies templates consistent with the marketing use cases. However, the package registry metadata declares no required env vars/credentials while the README/IMPLEMENTATION.md and runtime clearly support a live mode that needs RYNJER_USE_LIVE, RYNJER_BASE_URL and RYNJER_ACCESS_TOKEN (or an agent-created ryn_agent_v1_... key). That omission is an inconsistency to be aware of.
Instruction Scope
SKILL.md and code limit operations to prompt rewriting, local template lookup, cost estimation, generation requests, and polling the Rynjer API. The IMPLEMENTATION.md documents an agent registration/key-create flow (Ed25519 keypair generation and owner bind via UI) which is an auth onboarding step rather than hidden behavior; it may require out-of-band owner interaction. The instructions do not ask the agent to read unrelated host files or system secrets.
Install Mechanism
This is essentially an instruction-only skill with a small mock runtime JS file included. There is no install spec, no downloads, and nothing that writes executables or pulls remote archives — low install risk.
Credentials
The declared requirements list no environment variables or primary credential, but the runtime and documentation require (for live mode) RYNJER_USE_LIVE, RYNJER_BASE_URL, and RYNJER_ACCESS_TOKEN (or an agent-created API key). That mismatch is problematic because a user may install without realizing live-mode network calls will use an access token if provided. The number and type of env vars requested by the code (one bearer token and a base URL toggle) are proportionate to the purpose, but they should be declared up-front and the skill should clearly require minimal scopes for live usage.
Persistence & Privilege
The skill does not request always: true and does not modify other skills or system configs. It supports autonomous invocation by default (platform normal), which combined with live API access increases blast radius only if you enable RYNJER_ACCESS_TOKEN — a normal tradeoff for networked skills.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install rynjer-image-generation
  3. After installation, invoke the skill by name or use /rynjer-image-generation
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.3.0
Added 4 marketing templates (landing/ad/blog/ecommerce) and smart platform size recommendations. Updated positioning and documentation.
v0.2.0
Redesigned positioning: focused on short-path agent image generation for marketing use cases. Clear differentiation from full creative platforms.
v0.1.1
Tighten marketplace copy: clearer positioning, happy path, and live-verified credibility for soft launch
v0.1.0
Initial soft launch: agent-first image generation skill with prompt rewrite, cost estimate, live-verified auth flow, and polling support
Metadata
Slug rynjer-image-generation
Version 0.3.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 4
Frequently Asked Questions

What is Agent-first Marketing Image Generation?

Generates and refines image prompts, estimates costs, and produces business-ready images with predictable pricing for agent workflows via Rynjer API. It is an AI Agent Skill for Claude Code / OpenClaw, with 263 downloads so far.

How do I install Agent-first Marketing Image Generation?

Run "/install rynjer-image-generation" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Agent-first Marketing Image Generation free?

Yes, Agent-first Marketing Image Generation is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Agent-first Marketing Image Generation support?

Agent-first Marketing Image Generation is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Agent-first Marketing Image Generation?

It is built and maintained by Antipas (@antipas); the current version is v0.3.0.

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