Ralph Orchestrator is a framework that repeatedly runs AI-agent tasks until they finish or reach an iteration limit. Developers use it to coordinate autonomous coding work through command-line, web-dashboard, and MCP-server interfaces, with state managed per workspace. The catalogue entries provide agent skills, agents, instructions, and plugins for operating Ralph.
Borrowing it
Nothing to install: this file belongs to mikeyobrien/ralph-orchestrator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mikeyobrien/ralph-orchestrator/main/.claude/skills/pr-demo/SKILL.mdgit clone --depth 1 https://github.com/mikeyobrien/ralph-orchestratorWrote 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.
[](https://agentmods.dev/skills/mikeyobrien/ralph-orchestrator/pr-demo)<a href="https://agentmods.dev/skills/mikeyobrien/ralph-orchestrator/pr-demo"><img src="https://agentmods.dev/badge/skills/mikeyobrien/ralph-orchestrator/pr-demo/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.
<a href="https://agentmods.dev/skills/mikeyobrien/ralph-orchestrator/pr-demo"><img src="https://agentmods.dev/badge/skills/mikeyobrien/ralph-orchestrator/pr-demo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 195 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.01406 |
| Opus 5 | $0.00017 | $0.00703 |
| Sonnet 5 | $0.00007 | $0.00281 |
| Haiku 4.5 | $0.00003 | $0.00141 |
Grade A, and why
pr-demo 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.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Demo Creation
Overview
Create polished terminal demos for PRs using asciinema recordings converted to GIF. The workflow: script → record → convert → embed.
Tool Selection
| Goal | Tool Chain | Output |
|---|---|---|
| CLI demo for GitHub PR | asciinema → agg | GIF (< 5MB) |
| Smaller file needed | asciinema → svg-term-cli | SVG (< 500KB) |
| TUI screenshot | tmux → freeze | SVG/PNG |
Default choice: asciinema + agg (best compatibility, GitHub renders GIFs natively)
Prerequisites
# Install tools (macOS)
brew install asciinema
cargo install --git https://github.com/asciinema/agg
npm install -g svg-term-cli # Optional: for SVG output
Workflow
1. Script Your Demo (REQUIRED)
Before recording, write a brief script:
## Demo: [feature name]
Duration: ~20-30 seconds
1. [0-3s] Show command being typed
2. [3-10s] Command executes, show key output
3. [10-25s] Highlight the "aha moment" - what makes this valuable
4. [25-30s] Clean exit or final state
Keep it short. 20-30 seconds max. Show ONE thing well.
2. Prepare Environment
# Clean terminal state
clear
export PS1='$ ' # Simple prompt
export TERM=xterm-256color # Consistent colors
# Hide sensitive info (API keys, paths with usernames)
Terminal size: 100x24 (readable when scaled down)
3. Record
# Record to .cast file
asciinema rec demo.cast --cols 100 --rows 24
# Execute your scripted demo
# Press Ctrl+D or type 'exit' when done
Tips:
- Type at readable speed (not too fast)
- Pause briefly after key moments
- If you make a mistake, start over (editing is harder than re-recording)
4. Convert to GIF
# Basic conversion (recommended)
agg demo.cast demo.gif
# With speed adjustment (1.5x faster)
agg --speed 1.5 demo.cast demo.gif
# With custom font size for readability
agg --font-size 14 demo.cast demo.gif
Alternative - SVG (smaller files):
svg-term --in demo.cast --out demo.svg --window
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.
- 11d ago First seen · 206 lines · 35 tokens per session scan A b6f73cdeb684
pr-demo is a skill published in the GitHub repository mikeyobrien/ralph-orchestrator (3,132 stars, last pushed today), licensed MIT. It adds 35 tokens to every session and 1,406 once invoked, about $0.0002 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 2026-08-30.
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