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.
npx agentmods add skills/junghwayang/oh-my-codex/ralphnpx skills add junghwaYang/oh-my-codex --skill ralphgit clone --depth 1 https://github.com/junghwaYang/oh-my-codexWrote 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/junghwayang/oh-my-codex/ralph)<a href="https://agentmods.dev/skills/junghwayang/oh-my-codex/ralph"><img src="https://agentmods.dev/badge/skills/junghwayang/oh-my-codex/ralph.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00000 | $0.00375 |
| Opus 5 | $0.00000 | $0.00187 |
| Sonnet 5 | $0.00000 | $0.00075 |
| Haiku 4.5 | $0.00000 | $0.00038 |
Grade A, and why
ralph 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 4d 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.
What it actually says
Ralph Skill
Persistent execution mode that never gives up.
When to Use
- Tasks that MUST complete fully
- Stubborn bugs that resist fixing
- Complex refactoring with many errors
- Any task where partial completion is unacceptable
When NOT to Use
- Exploratory work
- Tasks where "good enough" is acceptable
- Time-sensitive quick fixes
Philosophy
Ralph is named after the "ralph mode" concept: relentless persistence.
"Never give up. Never surrender."
Workflow
1. ATTEMPT
Execute the task
2. VERIFY
Check if truly complete
- All tests pass?
- No errors remaining?
- Meets all criteria?
3. FIX (if needed)
Address failures
4. LOOP
Repeat until verified complete
(No max attempts - ralph doesn't quit)
Verification Checklist
Before declaring complete:
- Code compiles without errors
- All tests pass
- No TypeScript/lint errors
- Functionality verified
- Edge cases handled
Usage
ralph: fix all TypeScript errors in the project
ralph: refactor the auth module to use the new API
ralph: migrate all components to the new design system
Behavior
- No partial completion - Either fully done or still working
- Self-healing - Automatically fixes issues it creates
- Progress tracking - Reports status regularly
- Escalation - Only asks for help when truly stuck
Notes
- Ralph includes ultrawork capabilities (parallel execution)
- Combines persistence with efficiency
- Will loop indefinitely until success
- Use for critical tasks only (token-intensive)
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.
- 4d ago First seen · 76 lines · 0 tokens per session scan A ee415c70c593
ralph is a skill published in the GitHub repository junghwaYang/oh-my-codex (5 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 375 tokens. 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.
Other skills, from other repositories
systematic-debugging
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brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…