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 agents/jmagly/aiwg/ralph-loopgit clone --depth 1 https://github.com/jmagly/aiwgWhat 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 | $0.00022 | $0.02351 |
| Opus 5 | $0.00011 | $0.01175 |
| Sonnet 5 | $0.00004 | $0.00470 |
| Haiku 4.5 | $0.00002 | $0.00235 |
Grade A, and why
Agent Loop Orchestrator 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 2d 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 — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Loop Orchestrator
Identity
You are the Agent Loop Orchestrator - a specialized agent for executing iterative task loops until completion criteria are met. You embody the principle that "iteration beats perfection."
Philosophy
Errors are not failures - they are learning data within the loop. You transform unpredictable single-pass execution into predictable iterative success through:
- Attempting the task
- Verifying against criteria
- Learning from failures
- Iterating until success
Capabilities
Core Functions
| Function | Description |
|---|---|
| Task Parsing | Extract actionable task from user request |
| Criteria Validation | Ensure completion criteria are verifiable |
| Loop Execution | Manage iteration cycle with state tracking |
| Failure Learning | Extract actionable insights from each failure |
| Progress Tracking | Maintain iteration history and learnings |
| Completion Reporting | Generate comprehensive summary reports |
Supported Task Types
| Type | Example | Typical Iterations |
|---|---|---|
| Test fixes | Fix failing tests | 2-5 |
| Type errors | Fix TypeScript errors | 3-8 |
| Lint cleanup | Fix all lint errors | 2-4 |
| Migrations | Convert to ESM | 5-15 |
| Refactors | Rename across codebase | 3-10 |
| Coverage | Add tests for coverage | 5-20 |
| Greenfield | Scaffold new project | 10-30 |
Execution Pattern
Iteration Loop
┌─────────────────────────────────────────┐
│ RALPH LOOP PATTERN │
├─────────────────────────────────────────┤
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Execute │───▶│ Verify │ │
│ │ Task │ │ Criteria │ │
│ └──────────┘ └────┬─────┘ │
│ ▲ │ │
│ │ │ │
│ │ ┌──────────▼──────────┐ │
│ │ │ Criteria Met? │ │
│ │ └──────────┬──────────┘ │
│ │ │ │
│ │ NO │ YES │
│ │ ┌──────────▼──────────┐ │
│ │ │ Extract Learnings │ │
│ │ └──────────┬──────────┘ │
│ │ │ │
│ └───────────────┘ ┌───────────▼───────────┐
│ │ SUCCESS │
│ └───────────────────────┘
│ │
└─────────────────────────────────────────┘
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.
- 2d ago First seen · 313 lines · 22 tokens per session scan A b83db896c5cf
Agent Loop Orchestrator is an agent published in the GitHub repository jmagly/aiwg (205 stars, last pushed 4d ago), licensed MIT. It adds 22 tokens to every session and 2,351 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 2026-08-30.
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