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 skills add mnzralee/claude-multi-agent-architecture --skill ralph-loopgit clone --depth 1 https://github.com/mnzralee/claude-multi-agent-architectureWrote 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/mnzralee/claude-multi-agent-architecture/ralph-loop)<a href="https://agentmods.dev/skills/mnzralee/claude-multi-agent-architecture/ralph-loop"><img src="https://agentmods.dev/badge/skills/mnzralee/claude-multi-agent-architecture/ralph-loop.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 5.1 | $0.00087 | $0.10405 |
| Opus 5 | $0.00044 | $0.05202 |
| Sonnet 5 | $0.00017 | $0.02081 |
| Haiku 4.5 | $0.00009 | $0.01040 |
Grade B, and why
ralph-loop scanned grade B with 2 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 8d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl -sf -X POST $ENDPOINT/quote -d '{"name":"example"}' | jq -e '.tier and .amount' # Criterion 2 Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| **ENV-RO** | Read-only deployment-environment query (a state snapshot or read call against a running environment) | `<query command> | grep -q expected` OR `curl -X POST $ENDPOINT ...` | No | How it starts
The opening of the file, as written. The whole thing — 784 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ralph-Loop Skill
The engineering discipline for endless iteration toward a goal. Bounded iteration with rigor, not non-stop execution. Synthesized from a 9-agent review-board pattern, cluster-of-failures investigation, and execution-and-reversal experience on long-arc work.
When to Use This Skill
Invoke /ralph-loop (or expect auto-surface) when:
- Multi-iteration goals (any goal requiring more than 3 distinct work packages)
- Long-arc execution (more than 3h wall-clock OR risk of context degradation)
- High-stakes work where mistakes or assumptions are costly (infrastructure mutations, deployment-environment operations, production-grade refactors)
- "Get to X/X" goals (10/10 production-ready, full module close, acceptance-gate sweep)
- When the user invokes
/multi-agent-orchestrationfor a multi-day scope - When the user says "non-stop", "marathon", "endless work", "complete everything"
Do NOT use for: single-work-package fixes, one-shot operations, anything under 1h. Use the lighter /multi-agent-orchestration skill for those.
Core Philosophy: Bounded Iteration, Not Non-Stop Execution
The single most important reframe: "non-stop autonomous marathon" framings are structurally unviable per industry consensus.
Sources (the evidence base for this reframe):
- Anthropic, "Effective context engineering for AI agents" (2026): context degrades past 50% utilisation
- arxiv 2603.24755 "SlopCodeBench": empirical anti-pattern accumulation in long-horizon coding tasks
- Alibaba Cloud, "From ReAct to Ralph Loop" (2025): 15-25% premature-completion rate without external verification chain
- Stark Insider, "Claude Code Autonomous Coding Time Hack" (2026): the "Dumb Zone" past 100K-150K tokens
- Anthropic Claude Code best-practices:
/clearcadence canonical - m.academy, "Clear the context window in Claude Code"
The Ralph-Loop pattern reframes long-arc work as:
A chain of 12-15 bounded iterations of 60-120 min each, separated by
/clearand persistent state. Each iteration owns ONE work package with explicit FAIL_TO_PASS + PASS_TO_PASS acceptance commands. Between iterations, in-context memory is cleared; persistent state in.claude/progress/current-module.jsoncarries the handoff.
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.
- 8d ago First seen · 784 lines · 87 tokens per session scan B dc57e35f12b1
ralph-loop is a skill published in the GitHub repository mnzralee/claude-multi-agent-architecture (5 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 10,405 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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