Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
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.
git clone --depth 1 https://github.com/ruvnet/rufloWrote 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/agents/ruvnet/ruflo/python-specialist)<a href="https://agentmods.dev/agents/ruvnet/ruflo/python-specialist"><img src="https://agentmods.dev/badge/agents/ruvnet/ruflo/python-specialist/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/agents/ruvnet/ruflo/python-specialist"><img src="https://agentmods.dev/badge/agents/ruvnet/ruflo/python-specialist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00007 | $0.00056 |
| Opus 5.5 | $0.00003 | $0.00022 |
| Sonnet 5.5 | $0.00001 | $0.00011 |
| Haiku 4.5 | $0.00001 | $0.00006 |
Grade A, and why
python-specialist 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 1mo 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- python-specialist — 100% identical, 0 lines differ
- python-specialist — 100% identical, 0 lines differ
- python-specialist — 100% identical, 0 lines differ
What it actually says
You are a Python specialist. Focus on: type hints, PEP standards, pythonic idioms, virtual environments. Use dataclasses, prefer pathlib, leverage context managers. Security-first development with vulnerability awareness
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.
- 1mo ago First seen · 10 lines · 7 tokens per session scan A ab0a6c5857fb
python-specialist is an agent published in the GitHub repository ruvnet/ruflo (77,829 stars, last pushed today), licensed MIT. It adds 7 tokens to every session and 56 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-03.
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principle-distiller
Distil durable principles from a packed lifeboat's decision record — each principle citing the record ids or lifeboat paths it rests on. Host-delegated; feeds abcd disembark principles --principles-json.
planning-interviewer
Run an intent's planning interview in a plain Terminal on the person's own route, writing each question for abcd to draw and editing the intent record after each answer. Started by abcd intent interview , never dispatched by the host.
swiss-legal-prompt-engineer
Transforms vague legal queries into structured prompts through Socratic dialogue, recommends optimal workflows, and guides system navigation with persistent cross-session learning.
prompting-research-chat-distiller
Per-agent SOTA prompting research for chat-distiller (Pass B targeted retrieval over specstory transcripts); references baseline 01-general-best-practices.md.