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 topprismdata/cultivating-ml-agent --skill multi-agent-rolesgit clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/multi-agent-roles)<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/multi-agent-roles"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/multi-agent-roles/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/topprismdata/cultivating-ml-agent/multi-agent-roles"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/multi-agent-roles.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 | $0.00051 | $0.00810 |
| Opus 5 | $0.00026 | $0.00405 |
| Sonnet 5 | $0.00010 | $0.00162 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
multi-agent-roles 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 9d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Role Architecture
Context
A single agent stuffed with all system prompts + 43+ skills gets confused (attention fragmentation). agy verified that splitting into Architect / Coder / Critic / Researcher / Integrator roles reduces hallucination and improves complex pipeline stability. Inspired by AutoGen, CrewAI, and MetaGPT.
The core insight: single agent = single bottleneck; role separation enables parallel work and specialized expertise.
Guidance
Built-in Roles
from framework.src.agents import Orchestrator
orch = Orchestrator(llm_call=my_llm_function)
# 标准 ML pipeline: Architect → Coder → Critic with revision loop
pipeline = orch.create_standard_ml_pipeline()
outputs = orch.run_pipeline("standard-ml",
"Predict Spaceship Titanic Transported",
max_revisions=2)
# → Architect outputs plan → Coder writes code → Critic reviews
# → If REJECT, Architect revises with Critic feedback
Available Roles
| Role | Responsibility | Forbidden |
|---|---|---|
data-architect |
Plan EDA/FE/model strategy | Write code, submit |
ml-coder |
Implement plans as Python | Modify plan, submit |
continuity-critic |
Catch leakage/overfitting | Fix code, submit |
knowledge-researcher |
Find papers + Kaggle insights | Recommend implementations |
Custom Pipeline
from framework.src.agents import Pipeline
custom = (Pipeline(name="research-then-code", description="...")
.add_step("knowledge-researcher", input_from=None, output_to="data-architect")
.add_step("data-architect", input_from="knowledge-researcher", output_to="ml-coder")
.add_step("ml-coder", input_from="data-architect"))
orch.register_pipeline(custom)
orch.run_pipeline("research-then-code", "Find best technique for X")
Why This Matters
| Single Agent | Multi-Agent |
|---|---|
| 1 confused "do everything" prompt | 4 focused "do one thing" prompts |
| Hallucinates across domains | Specializes per role |
| One failure breaks everything | Failure isolated per step |
| Hard to debug | Each step traceable |
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.
- 9d ago First seen · 89 lines · 51 tokens per session scan A b4082036479e
multi-agent-roles is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 12d ago), licensed MIT. It adds 51 tokens to every session and 810 once invoked, about $0.0003 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 2026-08-31.
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