OpenSwarm is an open-source multi-agent system in which specialized agents coordinate to produce deliverables such as presentations, research reports, data visualizations, documents, images, and videos. It is intended for people who want to request complex creative or analytical work from a terminal, with connections to external services. Its catalogue entries define agents, commands, rules, and instructions for those workflows.
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/VRSEN/OpenSwarmWrote 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/rules/vrsen/openswarm/data_analyst)<a href="https://agentmods.dev/rules/vrsen/openswarm/data_analyst"><img src="https://agentmods.dev/badge/rules/vrsen/openswarm/data_analyst/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/rules/vrsen/openswarm/data_analyst"><img src="https://agentmods.dev/badge/rules/vrsen/openswarm/data_analyst.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 | $0.00010 | $0.00594 |
| Opus 5.5 | $0.00004 | $0.00238 |
| Sonnet 5.5 | $0.00002 | $0.00119 |
| Haiku 4.5 | $0.00001 | $0.00059 |
Grade A, and why
data_analyst 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.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scope
- These rules describe how to build and refine
data_analyst_agent. - Keep edits limited to
data_analyst_agent/unless cross-agent collaboration is required. - Use this file to align tooling updates, visualization workflows, and testing practices with Agency Swarm conventions.
Data Analyst Agent Purpose
- Analyze raw or collected data and clearly explain the findings.
- Generate charts, dashboards, and screenshots that highlight trends and support decision-making.
- Visualize the results and analyze them to reveal hidden trends.
Design Workflow
- Review existing analytics and browser utilities under
tools/andtools/utils/. - Determine the format of the input data. If it's an online dashboard service - research api or methods of gathering data from it.
- Construct tools one by one inside the
tools/folder that allow the agent to fetch data and visualize it. Use either@function_toolorBaseTool. - Source credentials via
dotenv; never demand API keys or secrets as runtime inputs. - Ensure each tool returns image outputs as described in the OpenAI Agents documentation: https://openai.github.io/openai-agents-python/tools/#returning-images-or-files-from-function-tools.
- Test each tool individually before proceeding with agent development.
- After tools are ready, create the Data Analyst agent. Begin with
instructions.md, outlining the role, goals, available tools, and usage guidelines.
Customization Guidelines
Depending on needs, you may adjust:
- Data connectors - swap or extend integrations (databases, APIs, files) while preserving consistent return schemas.
- Visualization styles - introduce helper utilities for specialized chart types or interactive dashboards.
- Instruction emphasis - refine prompts to prioritize exploratory analysis, anomaly detection, or KPI reporting.
Customization Examples
- Introducing a warehouse connector (e.g., Snowflake) -> a tool that authenticates via env vars and returns tidy tables for plotting.
- Supporting CSV uploads -> requires a parser tool that validates headers, infers types and plots the data.
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 · 43 lines · 10 tokens per session scan A 15085a571d55
data_analyst is a cursor rule published in the GitHub repository VRSEN/OpenSwarm (2,868 stars, last pushed 2mo ago), licensed MIT. It adds 10 tokens to every session and 594 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 2026-08-30.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
config-validation-and-normalization
Config validation and normalization responsibilities (defineConfig normalizes, the orm section schema validates and resolves paths, the loader reports per-section diagnostics).