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 rules/vrsen/openswarm/data_analystgit clone --depth 1 https://github.com/VRSEN/OpenSwarmWhat 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 | $0.00010 | $0.00594 |
| Opus 5 | $0.00005 | $0.00297 |
| Sonnet 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 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 — 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.
- 2d 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 (2,856 stars, last pushed 1mo ago), licensed MIT. It adds 10 tokens to every session and 594 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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