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.
Borrowing it
Nothing to install: this file belongs to VRSEN/OpenSwarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VRSEN/OpenSwarm/main/.cursor/commands/write-instructions.mdgit 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/commands/vrsen/openswarm/write-instructions)<a href="https://agentmods.dev/commands/vrsen/openswarm/write-instructions"><img src="https://agentmods.dev/badge/commands/vrsen/openswarm/write-instructions/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/commands/vrsen/openswarm/write-instructions"><img src="https://agentmods.dev/badge/commands/vrsen/openswarm/write-instructions.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 | $0.00000 | $0.00919 |
| Opus 5.5 | $0.00000 | $0.00368 |
| Sonnet 5.5 | $0.00000 | $0.00184 |
| Haiku 4.5 | $0.00000 | $0.00092 |
Grade A, and why
write-instructions 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Instructions Writer - Implementation Guide
For the next turn, take the role of an expert prompt engineer. Your task is to achieve the desired agent behaviour by writing or refining agent instructions.
Core Principles
- Start Simple: Use concise, verb-driven instructions
- Be Specific: Explicitly state desired outputs and formats
- Provide Examples: Include concrete examples of expected behavior
- Use Positive Instructions: "Do this" rather than "Don't do that"
- Integrate Tools: Show exactly when and how to use each tool
- Test Your Own Writing: Think about how you would react to the instructions you're writing
- Minimal Changes: When refining, make the smallest change needed to fix the issue
Instructions Template
# Role
**[Role, e.g., "Data analyst for financial reports"]**
# Goals
- [High level goal 1 - e.g., "increase sales by 10%"]
- [High level goal 2 - e.g., "improve customer satisfaction"]
# Context
- Part of: [agency]
- Works with: [other agents]
- Used for: [purpose]
# Instructions
## [Task Name]
**[Provide a step-by-step instructions process on how this task should be performed. Use a numbered list. Include specific tools in steps.]**
[...repeat for each task]
# Examples (Optional - if provided by the user)
**1. [Scenario]**
Input: "[sample]"
Process: [steps, tools, validation]
Output: "[expected]"
**2. [Edge Case]**
Input: "[error or odd case]"
Process: [detection, recovery, notify]
Output: "[error response]"
# Output Format
- [Bullet points describing how the agent should respond]
# Additional Notes
- [Any additional notes that don't fit into any of the other sections, or reiterate on the most critical details]
Process
Step 1: Explore & Understand
- Check folder structure:
agency.py, agent folders,shared_instructions.md, PRD - Identify agent's role, responsibilities, available tools, MCP servers
- Understand communication flows and relationships with other agents
- For refinement: Read current
instructions.mdFIRST
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 · 128 lines · 0 tokens per session scan A 51717c000e9b
write-instructions is a command published in the GitHub repository VRSEN/OpenSwarm (2,873 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 919 tokens. 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 commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
prompt-show
Display full details of a saved prompt by ID.
ai
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.