Borrowing it
Nothing to install: this file belongs to wbern/agent-instructions. 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/wbern/agent-instructions/main/.claude/commands/beepboop.mdgit clone --depth 1 https://github.com/wbern/agent-instructionsWrote 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/wbern/agent-instructions/beepboop)<a href="https://agentmods.dev/commands/wbern/agent-instructions/beepboop"><img src="https://agentmods.dev/badge/commands/wbern/agent-instructions/beepboop/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/wbern/agent-instructions/beepboop"><img src="https://agentmods.dev/badge/commands/wbern/agent-instructions/beepboop.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 | $0.00008 | $0.00535 |
| Opus 5.5 | $0.00003 | $0.00214 |
| Sonnet 5.5 | $0.00002 | $0.00107 |
| Haiku 4.5 | $0.00001 | $0.00053 |
Grade A, and why
beepboop 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 2mo 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Attributed Communication Command
Execute the user's requested task (e.g., posting PR comments, GitHub issue comments, or other communications through various MCPs), but frame the output with clear AI attribution.
General Guidelines
Output Style
- Never explicitly mention TDD in code, comments, commits, PRs, or issues
- Write natural, descriptive code without meta-commentary about the development process
- The code should speak for itself - TDD is the process, not the product
Beads is available for task tracking. Use mcp__beads__* tools to manage issues (the user interacts via bd commands).
Plan File Restriction
NEVER create, read, or update plan.md files. Claude Code's internal planning files are disabled for this project. Use other methods to track implementation progress (e.g., comments, todo lists, or external tools).
Instructions
User arguments:
Beepboop: $ARGUMENTS
End of user arguments
IMPORTANT Communication Format:
-
Opening: Begin with "Beep boop, I am Claude Code 🤖, my user has reviewed and approved the following written by me:"
- Use italics for this line
- Clearly establishes AI authorship
-
Middle: Perform the requested task (post comment, create review, etc.)
- Execute whatever communication task the user requested
- Write the actual content that accomplishes the user's goal
-
Closing: End with "Beep boop, Claude Code 🤖 out!"
- Use italics for this line
- Provides clear closure
Purpose
This command ensures transparency about AI usage while maintaining that the user has reviewed and approved the content. It prevents offloading review responsibility to other users while being open about AI assistance.
Examples
- Posting a GitHub PR review comment
- Adding a comment to a GitHub issue
- Responding to feedback with AI-generated explanations
- Any communication where AI attribution is valuable
Testing Requirements
| Change | Required |
|---|---|
| Content (fragment/source) | Snapshot update |
| Feature flag | Conditional test (enabled + disabled), FLAG_OPTIONS, CLI mock |
| CLI option | cli.test.ts mock |
| Generation logic | Unit test |
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.
- 2mo ago First seen · 67 lines · 8 tokens per session scan A 54e287eaec19
beepboop is a command published in the GitHub repository wbern/agent-instructions (168 stars, last pushed 5mo ago), licensed MIT. It adds 8 tokens to every session and 535 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.