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/aegntic/compound-engineeringWrote 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/aegntic/compound-engineering/reproduce-bug)<a href="https://agentmods.dev/commands/aegntic/compound-engineering/reproduce-bug"><img src="https://agentmods.dev/badge/commands/aegntic/compound-engineering/reproduce-bug/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/aegntic/compound-engineering/reproduce-bug"><img src="https://agentmods.dev/badge/commands/aegntic/compound-engineering/reproduce-bug.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 | $0.00020 | $0.00816 |
| Opus 5.5 | $0.00008 | $0.00326 |
| Sonnet 5.5 | $0.00004 | $0.00163 |
| Haiku 4.5 | $0.00002 | $0.00082 |
Grade A, and why
reproduce-bug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reproduce Bug Command
Look at github issue #$ARGUMENTS and read the issue description and comments.
Phase 1: Log Investigation
Run the following agents in parallel to investigate the bug:
Before dispatching repo-research-analyst or learnings-researcher, use the platform's file-search tool against the bundled agent directory to look for <agent-name>.md, then use the file-read tool to load the full template. Only if the bundled template cannot be loaded should you fall back to ov_load_global_agent "<agent-name>". Before dispatching, quote the first non-empty line of the loaded template and record the source used. If you cannot quote the template because it was not found or could not be read, stop execution, raise the missing-template issue, and do not dispatch. Never dispatch a named agent by name alone.
- Task repo-research-analyst(issue_description) - Search codebase for relevant code paths
- Task learnings-researcher(issue_description) - Check if similar bugs have been solved before
Think about the places it could go wrong looking at the codebase. Look for logging output we can look for.
Run the agents again to find any logs that could help us reproduce the bug.
Keep running these agents until you have a good idea of what is going on.
Phase 2: Visual Reproduction with agent-browser
If the bug is UI-related or involves user flows, use agent-browser to visually reproduce it:
Step 1: Verify Server is Running
agent-browser open http://localhost:3000
agent-browser snapshot -i
If server not running, inform user to start the development server for the project.
Step 2: Navigate to Affected Area
Based on the issue description, navigate to the relevant page:
agent-browser open "http://localhost:3000/[affected_route]"
agent-browser snapshot -i
Step 3: Capture Screenshots
Take screenshots at each step of reproducing the bug:
agent-browser screenshot "bug-[issue]-step-1.png"
Step 4: Follow User Flow
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 · 102 lines · 20 tokens per session scan A 2252381dfa42
reproduce-bug is a command published in the GitHub repository aegntic/compound-engineering (2 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 816 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
logic-fix-all
Autonomous audit-and-fix — after consent, scan the target, fix every logic issue found (all severities), verify each fix, and report anything unresolved.
logic-explain
Trace execution step by step — use when code behavior is surprising or confusing.
logic-health
Sweep a whole codebase or directory for logic correctness — use before a release or to identify risk hotspots.
logic-locate
Locate the root cause of a confirmed failure — use when you have a stack trace, failing test, or wrong output.
logic-review
Review code for logic bugs — use when you suspect something is wrong but have no failing test yet.
verify
Verify code, architecture, or bug fixes using Chain-of-Verification.