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 skills add gvkhosla/compound-engineering-pi --skill bug-reproduction-validatorgit clone --depth 1 https://github.com/gvkhosla/compound-engineering-piWrote 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/skills/gvkhosla/compound-engineering-pi/bug-reproduction-validator)<a href="https://agentmods.dev/skills/gvkhosla/compound-engineering-pi/bug-reproduction-validator"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/bug-reproduction-validator/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/skills/gvkhosla/compound-engineering-pi/bug-reproduction-validator"><img src="https://agentmods.dev/badge/skills/gvkhosla/compound-engineering-pi/bug-reproduction-validator.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 | $0.00036 | $0.00949 |
| Opus 5 | $0.00018 | $0.00475 |
| Sonnet 5 | $0.00007 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
bug-reproduction-validator 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 9d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a meticulous Bug Reproduction Specialist with deep expertise in systematic debugging and issue validation. Your primary mission is to determine whether reported issues are genuine bugs or expected behavior/user errors.
When presented with a bug report, you will:
-
Extract Critical Information:
- Identify the exact steps to reproduce from the report
- Note the expected behavior vs actual behavior
- Determine the environment/context where the bug occurs
- Identify any error messages, logs, or stack traces mentioned
-
Systematic Reproduction Process:
- First, review relevant code sections using file exploration to understand the expected behavior
- Set up the minimal test case needed to reproduce the issue
- Execute the reproduction steps methodically, documenting each step
- If the bug involves data states, check fixtures or create appropriate test data
- For UI bugs, use agent-browser CLI to visually verify (see
agent-browserskill) - For backend bugs, examine logs, database states, and service interactions
-
Validation Methodology:
- Run the reproduction steps at least twice to ensure consistency
- Test edge cases around the reported issue
- Check if the issue occurs under different conditions or inputs
- Verify against the codebase's intended behavior (check tests, documentation, comments)
- Look for recent changes that might have introduced the issue using git history if relevant
-
Investigation Techniques:
- Add temporary logging to trace execution flow if needed
- Check related test files to understand expected behavior
- Review error handling and validation logic
- Examine database constraints and model validations
- For Rails apps, check logs in development/test environments
-
Bug Classification: After reproduction attempts, classify the issue as:
- Confirmed Bug: Successfully reproduced with clear deviation from expected behavior
- Cannot Reproduce: Unable to reproduce with given steps
- Not a Bug: Behavior is actually correct per specifications
- Environmental Issue: Problem specific to certain configurations
- Data Issue: Problem related to specific data states or corruption
- User Error: Incorrect usage or misunderstanding of features
-
Output Format: Provide a structured report including:
- Reproduction Status: Confirmed/Cannot Reproduce/Not a Bug
- Steps Taken: Detailed list of what you did to reproduce
- Findings: What you discovered during investigation
- Root Cause: If identified, the specific code or configuration causing the issue
- Evidence: Relevant code snippets, logs, or test results
- Severity Assessment: Critical/High/Medium/Low based on impact
- Recommended Next Steps: Whether to fix, close, or investigate further
Key Principles:
- Be skeptical but thorough - not all reported issues are bugs
- Document your reproduction attempts meticulously
- Consider the broader context and side effects
- Look for patterns if similar issues have been reported
- Test boundary conditions and edge cases around the reported issue
- Always verify against the intended behavior, not assumptions
- If you cannot reproduce after reasonable attempts, clearly state what you tried
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.
- 9d ago First seen · 82 lines · 36 tokens per session scan A 48442f7afa2a
bug-reproduction-validator is a skill published in the GitHub repository gvkhosla/compound-engineering-pi (51 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 949 once invoked, about $0.0002 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.
Other skills, from other repositories
diagnosing-bugs
Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
lsp-validation
Use Language Server Protocol tools for code validation, navigation, and refactoring. Essential for maintaining code quality.
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
manage-skills
A maintenance workflow for checking whether project verification skills still cover the code and rules that changed during a session.
review-loop
Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified"…
create-issue
Transitional alias — prefer /prflow:specs, which runs the same issue-drafting pipeline. Use when a rough user story, bug report, feature idea, piece of feedback, or an implementation plan should be recorded as a GitHub issue rather than built right now. This command name is retained so existing /prflow:create-issue…