bug-reproduction-validator

bug-reproduction-validator is a skill for Claude Code, Codex from marchatton/agent-skills. It costs 57 tokens per session (819 once invoked), scanned A, original, MIT.

A bug-checking process that tests whether a reported problem can be reproduced and differs from the expected behavior. It examines the report, creates a minimal test case, and checks the relevant system state.

In plain words
What is it for?
Use it to validate bug reports, confirm reproduction steps, compare expected and actual results, and document evidence for a fix.
Why use it?
It prevents developers from trying to fix issues that are expected behavior, user errors, or cannot be reproduced. It also creates clearer evidence about the actual problem.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: model in frontmatter; mentions subagents; installed under .agents/ (shared by several agents).

Good fit Use it to validate bug reports, confirm reproduction steps, compare expected and actual results, and document evidence for a fix.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/marchatton/agent-skills/bug-reproduction-validator
Install

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.

Any agent
npx skills add marchatton/agent-skills --skill bug-reproduction-validator
Clone the repo
git clone --depth 1 https://github.com/marchatton/agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bug-reproduction-validator

README.md
[![agentmods](https://agentmods.dev/badge/skills/marchatton/agent-skills/bug-reproduction-validator/github.svg)](https://agentmods.dev/skills/marchatton/agent-skills/bug-reproduction-validator)
Your own site
<a href="https://agentmods.dev/skills/marchatton/agent-skills/bug-reproduction-validator"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/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.

agentmods 80×15 button for bug-reproduction-validator

Your own site · 80×15
<a href="https://agentmods.dev/skills/marchatton/agent-skills/bug-reproduction-validator"><img src="https://agentmods.dev/badge/skills/marchatton/agent-skills/bug-reproduction-validator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 819 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00819
Opus 5 $0.00028 $0.00409
Sonnet 5 $0.00011 $0.00164
Haiku 4.5 $0.00006 $0.00082

Measured 8d ago against content hash e51875f0b72f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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 8d 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.

.agents/skills/03-plan/bug-reproduction-validator/SKILL.md · 70 lines

What it actually says

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 I report a bug, don't start by trying to fix it. Instead, start by writing a test that reproduces the bug. Then, have subagents try to fix the bug and prove it with a passing test.

When presented with a bug report, you will:

  1. 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
  2. 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 test-browser to visually verify
    • For backend bugs, examine logs, database states, and service interactions
  3. 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
  4. 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
  5. 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
  6. 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

When you cannot access certain resources or need additional information, explicitly state what would help validate the bug further. Your goal is to provide definitive validation of whether the reported issue is a genuine bug requiring a fix.

Changes

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.

  1. 8d ago First seen · 70 lines · 57 tokens per session scan A e51875f0b72f

Subscribe to this mod's changes

bug-reproduction-validator is a skill published in the GitHub repository marchatton/agent-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 57 tokens to every session and 819 once invoked, about $0.0003 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 2026-08-31.

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