Agent skill

Bug Reproducer

by AnastasiyaW in AnastasiyaW/codex-claude-code-config

Find likely software bugs in a codebase, rank concrete bug candidates, and prove or reject them with focused regression tests before proposing a fix.

MITAuto-check passedTesting & QA

Install Bug Reproducer

skills CLI
$ npx skills add AnastasiyaW/codex-claude-code-config --skill bug-reproducer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AnastasiyaW/codex-claude-code-config bug-reproducer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AnastasiyaW/codex-claude-code-config.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/development/bug-reproducer .claude/skills/bug-reproducer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bug-reproducer
GitHub stars
154
Token cost
~4.1k tokens
SKILL.md length
1,946 words
Files
11 (incl. scripts, references)
Skills in repo
50
Repo updated
First seen
Licence
MIT

At a glance

Find likely software bugs in a codebase, rank concrete bug candidates, and prove or reject them with focused regression tests before proposing a fix.

  • Works in 8 steps: Map the project read-only → Generate concrete candidates → Rank before testing → …
  • Codex needs to hunt for unknown bugs
  • SKILL.md covers Honor existing authority, then…, Choose the workflow, Run hunt-and-prove and Handle an existing bug report, plus 6 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Bug Reproducer is an agent skill from AnastasiyaW/codex-claude-code-config. Find likely software bugs in a codebase, rank concrete bug candidates, and prove or reject them with focused regression tests before proposing a fix. Also turn bug reports, stack traces, screenshots, failing behavior, support tickets, and regressions into minimal reproducible cases with red-to-green evidence. Use when Codex needs to hunt for unknown bugs, audit code for correctness defects, test suspicious edge cases, reproduce a reported failure, isolate root cause, or verify that an approved fix works without…

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts and reference files (for example `ATTRIBUTION.md`, `agents/openai.yaml` and `references/bug-discovery-playbook.md`).

It sits in Testing & QA, covering Debugging, QA and bug reports and Customer support. The repository describes itself as: Claude Code, Codex, and multi-agent configuration system: principles, hooks, skills, and workflow patterns for AI-assisted development. The licence is MIT.

When your agent uses it

  • Codex needs to hunt for unknown bugs
  • Audit code for correctness defects
  • Test suspicious edge cases
  • Reproduce a reported failure

Example prompts

  • “/bug-reproducer”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Map the project read-only
  2. Generate concrete candidates
  3. Rank before testing
  4. Resolve Gate 1 authority
  5. Test approved candidates
  6. Isolate and resolve Gate 2 authority
  7. Apply only the approved fix
  8. Prove red to green

What it can do on your machine

Read from SKILL.md and the folder at commit 3601289. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bug Reproducer loads about 4.1k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 200 tokens; SKILL.md has 1,946 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~200
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from AnastasiyaW/codex-claude-code-config at commit 3601289, republished under its MIT licence (© AnastasiyaW). 1,946 words, ~4,096 tokens.

Download SKILL.mdSave it as .claude/skills/bug-reproducer/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
bug-reproducer
description
Find likely software bugs in a codebase, rank concrete bug candidates, and prove or reject them with focused regression tests before proposing a fix. Also turn bug reports, stack traces, screenshots, failing behavior, support tickets, and regressions into minimal reproducible cases with red-to-green evidence. Use when Codex needs to hunt for unknown bugs, audit code for correctness defects, test suspicious edge cases, reproduce a reported failure, isolate root cause, or verify that an approved fix works without regressions. Honor explicit user authority for the requested fix; use approval gates only for authority that the request did not already grant or when scope materially changes. Do NOT use for ordinary implementation where no bug investigation or reproduction is needed.

Bug Reproducer

Turn a codebase or bug report into evidence. When no bug is supplied, discover concrete candidates from the code and its contracts, then try to prove the strongest candidates with focused tests. Never present a suspicion as a confirmed bug.

Honor existing authority, then apply missing gates

Invocation alone to hunt for unknown bugs is inspection-only. A direct request to fix or implement a reported bug, or to find and fix bugs in a named repository, authorizes the normal reversible reproduction tests and causal production edits inside that stated task. A direct request to reproduce a reported bug or create its regression test likewise authorizes the normal reversible reproduction files and commands, but not a production repair. Record the matching task scope and proceed without stopping for duplicate confirmation, even when the exact responsible file is learned during diagnosis. Do not ask the user to approve the same scope twice.

If the user asked only to inspect, audit, find, or isolate, do not infer permission to create reproduction files or change production code. A direct reproduction or regression-test request authorizes only the normal reversible reproduction work described above; it does not authorize a production repair. Before a gate that has not been satisfied, do not create or edit project files, install dependencies, run formatters or migrations, modify configuration, generate reports, create worktrees, or execute commands likely to mutate project state. Read source, configuration, documentation, existing tests, user-supplied logs, and Git history. Run an existing targeted test only when it is clearly safe and does not require project changes.

Gate 1 — test the candidate

When the current request has not explicitly authorized creating and running a reproducer, present and stop at:

markdown
## Bug candidates

| # | Candidate | Contract evidence | Trigger | Location | Confidence |
|---|---|---|---|---|---|
| 1 | ... | ... | ... | file:line | high/medium |

## Proposed bug test

- Candidate(s) to test:
- Why each could be a real bug:
- Exact files to create or edit:
- Minimal fixture/input:
- Test or harness command:
- Signal that will confirm each bug:
- Main risk or uncertainty:

No project files have been modified. Do you want me to create and run these tests?

List only candidates backed by a reachable code path, a defensible expected behavior, and a specific triggering input or state. Approval covers only the displayed reproduction files and commands. A direct request to reproduce a supplied symptom or write its regression test already satisfies this gate: record that scope and continue without requiring file names that diagnosis has not yet established. Test one strong candidate by default; batch at most three only when their files and commands are all explicit.

Gate 2 — fix a proven bug

When the current request has not already authorized a causal production repair, present and stop at:

markdown
## Proposed fix

- Reproduction status: REPRODUCED
- Proven bug:
- Root cause:
- Exact production files to change:
- Proposed transformation:
- Behavior that must remain identical:
- Regression and broader test plan:
- Main risk:

The bug is proven by a failing test, but no production fix has been applied. Do you want me to apply this exact fix?

Continue when the current request already covers the causal repair or after explicit approval. If the file list, dependencies, public behavior, risk tier, or approach materially changes beyond that authority, request fresh approval. Never bundle unrelated cleanup or another fix into the approved patch.

Choose the workflow

  • hunt-and-prove — Default when no specific bug is supplied. Discover, rank, and test likely correctness bugs.
  • reproduce-only — Stop after a minimal failing case is proven. Use when the user asks only to find, reproduce, isolate, or write regression tests.
  • prove-fix — Use for a reported bug or after a discovered candidate is proven and the user wants it fixed. Apply only gates not already satisfied by the current request, then prove red to green.
  • flaky — Reproduce timing, ordering, seed, isolation, or concurrency-dependent failures across repeated controlled runs. Do not label one intermittent failure as reproduced.

Run hunt-and-prove

1. Map the project read-only
  • Identify runtime, entry points, public interfaces, data boundaries, test framework, existing commands, and high-change or high-risk modules.
  • Derive intended behavior from tests, types, schemas, documentation, callers, UI copy, API contracts, and invariants. Prefer explicit contracts over personal style preferences.
  • Inspect recent changes only when repository history is available; do not assume old code is correct.
  • Read references/bug-discovery-playbook.md before ranking candidates.
2. Generate concrete candidates
  • Trace user-controlled or boundary inputs through real reachable paths.
  • Look for falsifiable defects: off-by-one boundaries, inverted conditions, missing empty/null handling, unsafe state transitions, ordering or deduplication errors, stale cache keys, permission gaps, precision/time-zone mistakes, async races, inconsistent validation, and error paths that violate the surrounding contract.
  • For every candidate, identify the exact path, triggering input/state, expected result, likely actual result, and evidence for the expectation.
  • Reject vague possibilities such as “this could be null” unless the codebase permits null and the path mishandles it.
  • Do not call security posture, performance, formatting, maintainability, or missing features a bug unless they violate a concrete product contract.
3. Rank before testing

Rank candidates using:

  1. Contract strength — Is expected behavior explicit in tests, types, docs, schemas, callers, or consistent nearby behavior?
  2. Reachability — Can a real caller or valid input reach the path?
  3. Reproducibility — Can a small deterministic test distinguish correct from incorrect behavior?
  4. Impact — Does it affect outputs, data, permissions, crashes, or a user-visible workflow?

Show no more than five candidates and label confidence high, medium, or low. Do not propose testing low-confidence candidates while a stronger one exists. If no candidate survives this filter, return NO_BUG_PROVEN and identify the next useful evidence instead of inventing a bug.

4. Resolve Gate 1 authority
  • If the request explicitly asked to reproduce a supplied symptom, create a regression test, or otherwise authorized reproduction work, record the matching task scope and continue. Do not require exact file names before diagnosis establishes them.
  • Otherwise show the ranked shortlist and exact test plan for the strongest one to three candidates.
  • Name every file and command that approval would cover.
  • State that no project files have been modified, then stop and wait.
5. Test approved candidates
  • Create only the approved test or harness.
  • Use deterministic inputs and the smallest fixture that reaches the real production path.
  • Assert the intended contract, not the current implementation.
  • Capture the narrow command with scripts/capture_command.py when appropriate:
bash
python3 scripts/capture_command.py --label reproduced --output reproduced.json -- npm test -- path/to/regression.test.ts
  • Confirm that a failure matches the predicted behavior and cause. Syntax errors, missing dependencies, invalid fixtures, unrelated failures, and assertions built on an unsupported assumption reject or invalidate the candidate.
  • Return REPRODUCED, NOT_REPRODUCED, or INCONCLUSIVE for each tested candidate. Clearly separate rejected speculative tests by default. Delete them only with explicit user authority.
  • Stop after evidence when the user requested discovery or reproduction only. Do not slide into production changes.
6. Isolate and resolve Gate 2 authority
  • Trace a reproduced failure to the smallest responsible condition and distinguish root cause from the line where it surfaces.
  • If the current request already authorized the causal fix, record how the patch remains inside it and continue.
  • Otherwise show Gate 2 with exact production files, transformation, preserved behavior, checks, and risk, then stop and wait for separate approval.
7. Apply only the approved fix
  • Preserve the regression test unless the user explicitly requests a temporary harness.
  • Change only the approved production files and necessary test expectations.
  • Prefer the smallest causal fix over symptom suppression.
  • Do not weaken assertions, catch and ignore errors, add arbitrary retries, disable tests, or broaden tolerances merely to turn the test green.
Show full SKILL.md (832 more words)Show less
8. Prove red to green
  • Run the exact same targeted command used for failing evidence.
  • Run the narrowest additional check that meaningfully covers the changed behavior and its integration boundary. Escalate to a broader relevant suite, typecheck, lint, or build when the change's risk or contract requires it; record the selected scope and any unavailable or disproportionate checks as limitations.
  • Capture and classify the fixed run:
bash
python3 scripts/capture_command.py --label fixed --output fixed.json -- npm test -- path/to/regression.test.ts
python3 scripts/capture_command.py --label relevant-check --output relevant-check.json -- npm test -- path/to/affected-area
python3 scripts/compare_evidence.py reproduced.json fixed.json result.json --reproduction confirmed --relevant-evidence relevant-check.json

When the exact targeted reproducer is genuinely the only proportionate relevant check, do not reuse fixed.json as --relevant-evidence or pretend an additional command ran. Record that boundary explicitly instead:

bash
python3 scripts/compare_evidence.py reproduced.json fixed.json result.json --reproduction confirmed --targeted-scope-sufficient --scope-rationale "Pure function; no additional integration boundary exists."
  • Classify a passing targeted test with a failing captured relevant check as FIX_REGRESSION.
  • Classify a passing targeted test with a reused targeted receipt presented as an additional check as FIX_UNVERIFIED.
  • Classify a passing targeted test without a captured proportionate relevant check as FIX_UNVERIFIED, unless the project genuinely has no additional meaningful check and --targeted-scope-sufficient records the explicit rationale.

Handle an existing bug report

When the user supplies a symptom, error, screenshot, stack trace, or failing behavior:

  1. Restate expected behavior, actual behavior, trigger, environment, frequency, and last-known-good version.
  2. Separate observed facts from assumptions and remove secrets or personal data.
  3. Map the report to likely paths and existing test conventions.
  4. Skip broad discovery; preserve reproduction integrity and red-to-green proof, but do not repeat Gate 1 or Gate 2 when the reported-bug fix request already satisfies them.
  5. Read references/reproduction-playbook.md when selecting the smallest credible layer.

Use evidence labels precisely

  • REPRODUCED — A focused case fails deterministically for the predicted or reported reason.
  • NOT_REPRODUCED — The approved case does not produce the predicted failure.
  • NO_BUG_PROVEN — Read-only discovery or approved candidate tests did not prove a correctness defect.
  • INCONCLUSIVE — Environment, nondeterminism, missing access, or ambiguous signals prevent a conclusion.
  • STILL_FAILING — The same reproducer continues to fail after an attempted fix.
  • FIX_UNVERIFIED — The reproducer passes, but proportionate correctness evidence or an explicit targeted-only scope rationale is missing.
  • FIX_REGRESSION — The reproducer passes or changes, but a captured relevant correctness check fails.
  • FIX_PROVEN — The same reproducer goes red to green and either a captured additional proportionate relevant check passes or targeted-only scope is explicitly justified.

Never claim a bug from code inspection alone. Never claim a fix without red-to-green evidence.

Create the native report

Create a native report only when the user requests one or the repository requires one. Use the existing project evidence or audit location specified by the user or repository; do not create a generic outputs/ directory. Prepare context JSON following references/report-schema.md, including discovery scope and ranked candidates when using hunt-and-prove, then run:

bash
python3 scripts/generate_report.py result.json context.json path/to/existing-evidence-location/bug-reproducer-report.md

Include discovery evidence, tested candidates, minimal reproduction, root cause, approvals and scope, changed files, red/green commands, broader checks, limitations, and residual risks. Link the Markdown report in the final response.

Hand off

Lead with the strongest evidence label, then report:

  1. Scope inspected and candidates considered
  2. Candidate tests and outcomes
  3. What reproduced and why the failure is credible
  4. Root cause, if proven
  5. Approved fix and red-to-green evidence, if requested
  6. Broader checks, limitations, and residual risk
  7. Link to the report when one was requested

If no bug is proven, preserve the project and say so plainly. A clean hunt is evidence about the inspected scope, not proof that the entire codebase has no bugs.

Local operating rules

  • On Windows, prefer an explicit verified Python executable in capture commands when the python launcher is slow or resolves to a store shim. Record the runtime in the evidence JSON.
  • Treat --reproduction confirmed as operator input, not independent proof. compare_evidence.py accepts an additional passing check only through a captured --relevant-evidence receipt that is distinct from the targeted reproducer. If the targeted reproducer is sufficient, declare that scope and rationale explicitly. The agent or a fresh verifier must inspect the captured commands, exit codes, output, and relevant-check scope before accepting the status.
  • Do not use the upstream installer when the destination may already exist: it force-removes the destination. A backup does not authorize that removal. Install only into an absent destination; request explicit replacement or deletion authority before touching an existing one, then verify the resulting file list.

Gotchas

  • A passing focused test before a fix means NOT_REPRODUCED, even if the code looks suspicious.
  • A syntax error, missing dependency, invalid fixture, or unrelated failing suite invalidates the reproduction; it is not a product bug.
  • A clean hunt covers only the inspected scope. It never proves that the whole repository is bug-free.
  • compare_evidence.py classifies recorded receipts and declared scope; it does not prove causal root cause, independently run the relevant check, or decide whether targeted-only scope is actually sufficient.

Troubleshooting

  • capture command hangs on Windows: replace the launcher with the explicit verified Python executable, reduce the timeout, and rerun the same command.
  • FIX_UNVERIFIED: run and capture the proportionate relevant check, or explicitly record why the targeted reproducer is sufficient; do not upgrade the label by hand.
  • INCONCLUSIVE: check that before and after commands are byte-for-byte the same and that the failure signal matches the predicted assertion.

© AnastasiyaW, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in skills/development/bug-reproducer of AnastasiyaW/codex-claude-code-config.

  • SKILL.md
  • ATTRIBUTION.md
  • LICENSE-upstream
  • agents/openai.yaml
  • references/bug-discovery-playbook.md
  • references/report-schema.md
  • references/reproduction-playbook.md
  • scripts/capture_command.py
  • scripts/compare_evidence.py
  • scripts/generate_report.py
  • scripts/test_compare_evidence.py

Open the folder on GitHubat commit 3601289

Compare with similar skills

Bug Reproducer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bug Reproducer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bug Reproducer this skillAnastasiyaW/codex-claude-code-config154—~4.1kAutomated safety check: PassMIT
Issue Trackingstatic-web-server/static-web-server2.4k—~1.5kAutomated safety check: PassApache-2.0
Product Diagnosisamplitude/builder-skills159—~4kAutomated safety check: PassNone
Bug To Patch GeneratorArabelaTso/Skills-4-SE253—~4.4kAutomated safety check: PassApache-2.0
Prp DebugWirasm/prp2.3k—~1.1kAutomated safety check: PassMIT
Prp DebugWirasm/prp2.3k—~1.1kAutomated safety check: PassMIT

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Questions about Bug Reproducer

What does Bug Reproducer do?

Find likely software bugs in a codebase, rank concrete bug candidates, and prove or reject them with focused regression tests before proposing a fix. Bug Reproducer is an agent skill from AnastasiyaW/codex-claude-code-config. Find likely software bugs in a codebase, rank concrete bug candidates, and prove or reject them with focused regression tests before proposing a fix.

When should I use Bug Reproducer?

Bug Reproducer fits situations like: Codex needs to hunt for unknown bugs; audit code for correctness defects; test suspicious edge cases; reproduce a reported failure.

How do I install Bug Reproducer in Claude Code?

Run `npx skills add AnastasiyaW/codex-claude-code-config --skill bug-reproducer -a claude-code`. Or copy the skill folder (skills/development/bug-reproducer in AnastasiyaW/codex-claude-code-config) into .claude/skills/bug-reproducer in your project. Claude Code loads it when a task matches its description.

How do I install Bug Reproducer in Codex?

Run `npx skills add AnastasiyaW/codex-claude-code-config --skill bug-reproducer -a codex`. Or copy the skill folder (skills/development/bug-reproducer in AnastasiyaW/codex-claude-code-config) into .agents/skills/bug-reproducer in your project. Codex loads it when a task matches its description.

Can I use Bug Reproducer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AnastasiyaW/codex-claude-code-config --skill bug-reproducer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bug-reproducer, .gemini/skills/bug-reproducer, .github/skills/bug-reproducer and .opencode/skills/bug-reproducer in your project.

What does Bug Reproducer need to run?

Going by SKILL.md and its folder, Bug Reproducer needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Bug Reproducer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bug Reproducer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Bug Reproducer use?

Bug Reproducer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bug Reproducer use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.2k tokens, read only when the agent opens those files.

What are the alternatives to Bug Reproducer?

Skills that share tags, products or a category with Bug Reproducer: Issue Tracking (static-web-server/static-web-server, 2.4k stars), Product Diagnosis (amplitude/builder-skills, 159 stars), Bug To Patch Generator (ArabelaTso/Skills-4-SE, 253 stars) and Prp Debug (Wirasm/prp, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bug Reproducer?

AnastasiyaW (a GitHub user) maintains it in AnastasiyaW/codex-claude-code-config, which has 154 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on October 11, 2026.

Source: AnastasiyaW/codex-claude-code-config on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.