bugbash

name	bugbash
description	Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.

Bugbash (General Software)

Systematically explore a software project, find issues, and produce a report with full reproduction evidence for every finding. This skill applies to CLIs, APIs, Backends, Libraries, and other non-web interfaces.

Setup

Identify the Target (e.g., a CLI binary, an API base URL, a Python package).

Parameter	Default	Example override
Target	(required)	./my-cli, http://localhost:8080, import mylib
Output directory	/tmp/dogfood-output/	Output directory: ./qa-reports
Scope	Full project	Focus on the auth middleware

Process

Lightweight exploration flow (no rigid 5-phase orchestration, no quotas, no 5-item capture lists):

1. Initialize

mkdir -p {OUTPUT_DIR}/logs {OUTPUT_DIR}/evidence

Create a report.md in the output directory and fill in the header fields. Include:

• Target
• Date
• Environment Details (OS, language version)
• Summary Counts (Critical, High, Medium, Low)

If the software needs to be built or started (e.g., npm run build, docker-compose up -d, cargo build), do that now. Keep track of the startup logs and run servers in the background if necessary (e.g., using & and redirecting output for commands that do not support detached mode).

Orient by mapping the surface area of the software before testing (for CLIs: {TARGET} --help + subcommands + envs; for APIs: schemas/routes; for libs: exports). Save to {OUTPUT_DIR}/surface-area.txt.

Explore the surface systematically: happy paths, invalid inputs, missing context, boundary conditions. At each step capture stdout, stderr, exit codes, HTTP status.

Document issues as you find them (do not batch at end). Every issue needs full repro evidence for a reader to copy-paste and reproduce: description, severity (Critical/High/Medium/Low), exact repro steps/commands, expected vs actual behavior, and evidence (stdout/stderr, traces, logs, exit codes). Save verbose evidence files to {OUTPUT_DIR}/evidence/issue-{NNN}.txt and reference in report; embed short ones in markdown.

Wrap up: re-read and sync the report's summary severity counts to the actual documented issues; stop any background processes started in init; tell the user the report is ready with totals, breakdown, and top critical items.

Guidance

• Repro is everything. Every issue needs proof. Provide the exact curl command, CLI invocation, or script used to trigger the bug. A reader should be able to copy-paste the commands to see the exact same failure.
• Verify reproducibility. Before documenting an issue, verify it is reproducible with at least one retry. If it's flaky, note that and try to identify the conditions.
• Capture the environment state. Often bugs depend on the environment (files present, variables set). Note pwd, env vars, or local files if they are part of the repro.
• Write findings incrementally. Append each issue to the report as you discover it. If the session is interrupted, findings are preserved. Never batch all issues for the end.
• Don't just look for crashes. Usability matters. Confusing error messages, undocumented required flags, and sluggish performance are all valid issues.
• Test like a user, not a robot. Try common workflows end-to-end. Combine flags or API calls in ways a real user would, passing the output of one command as input to the next.
• Check the exit codes and status codes. A CLI returning 0 on failure, or an API returning 200 OK for an error payload, is a bug. echo $? or curl -w "%{http_code}" are your friends.