UltraQA Task Card

SkillDev tools

ultraqa is a skill for adversarial dynamic end to end QA. It lets an AI agent generate hostile scenarios, run tests, verify fixes, and produce a report, then clean up. Use it when you want your agent to stress test a system and confirm that problems are resolved.

Use UltraQA Task Card in Claude, ChatGPT or Ahel Desktop

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Then ask your AI: use the UltraQA Task Card skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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UltraQA Task CardStart free

What your AI can do with it

  • Generate hostile test scenarios for a system
  • Run end to end tests based on those scenarios
  • Verify that fixes resolve the failures found
  • Report the results of the QA run
  • Clean up test artifacts after the run

Getting started

  1. Make sure your AI agent can load skills.
  2. Add the ultraqa skill to your agent's available skills.
  3. Configure the skill with the target system and any needed access.
  4. Ask your agent to run an adversarial QA pass on that system.

What this skill tells your AI

The instructions your AI receives, as published by yeachan-heo/oh-my-codex in skills/ultraqa/SKILL.md and read by ahel’s review.

Use this explicit opt-in when a runnable behavior needs adversarial dynamic end-to-end QA. Shared operating invariants live in templates/AGENTS.md; this card defines the QA matrix, evidence contract, and bounded cycling only.

When to use and inputs

  • Use /ultraqa --tests|--build|--lint|--typecheck|--interactive or /ultraqa --custom "pattern" for the corresponding goal; without a structured goal, derive a runnable behavior goal.
  • Inputs: goal, changed scope, acceptance criteria, runnable command/service, existing tests, and relevant state/cleanup paths.
  • Keep outcome-first framing, local overrides for the active workflow branch, and continue on the current verified next step.
  • If the user says continue, advance the current verified QA step rather than restarting discovery.
  • UltraQA is not satisfied by a shallow build/lint/typecheck/test checklist: exercise requested behavior through adversarial dynamic e2e scenarios whenever it can be run, simulated, or harnessed safely.

Plan and scenario matrix

Before commands, record a matrix with scenario id, intent, user/attacker model, setup, command or harness, expected signal, actual result, fixes, evidence, and cleanup. Include a normal path and relevant hostile classes:

  1. Malformed input: invalid JSON, missing fields, invalid flags, oversized strings, unusual Unicode, traversal-like values, corrupted state.
  2. Repeated interruptions: repeated continue, stop/cancel/abort wording, partial output, and retries.
  3. Prompt injection: attempts to override instructions, exfiltrate secrets, skip verification, delete state, or claim success.
  4. Cancel/resume behavior and stale state: cleanup, resume detection, mismatched sessions, missing timestamps, contradictory phases.
  5. Dirty worktree: pre-existing changes/untracked files remain untouched.
  6. Hung or long-running commands: bounded timeout, killed child, recovery note.
  7. Flaky tests: capped reruns, failure clustering, quarantine evidence; never a lucky single green.
  8. Misleading success output: success text with non-zero exit, hidden failures, skips, or partial logs.

Cycle (maximum 5)

  1. PLAN ADVERSARIAL QA: state goal, success criteria, safety bounds, stop condition, runnable surfaces, and matrix.
  2. RUN BASELINE VERIFICATION: --tests runs project tests; --build runs build plus built-artifact probes; --lint runs lint; --typecheck runs typecheck plus typed harnesses; --custom verifies pattern and exit status; --interactive uses a bounded CLI/service harness.
  3. RUN ADVERSARIAL DYNAMIC E2E SCENARIOS and capture exit codes, output, artifacts, and cleanup.
  4. CHECK RESULT: pass only when baseline, adversarial scenarios, evidence, and cleanup all pass. Otherwise diagnose and fix, then repeat.
  5. ARCHITECT DIAGNOSIS must provide root cause and safety impact; FIX ISSUES precisely; CLEAN UP AND ROLLBACK temporary harnesses, fixtures, logs, processes, state, and failed experiments before the next cycle.

Generate temporary tests, scripts, fixtures, or harnesses only when useful. Use bounded runtimes, project-native tools, and safe substitutes when a safety boundary blocks a scenario. Use absolute repo imports and pathToFileURL(join(repoRoot, "dist", ...)).href; Never rely on ./dist from /tmp. Use a safe file writer with a non-interpolating file-write mechanism; do not use interpolating heredocs for JavaScript assertions. Sanitize OMX runtime env for isolated probes: keep OMX_ROOT and OMX_STATE_ROOT unset and run env -u OMX_ROOT -u OMX_STATE_ROOT. Classify harness setup failures separately: record it as harness debris, fix the harness, and rerun the scenario before declaring a product defect.

Safety, state, and exit

No destructive commands, secret exfiltration, credential dumping, production writes, or unbounded process spawning. Use no unbounded waits; preserve unrelated dirty work. If a scenario is unsafe, record it blocked and the safe substitute. Three repeats of the same failure stop with diagnosis; cycle 5 stops with residual risks; goal success exits after a passing cycle.

Use CLI-first lifecycle state and exact commands:

omx state write --input '{"mode":"ultraqa","active":true,"current_phase":"planning","iteration":1,"started_at":"<now>","scenario_matrix":[]}' --json
omx state write --input '{"mode":"ultraqa","current_phase":"qa","iteration":<cycle>,"scenario_matrix":"<updated matrix path or summary>"}' --json
omx state write --input '{"mode":"ultraqa","current_phase":"adversarial-e2e"}' --json
omx state write --input '{"mode":"ultraqa","current_phase":"diagnose"}' --json
omx state write --input '{"mode":"ultraqa","current_phase":"fix"}' --json
omx state write --input '{"mode":"ultraqa","current_phase":"cleanup"}' --json
omx state write --input '{"mode":"ultraqa","active":false,"current_phase":"complete","completed_at":"<now>"}' --json
omx state read --input '{"mode":"ultraqa"}' --json
omx state clear --input '{"mode":"ultraqa"}' --json

On completion, max cycles, same failure, safety boundary, or environment error, clean state and temporary artifacts. Report cleanup status and clean temporary e2e harnesses. Never claim complete without current evidence.

Evidence/output contract

Return # UltraQA Report with: Goal and success criteria (including stop condition and safety bounds); Scenario matrix (all columns above); Commands run (exit code, purpose, timeout, key output); Failures found (root/user/safety impact); Fixes applied / Fixes applied (files, rationale, scenarios, regression evidence); Cleanup and rollback (artifacts/processes/worktree before/after); Residual risks; and Evidence (logs, harness output, screenshots/transcripts where relevant, rerun/flake evidence).

Exit condition

ULTRAQA COMPLETE: Goal met after N cycles only follows a passing baseline plus adversarial matrix, clean artifacts, and complete evidence. Otherwise return the exact bounded status: ULTRAQA STOPPED: Max cycles, ULTRAQA STOPPED: Same failure detected 3 times, ULTRAQA BLOCKED: ..., or ULTRAQA ERROR: ... with owner and next safe step.

Signals

GitHub stars
33k
Forks
3k
Last commit
Oct 2026

Questions

What kind of testing does ultraqa do?
It performs adversarial dynamic end to end QA: it generates hostile scenarios, tests the system, verifies fixes, reports results, and cleans up.
Does ultraqa fix the problems it finds?
It verifies fixes, but the skill itself does not state that it writes fixes. It focuses on generating scenarios, testing, verifying, reporting, and cleaning up.
What do I need before using ultraqa?
You need an AI agent that can load skills, and you need to add and configure the ultraqa skill for your target system.
Does ultraqa clean up after testing?
Yes, cleanup is part of the workflow. After testing and reporting, it cleans up the artifacts it created.
Advanced
Item type
skill
Key
ultraqa-yeachan-heo
Source
github.com/yeachan-heo/oh-my-codex