Your words become its directions.
A quick start, a flag description, an example. Small ambiguities can become long detours.
Agent experience testing
It will read your docs. Try your commands. Find its own way through your product.
AJX lets you see that first attempt—and turn the confusing parts into a better experience.
One prompt installs the CLI and the skill. You bring the product.
A clear goal, with a result we can verify: 100 data rows in a valid CSV.
Both commands look relevant. The top-level help does not explain which one writes CSV.
The documented --out flag fails. A help lookup reveals the supported --output flag.
The task succeeds. The detours still reveal three ways to improve the product.
Pick a stop. Follow the evidence.
A familiar responsibility
Your docs, commands, and error messages are an interface. An agent has to understand them well enough to get something done.
AJX records what happens when a fresh worker tries. You get the result and the route it took to get there.
A quick start, a flag description, an example. Small ambiguities can become long detours.
A useful error points forward. A silent command leaves the agent guessing whether to wait or retry.
The agent may recover from a rough experience. The journey shows what success alone can hide.
From a first impression to a useful finding
You set the task and the boundaries. AJX helps you observe the experience without having to reconstruct it afterward.
Choose a real user task and a way to verify success. Set the harness, model, environment, and access.
“Export 100 records as CSV.”Run a fresh worker. Follow its progress in the local console, then inspect the events and artifacts.
A fork. A failed flag. A recovery.Open the product asks. Each connects an observed problem to a proposed change and a success check.
Fix the example. Try the task again.When the product leaves questions unanswered
Start with what your team can improve. Switch between these fictional attempts to see how the asks change with the product version and run conditions.
The documented --out flag failed. Command help showed --output, which worked.
Use the supported --output flag in the quick start, with a copyable CSV example.
The first attempt failed and required a help lookup before retrying.
Verify the fix: Run the documented command in a fresh workspace; it must produce a 100-row CSV.
E-003 → E-004 → E-005 Follow the journey ↗Only start and completion messages appeared during the 47-second export.
Emit bounded progress on stderr and a final structured completion result.
The agent could not distinguish productive work from a stalled command.
Verify the fix: Run a delayed export; progress must remain separate from CSV stdout.
E-005 → E-006 Follow the journey ↗Both commands were described as writing records; the required CSV path was unclear.
State the output formats and intended use of both commands in top-level help.
The agent had to choose without a selection signal.
Verify the fix: A user reading only top-level help can identify the CSV command.
E-001 → E-002 Follow the journey ↗Only start and completion messages appeared during the 20-second export.
Emit bounded progress on stderr and a final structured completion result.
The agent could not observe intermediate progress.
Verify the fix: Run a delayed export; progress must remain separate from CSV stdout.
E-002 → E-003 Follow the journey ↗The command requested browser approval after the agent had started the task.
Provide a non-interactive authorization preflight and document the required human approval.
The unattended task stopped without an output file.
Verify the fix: In headless mode, preflight reports the missing approval before an export begins.
E-002 → E-003 → E-004 Follow the journey ↗One product. More than one perspective.
After the first attempt, try the same task across harnesses, models, and environments. Keep the evidence together so your team can compare what changes.
Your product’s turn
Open your project in a skill-capable agent and paste this prompt. Your agent handles AJX installation, checks the setup, and helps you choose a first task.
A CLI, SDK, API, or tool you want agents to use.
macOS or Linux, with Codex, Claude Code, or Kiro CLI installed and signed in.
Check the task, success criteria, workspace access, and expected model costs.
Install the latest stable AJX CLI and skill by following https://raw.githubusercontent.com/royosherove/ajx/main/docs/AGENT-QUICKSTART.md Then load the installed AJX skill. I want to use AJX to test jq. Starting point: https://jqlang.org/manual/ Task: Filter a sample JSON inventory to in-stock items and export their names and prices as CSV. Prepare any tools, generated sample data, and local test server needed in a temporary workspace. Use the AJX skill to run one trial with a fresh agent, verify the result, and show me the report and journey.
bash -o pipefail -c 'curl -fsSL https://github.com/royosherove/ajx/releases/latest/download/install.sh | bash' && export PATH="$HOME/.local/bin:$PATH"
macOS or Linux. The installer includes the skill. Your agent can read it directly; start a fresh session if the host needs one to discover /ajx. No GitHub login required.
Installs the native CLI + Agent Skill together.
No Go toolchain or GitHub login
required.