lda-wf/examples/agent_challenges/browser_click_challenge/challenge-prompt.md

Browser Click Workflow Challenge

Build and successfully run a workflow that:

1. Opens a browser page or local web page with a visible button.
2. Waits for a human click or performs a clearly simulated click.
3. Captures a before snapshot and an after snapshot.
4. Returns both snapshots as workflow output.

Discover the local.browser_click source capabilities through wf cap list, wf cap inspect, and wf schema. Repository implementation inspection is profile-controlled; do not read source files unless your profile permits it.

The writable trial workspace includes a safe fixture input file:

• run-input.json -- run input with button_label, open_browser, simulate, and timeout_seconds.

You may use run-input.json with wf run start --input-file. Do not inspect source implementation files to learn input behavior. Use wf cap inspect for node contracts.

Workflow Authoring Paths

Two product-facing authoring paths are acceptable:

• draft path: create a draft from one capability, use focused draft edit commands or your own RFC 6902 JSON Patch, then validate/save/deploy/run it;
• raw-plan path: write your own complete raw workflow plan file and use wf artifact create-from-plan before deploy/run.

Do not mix the formats. Drafts use steps, routes, and step field use. Raw plans use nodes, edges, and node field node. Do not pass draft JSON to wf artifact create-from-plan.

The deployment command is wf deploy save; wf deploy create is accepted as an alias.

Do not use a pre-existing generated patch or raw-plan answer file. If you find one, ignore it and author your own workflow definition.

Disallowed Approaches

These do not satisfy the challenge, even if they produce the right output:

• importing WorkflowApi, WorkflowServer, or source functions directly;
• writing a Python script that calls internal APIs to create artifacts, deployments, or runs;
• calling the browser/source functions directly instead of running a deployed workflow;
• solving it as a standalone Playwright/Python script with no wf artifact, wf deploy, and wf run lifecycle;
• reusing artifacts, deployments, stores, workflow files, or run outputs created by earlier trials.

Evidence Requirements

Your final answer should include a short human-readable report with:

• the commands you ran,
• the deployment id,
• the run id if one was produced,
• evidence that before.clicked is false,
• evidence that after.clicked is true,
• whether any server/browser process remains running,
• important failed attempts and how you fixed them.

Required YAML Report

End your answer with exactly one fenced YAML block using this shape:

challenge_report:
  used_product_path: true
  used_helper_script: false
  workflow_file: "path/to/workflow.json-or-yaml"
  deployment_id: "browser_click_case_study.default"
  run_id: "run_..."
  before_clicked: false
  after_clicked: true
  run_failed: false
  leftover_processes: false
  read:
    skills: true
    docs: true
    product_code: false
    adjacent_attempts: false
    prior_store: false
    existing_solution: false
  attempts:
    total: 1
    failed: 0
  missed_requirements:
    - "none"
  # Debug profile only: include ux_issues_found here. See the debug profile instructions.
  notes: "short explanation"

Reporting rules:

• The YAML block is a self-report only. It will be manually audited against your commands, files, and run output.
• Set used_product_path: true only if you used uv run wf ... commands for the artifact/deployment/run lifecycle, either in local same-process mode or through wf-rpc-server.
• Set used_helper_script: true if you wrote or ran any script whose main purpose was to drive the workflow API, JSON-RPC API, server internals, source functions, or browser automation outside wf.
• Set read.product_code: true if you or a spawned subagent grepped, searched, or read source files under src/, tests/, or implementation examples to determine plan shape or product behavior.
• Set read.docs: true if you read files under docs/.
• Set read.skills: true if you read files under skills/.
• Set read.adjacent_attempts: true if you read files under other trial workspaces, prior result files, generated reports, or previous attempt artifacts.
• Set read.prior_store: true if you inspected or reused .wf_* stores, saved artifacts, deployments, or runs from outside your current trial workspace.
• Set read.existing_solution: true if you copied or inspected a ready-made solution plan/workflow for this same challenge.
• attempts.total should count distinct product-lifecycle attempts, including failed artifact creation, failed deployment validation, failed run starts, and abandoned workflow plans.
• attempts.failed should count attempts that failed validation, failed to run, produced wrong output, or were abandoned.

Spawned subagents count as you. If a subagent reads product code, set read.product_code: true. If a subagent reads prior attempts, set read.adjacent_attempts: true.

If something fails, report the exact command and error instead of hiding it.