SuperAPI AI Directory

mobile-testing

Testing & quality

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Updated 10-16 01:34

About this skill

Mobile Testing (Chinese Version)

English version: See the corresponding English skill.

When to Use

  • Need to handle mobile testing-related tasks in a real project.
  • Need a result that can be directly used for execution, review, or follow-up.

Execution Flow

  1. Read and follow the main prompt in "Load on Demand" (coverage checklist, output structure, quality requirements).
  2. Only supplement project context that truly affects the result: scope, environment, constraints, risks, dependencies, expected deliverables.
  3. When information is incomplete, first provide a usable initial version and explicitly mark assumptions and information gaps.
  4. Default to Markdown; switch only when the user specifies another format.

Domain Execution Key Points

  1. Use production distribution, changes, and incident evidence to select a risk matrix of OS × device × install/upgrade path × network × foreground/background, note samples and uncovered combinations, and do not fabricate a support list.
  2. For deeplinks, preserve the original URI, decoding rules, routing/parameters, and authorization state; compare cold start, warm start, and restoration, and parameters must not be lost or double-decoded after restoration.
  3. For interrupted payments, record the order/operation_id and submission point; after restoration, the server-side read-back accounting and UI state must be consistent; if the response is lost, check the status first and do not pay twice.
  4. Verify data compatibility, permission changes, state after process termination, and resources for upgrades; unexecuted devices can only be candidates, and support scope conclusions must be separated from real device evidence.

Core Constraints

  • Prioritize by risk/business impact; do not spread effort evenly.
  • Write "confirmed facts" and "current assumptions" separately.
  • Do not fabricate interfaces, fields, environments, or root cause details that the user did not provide.
  • The result must be actionable: scenarios specific, prioritized, and able to guide the next step.

Load on Demand

  • When calibrating domain results, correct comparisons, or evidence gaps, read references/domain-decision-example.md 's desensitized inputs, item-by-item expectations, and recalculation basis.

  • Before producing output, you must read and follow prompts/mobile-testing.md(minimum coverage checklist, output structure, quality requirements).

  • When Excel/CSV/JSON/Word or other formats are needed: read output-formats.md, and output according to the user's format requirements.

  • When a ready-made template needs to be applied: read the matching template in output-templates/ , and do not invent a conflicting structure.

  • When the user wants examples or to benchmark against existing assets: read the relevant samples in examples/ .

  • When in-depth materials such as framework specifications, troubleshooting, or report schemas are needed: read only the files in references/ that are relevant to the current issue; do not read the entire directory.

  • When format conversion or auxiliary validation is needed: prefer using the existing scripts in scripts/ rather than rewriting them.

  • When evaluating/regressing this skill: use evals/, and validate and run with skill-up.

  • When the user only wants the shortest onboarding path: read quick-start.md。

Pre-Delivery Self-Check

  • Has followed the main prompt's output structure
  • Minimum coverage focus: scope, device and system coverage, install/upgrade checks, core flows, permission handling, network and interruption behavior, push or deeplink (if relevant), performance or power consumption risks... (details are subject to the main prompt)
  • Has covered the minimum checklist, or indicated why it was omitted
  • High-risk items have clear priorities
  • Did not fabricate details not provided by the user
  • Assumptions and information gaps are marked

Common pitfalls

  • When neither the scope nor the context is clear, do not pretend it is already fully usable.
  • Do not write all items as equally important.
  • Do not skip assumptions and information gaps.
  • Do not output long passages of vague theory unrelated to the current toolchain.