SuperAPI AI Directory

manual-testing

Testing & quality

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Updated 10-16 09:33

About this skill

Manual/Exploratory Testing (Chinese version)

English version: See the corresponding English skill.

When to use

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

Execution flow

  1. Read and follow the main prompt in "Load as needed" (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 points

  1. Establish a charter for each time box: task/risk, environment/build, data, exploration scope, deadline, and evidence; account separately for executing established paths and exploring new issues, and do not expand authorization on your own.
  2. Step record: preconditions → action → expected → actual → evidence ID → status; not started is NOT_RUN, interrupted is INCOMPLETE, environment-blocked is BLOCKED, and do not treat the checklist as a completion record.
  3. First prepare recoverable data and checkpoints; at the end, restore accounts, orders, and configurations according to project rules and verify leftovers; unauthorized deletion cannot be used as a cleanup shortcut.
  4. If time is insufficient, first complete the critical risk loop and save unfinished items, owners, and recovery entry points; the report covers only real observations, and unknown expectations remain to be confirmed.

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 as needed

  • 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/manual-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, and do not read through the entire directory.

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

  • When this skill needs to be evaluated/regressed: use evals/, and validate and run it 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, critical paths, environment and account preparation, positive checks, exception checks, boundary checks, exploratory priorities, evidence to collect... (for details, refer to the main prompt)
  • Minimum checklist covered, or state why it is omitted
  • High-risk items have clear priorities
  • No details fabricated that the user did not provide
  • Assumptions and information gaps are identified

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.