performance-result-analysis

Performance Results Analysis (Chinese Version)

When to Use

You need to interpret load test results and determine goal achievement, inflection points, error patterns, and capacity risks.
You need to conduct a risk review of an existing plan, results, or evidence and produce actionable improvement items.
The input is incomplete, but you still need to first deliver a usable initial version with assumptions and information gaps.

The default output is Markdown, suitable for review, execution, and continuous supplementation.
When the user requests a table, CSV, JSON, or ticket format, retain the same risk, evidence, priority, and boundary fields.
If the output will enter an automated process, first confirm the field schema, enumeration values, and required items.

First read and follow prompts/performance-result-analysis.md 's inputs, execution rules, minimum coverage checklist, and output order.
Supplement the scope, environment, version, constraints, evidence, and success criteria that truly affect judgment.
First perform an input audit, then distinguish confirmed facts, reasonable assumptions, and questions to be confirmed.
Sort by risk and evidence strength, and produce results that can be directly executed, reviewed, or verified.
When information is insufficient, do not stop at asking questions: first deliver a limited initial version and explain which conclusions cannot currently hold.

Each execution must read prompts/performance-result-analysis.md; it is the complete execution specification of this Skill.
When you need to evaluate or regress this Skill, read evals/eval.yaml and the matching evals/cases/ test cases.
Only read references/、examples/、scripts/ or output-formats.mdwhen the directory actually exists and the task requires it; do not assume assets that do not exist.

Do not look only at average latency
First check whether the load test is valid
Conclusions must state the applicable load and environment boundaries
Do not fabricate system behavior, fields, data, metrics, or root causes that do not exist in the input.
Key conclusions must be linked to evidence; when evidence is insufficient, mark them as assumptions and provide verification methods.
Priorities must state the basis of business impact, likelihood of occurrence, or detectability.

Covered: goal achievement, phase stability, latency distribution, throughput, errors, resources, saturation point, and data credibility.
Facts, assumptions, gaps, and recommendations have been distinguished.
High-risk items have clear priority, evidence, owner, or next step.
The output contains verifiable judgment criteria rather than vague statements.
No unauthorized production write operations or destructive actions were performed.

Listing only checkpoints without stating input conditions, expected results, or evidence.
Marking all items as high priority, losing the value of trade-offs.
Replacing domain judgment with tool names or general theory.
Directly refusing when input is incomplete, or conversely pretending conclusions are already certain.

Start from the path most likely to cause business loss, security issues, or release blockage.
Use the smallest verifiable experiment to reduce uncertainty, and record reproduction conditions.
Make the output directly executable and reviewable by another engineer.