api-pagination-testing ยท diff
git:20260915.d1274c9 to git:20260915.f49090d
18 added, 1 removed. Audit A to A.
---
name: api-pagination-testing
description: Use this skill when you need to design API pagination scenarios from ordered data and cursor or offset evidence; triggers include API pagination testing.
---
# API Pagination Testing
design boundary, continuity, and consistency candidates from pagination contracts, ordering rules, datasets, and cursor evidence. Produce APT-## findings. This Skill organizes traceable API-quality candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence.
## When to Use
- - When you need undefined candidates from pagination parameter contracts, sort fields, cursor or offset rules, dataset snapshots, concurrent changes, and response samples.
+ - When you need pagination continuity and consistency candidates from pagination parameter contracts, sort fields, cursor or offset rules, dataset snapshots, concurrent changes, and response samples.
- When you need selection rationale, applicability constraints, evidence gaps, and the smallest validation action.
- When inputs are incomplete but a bounded first pass can preserve blocked or unassessed boundaries.
Do not use it to execute tests, invent contract or behavior, replace a complete strategy, or accept risk for a Human.
+ ## Output Format Options
+
+ - Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
+ - Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.
+
## How to Use
1. Read prompts/api-pagination-testing.md and provide the objective, scope, material, environment, and evidence.
2. Complete the known, missing, conflicting, stale, out_of_scope, and assumptions input audit before findings.
3. Record APT-## with the subject, preconditions, behavior of concern, source evidence, and validation, plus impact/priority, owner role, close condition, and evidence state.
4. Preserve conflicts, unknown constraints, and open questions when evidence is incomplete.
## Core Constraints
- Do not execute tests, assume missing rules, versions, thresholds, data, or responses, or treat candidate counts as coverage proof.
- File presence, names, design declarations, and Eval configuration are not runtime evidence.
- Mark unknowns unassessed, blocked, or pending clarification instead of filling them with convention.
- Do not edit requirements, code, test assets, or target systems.
## Pre-delivery Check
- [ ] Recorded the known, missing, conflicting, stale, out_of_scope, and assumptions input audit.
- [ ] Every APT-## has source, evidence state, impact/priority, owner role, close condition, and validation.
- [ ] Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate.
- [ ] Findings are not execution results, coverage proof, or release claims.
## Reference Files
- Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
- Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.
+
+ ## Common Pitfalls
+
+ - Do not turn a method name, file presence, or candidate count into test execution, coverage, pass, or release evidence when scope or evidence is incomplete.
+ - Do not fill in missing rules, thresholds, data, environments, or results from convention; preserve unassessed, blocked, and pending items.
+ - Do not expand this specialist design or review into a complete strategy, full test cases, runtime execution, or a release decision.
+
+ ## Best Practices
+
+ - Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
+ - Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
+ - Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.