Use when apple-dev has finished feature code + Unit tests (Swift Testing) and is about to enter code-review. Provides the Apple SIT scope (xcodebuild test + simulator per declared target), the AC-driven integration walk, APIProtocol-mock discipline, and evidence sink (progress/apple-dev.md). SIT is dev-owned; apple-code-reviewer audits the evidence.
npx skills add https://github.com/pcliangx/AppGenesisForge --skill agf-running-apple-sit
Use this skill when:
SIT verifies that independently-developed components compose correctly at the app-integration layer — SwiftUI 视图 ↔ AppCore 业务层 ↔ swift-openapi-generator 生成的 client ↔(mock 后端)。It is NOT:
swift test 已在分支全绿)If a failure reproduces in plain swift test with mocks, it's a unit-level miss — fold it back into the unit suite, don't write it up as a SIT defect.
maincd apple/AppCore && swift test,strict concurrency 零 warningdocs/changes/<change>/tasks.md(AC↔scenario 映射,ADR-012;旧 feature fallback docs/prd/[feature]-[date].md),且每条 AC 的 target 归属清楚(macos / ios / universal)APIProtocol(ADR-008),禁手写 JSON fixture;openapi.json 与 main 上后端导出一致xcrun simctl list devices available 有目标 destinationIf any precondition fails: SendMessage product-lead, do not proceed.
SIT 在模拟器 / 本机层执行(非签名分发包——那是 E2E 的事):
# iOS target
xcodebuild test -project apple/App.xcodeproj -scheme App \
-destination 'platform=iOS Simulator,name=iPhone 16' \
-resultBundlePath sit-ios.xcresult
# macOS target
xcodebuild test -project apple/App.xcodeproj -scheme App \
-destination 'platform=macOS' \
-resultBundlePath sit-macos.xcresult
uvicorn app.main:app)+ 真实 Postgres(docker compose up -d postgres),client 指向 localhost;LLM 类 AC 用 dedicated SIT key(参见 agf-wiring-apple-llm 的 env 契约)。Walk every AC from docs/changes/<change>/tasks.md(旧 feature fallback PRD). For each AC at the integration layer:
xcrun xcresulttool get --path *.xcresult --format json 关键段)/ 后端响应摘录> Verify, don't assume. 不许因为"代码看着对"写 Passed——跑命令、贴真实输出、对比。
证据全部进 progress/apple-dev.md 的 SIT 证据 段(pool 模式 progress/apple-dev-<N>.md)——pass = 单行 AC 标注(✅ AC-N (integration, macos|ios): <一句话>),fail/blocked 展开命令 + 输出 + 偏差。大体积 xcresult → progress/evidence/[feature]/ 按路径引用。
Format authority: .claude/standards/ac-lifecycle.md → 完整条目格式(5 段格式)。UAT 签字后由 product-lead 归档到 docs/qa/[feature]-process-log.md。
APIProtocol(ADR-008)❌ Redo SITToolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take pcliangx/agf-running-apple-sit from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.