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Reconcile baseline-versioned files that the `create-baseline upgrade` CLI staged for LLM-assisted semantic merge. Use when the CLI prints "Open Claude Code and run /upgrade-project to reconcile". Reads the stage manifest at `.claude/state/upgrade/<ts>/manifest.json`, reasons through each pending file's three-way delta in main context, writes a reconciled LOCAL, then deletes the stage when every file lands. Supports `--dry-run` (preview the reconciled diff without writing) and a structured "needs-user-input" fallback when the conflict cannot be disambiguated automatically.

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Generate or repair a project's .gitignore. Composes the baseline must-ignore set (the single source of truth in baseline-ignores.json) with optional gitignore.io enrichment, and merges add-only so existing entries are never lost. Use when a repo has no .gitignore, when the commit-leak guard reports a gap, or when onboarding a new stack. Trigger: "set up gitignore", "fix my gitignore", "ignore node_modules/secrets", "gitignore for <stack>".

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Drift check between the baseline implementation on disk and the claims in `docs/init/seed.md` + cross-references in CLAUDE.md, README.md, and the rendered docs site. Verifies hook/agent/skill/command names + counts, settings.json wiring, project.json key presence, .mcp.json servers, vendored license files, and helper script presence. Exit 0 PASS / 1 FAIL — suitable for CI. Read-only; safe to invoke any time.

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Use when the user asks to "map what our surfaces say today", "inventory our current messaging", or "find the gap between what we say and what we mean"; produces the narrative baseline — a surface-by-surface inventory of what every owned touchpoint (homepage, pricing, docs, decks, social bios, email footers) claims RIGHT NOW, each line labeled Measured / User-provided / Estimated, plus a per-surface gap read vs the intended message and the drift-baseline snapshot the Evaluate phase measures future drift against. Not for authoring the canon — use message-system-architect; not for scoring the surfaces or running the vetoes — use narrative-quality-auditor. 现状叙事盘点/各触点口径/意图差距/漂移基线

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Diagnose MSBuild build performance bottlenecks using binary log analysis. Only activate in MSBuild/.NET build context. USE FOR: identifying why builds are slow by analyzing binlog performance summaries, detecting ResolveAssemblyReference (RAR) taking >5s, Roslyn analyzers consuming >30% of Csc time, single targets dominating >50% of build time, node utilization below 80%, excessive Copy tasks, NuGet restore running every build. Covers timeline analysis, Target/Task Performance Summary interpretation, and 7 common bottleneck categories. Use after build-perf-baseline has established measurements. DO NOT USE FOR: establishing initial baselines (use build-perf-baseline first), fixing incremental build issues (use incremental-build), parallelism tuning (use build-parallelism), non-MSBuild build systems. INVOKES: binlog MCP server tools (overview, errors, search, items, properties); falls back to dotnet msbuild binlog replay + grep/cat when the MCP is unavailable.

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SEO drift monitoring: capture baselines of SEO-critical elements, detect changes, and track regressions over time. Git for SEO: baseline, diff, and track changes to your on-page SEO. Use when user says "SEO drift", "baseline", "track changes", "did anything break", "SEO regression", "compare SEO", "before and after", "monitor SEO changes", or "deployment check".

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为尚未使用机器学习的现有应用梳理推荐、分类、预测等功能的引入步骤，从明确问题、检查数据到初始模型的训练、评估和集成。

• 为现有应用添加推荐功能
• 检查训练数据是否准备就绪
• 为预测功能设计备用规则

将性能优化拆解为现状测量、单个假设的方案比较和正确性验证，在时间与费用上限内选出测得最快且安全的方案，并保留可重复执行的命令、配置和记录。

• 通过测量优化缓慢操作
• 在相同输入条件下比较实现
• 评估响应时间、吞吐量和执行费用

训练并评估从WiFi信号估计人体姿态的模型。将结果与始终输出平均姿态的基准模型比较，检查训练数据是否混入评估数据，并按实际测量条件报告改进幅度。

• 使用相机图像的姿态标签训练
• 评估推理时无需相机的姿态估计
• 与平均姿态基准比较准确度

按时间比较已保存的基准测试结果，整理胜率、响应时间及转入额外处理的比例等变化，帮助发现仅靠合格或不合格判定难以察觉的性能下降。

• 发布前检查性能趋势
• 扩充测试数据后复查结果
• 更新agent-booster后检查延迟

在真实浏览器中验证界面操作，将操作记录、验证结果和截图保存为可重放的会话，用于比较历史画面，或在CI失败后重新执行已保存的测试。

• 验证浏览器操作流程
• 记录界面变更前的比较基准
• 与历史测试截图比较差异

使用预设测试用例检查agent-booster的成功率和任务分流，可选与外部模型比较，并将各用例的实测结果和汇总保存为JSON。

• 发布前检查成功率
• 新增测试用例后验证任务分流
• 用实测结果核对性能说明

Compare accessibility findings between two versions of a web page. Identify newly introduced and fixed issues, count existing findings, and report evidence and suggested fixes without editing application code.

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• Check accessibility before committing local changes
• Compare a page against a branch baseline
• Confirm which accessibility issues a change fixed

Prepare a one-page briefing before a company's quarterly earnings report. Gather market estimates, rank key metrics, and outline optimistic, baseline, and pessimistic scenarios, including the developments that could influence the stock's reaction.

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• Preparing a briefing before quarterly earnings
• Prioritizing company-specific metrics to watch
• Comparing optimistic, baseline, and pessimistic scenarios

Review source code for MCP, a protocol connecting AI applications to tools. Check authentication, input validation, unsafe execution, and other security risks, then report findings with file and line references and separate manual follow-ups.

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• Reviewing an MCP server before release
• Checking authentication and validation on a network-exposed server
• Auditing tool code for unsafe execution paths

Create integration tests for .NET database access before migrating from Oracle to PostgreSQL. The tests record expected Oracle behavior with controlled test data and portable checks, providing a baseline for later migration validation.

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• Recording Oracle behavior before migration
• Testing repository methods and stored procedure calls
• Checking empty strings, NULL inputs, and datetime precision

Audit Azure Storage settings for public containers, encryption, access permissions, network restrictions, and logging gaps. Use command-line checks and monitoring alerts to produce findings and remediation guidance across subscriptions.

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• Auditing storage settings across Azure subscriptions
• Investigating publicly accessible blob containers
• Verifying encryption, network, and logging controls

Investigate malware that survives OS reinstallation by checking UEFI firmware, boot files, and Secure Boot settings. Use chipsec and supporting tools to compare trusted baselines, identify bootkit indicators, and document findings.

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• Investigating reinfection after OS reinstallation
• Checking unexpected Secure Boot changes
• Comparing firmware against a trusted vendor baseline

Inspect Windows startup entries with Sysinternals Autoruns to find possible malware persistence, settings that let malware restart automatically. Review signatures, file locations, and changes from a clean baseline to identify entries that need investigation.

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• Investigating suspicious startup entries on a Windows endpoint
• Comparing startup settings against a clean baseline
• Checking for remaining malware startup entries after cleanup

Improve prompts through test cases and baseline comparisons, measure how well AI answers use retrieved sources, and check agent configurations. Includes Python tools for prompt analysis, retrieval quality reports, and token estimates, with costs calculated from supplied rates.

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• Compare prompt revisions against a baseline
• Measure answer quality against retrieved sources
• Check agent tool settings and loop risks

Add or update Handsontable performance tests for rendering and user interactions. The skill guides trace recording, measurement boundaries, baseline comparisons, and reports that help reviewers identify performance changes in pull requests.

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• Add a performance test for a Handsontable feature
• Investigate filtering or sorting performance
• Compare performance changes in a pull request

Check running experiments, collect logs and result metrics, and summarize progress alongside baseline comparisons. It supports SSH servers, Vast.ai, and Modal, with optional Weights & Biases metrics and cost reporting for the supported compute services.

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• Check whether remote experiments have finished
• Compare collected metrics with the matching baseline
• Investigate NaN values or diverging training runs

Compare two GEO audits, which assess a site's readiness for AI search visibility, and produce a monthly client report. Summarize score changes, crawler access, completed actions, newly discovered issues, and priorities for the following month.

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• Prepare a monthly GEO progress report for a client
• Compare baseline and current audit scores
• Review completed actions and next month's priorities

Research key competitors and produce cited profiles, a comparison table, and implications for a product decision. Separate facts from inferences and assumptions, highlight evidence quality, and create a consistent baseline for later comparisons.

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• Reviewing a product's positioning against competitors
• Informing roadmap priorities with competitive evidence
• Investigating a rival's strengths after lost deals