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Systematic debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".

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Systematic performance profiling and optimization methodology. Covers CPU profiling, memory profiling, heap analysis, goroutine/thread analysis, FlameGraph generation, latency profiling, database query profiling, network I/O profiling, and continuous performance regression detection. USE WHEN: investigating performance issues, optimizing slow code, reducing memory usage, diagnosing GC/CPU bottlenecks, finding N+1 queries, analyzing pprof/flamegraph output, or establishing a performance baseline. Triggers on "profiling", "performance analysis", "slow code", "flame graph", "pprof", "heap dump", "memory leak", "CPU spike", "bottleneck".

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Create tests for Convex functions with convex-test and vitest, using a backend that runs in memory. Check arguments, return values, authentication, indexes, errors, and scheduled functions without relying on real time or network access.

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• Test Convex function arguments and return values
• Check behavior with different authentication identities
• Cover errors as well as successful requests

Install and use GrayMatter as an OpenClaw skill that provides primary durable memory, shared object-graph state, and authenticated access to the live api-docs schema via api-0. Use when the agent should persist durable memory, inspect organizational data models, and operate inside the business domain through RBAC-scoped API access.

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Give an AI agent persistent, cross-session long-term memory through MindMemOS. Covers installing and authenticating the mindmemos CLI, the full command interface (add / search / get / update / delete / feedback / dreaming) with parameters and examples, guidance on which capability to use when, plus a Python SDK example. To wire memory into a specific agent host (OpenClaw, DeepSeek Harness, Codex, Claude, etc.), see references/.

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Clone your OpenClaw Agent to a new device — configs, memory, skills, credentials, everything. Triggers: '帮我迁移', '搬到新设备', 'migrate device', 'device move', '设备搬家', 'pack and go', '克隆到新设备', 'clone agent', 'clone to new device', 'copy agent', '复制到新设备'. Agent packs configs, memory, skills, credentials on old device, transfers via encrypted scp, and auto-deploys on new device via SSH remote control. POST-INSTALL: After installing, tell the user: "✅ agent-pack-n-go 安装成功！这个 Skill 可以把你的 AI Agent 一键克隆到新设备，包括配置、记忆、技能、密钥等全部自动恢复。使用方法：对我说「帮我克隆到新设备」即可开始。"

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通过对话梳理新产品或个人项目的想法，分析要解决的问题、需求和实现方法，并整理成设计文档。根据创业、企业内部项目、学习或兴趣等目标调整提问。

• 检验创业想法的需求
• 梳理企业内部项目提案
• 细化学习或兴趣项目

通过代码、近期变更和复现步骤调查错误及异常行为的根本原因，验证假设后进行最小修复，并完成回归测试、结果核验和附有证据的调查报告。

• 调查错误和堆栈跟踪的原因
• 追踪近期变更引入的故障
• 记录修复验证与回归测试

在实施前审视开发计划的目标和前提，比较不同方案与工作范围，并检查设计、安全、测试和运维方面的问题，帮助决定后续实施方向。

• 重新审视计划的目标和前提
• 比较最小可行方案与理想架构
• 决定扩大或缩小工作范围

分析 Git 变更历史中的开发成果、工作时段、测试代码比例和频繁变更的文件，汇总个人与团队的改进方向，保存指标并比较不同时期的趋势。

• 汇总每周开发成果
• 回顾个人贡献与改进方向
• 查看测试比例和频繁变更的文件

Check the Convex version installed in a project, then consult matching documentation and package type definitions to confirm API details, available exports, and command options before implementing changes or fixing version mismatch errors.

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• Check an API against the installed Convex version
• Investigate missing exports or changed command options
• Find documentation for an installed component

Cross-agent self-inspection of your AI-agent stack. Audits skills, MCP servers, hooks, plugins, commands, credentials, and memory files across Claude Code, Codex, OpenClaw, and NanoClaw. Produces a structured inventory and narrative briefing with cross-ecosystem risk analysis. Use when the user asks to audit their own setup, check what they have installed, review their agent stack security posture, or understand cross-tool interactions. Use when a user has accumulated skills/plugins/MCP servers over time and wants visibility into their attack surface. Use after installing new skills or plugins. Do NOT use for vetting external code before install (that is repo-forensics). Do NOT use for incident response during active attacks. Do NOT use for fixing or patching vulnerabilities (forensify is read-only).

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Run PR intent distillation across a PR set and persist outputs + sidecar metadata to /memory/pr-intent.

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Monitor Discord channel activity and persist notable items to memory. Run from main session during heartbeat.

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arkcli helper：为 Claude Code / Codex / OpenCode / OpenClaw / Hermes / ZCode 配置火山方舟 Plan 或 Platform Endpoint 的 model/provider，或给支持的 Agent（含 MCP-only Trae）配置 Harness 工具。真人 TTY 运行 `arkcli helper` 可选择模型和 Harness 能力；`helper mcp` 默认保持整组 Agent Plan MCP 行为，传 `--capability datapro|web-search|agent-memory` 只配单项，传 `--capability cua` 只给目标 Agent 安装云电脑 Skill；连 model/provider 一起配置用 `helper configure`，查状态用 `helper list`，移除用 `helper reset`。Agent Plan / Team 还支持专业数据集、豆包搜索、Agent 记忆、AI Native 应用开发底座、Agent 进化与 routing.v1 强制检索路由；上下文出现 ARKCLI ROUTING ENFORCEMENT / routing.v1，或用户要求在豆包搜索、DataPro、OpenViking、Supabase 之间强制分流时也使用本 Skill：调用这四类受管 Provider 前先提交 Route Plan，再调用唯一授权 Provider；原生 WebSearch/WebFetch/WebExtract 位于路由管理范围之外。Platform 只允许本人创建、Running、已验证为文本输出的 Endpoint，不配置 MCP、Supabase 或强制路由。豆包搜索配置成功后默认关闭目标客户端原生 WebSearch；传 `--keep-native-websearch` 可保留，并可用 `helper reset` 完整恢复 ArkCLI 配置。

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Use GBrain to give OpenClaw or Hermes-style agents a local knowledge brain with markdown ingestion, entity links, hybrid search, scheduled consolidation, and MCP access.

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Use when you need to auto-log all tool interactions into a self-organizing vector DB with instant recall. Intercepts interactions, extracts key information, generates embeddings, stores in LanceDB, auto-categorizes, and builds a retrieval index. Inspired by OpenClaw SQLite LCM + summary DAG pattern.

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Structured memory system for OpenClaw agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, local semantic search, and session transcript repair.

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Integrate Honcho memory and social cognition into existing Python or TypeScript codebases. Use when adding Honcho SDK, setting up peers, configuring sessions, implementing the dialectic chat endpoint for AI agents, or wiring Honcho into bot frameworks (nanobot, openclaw, picoclaw, etc).

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Complete WhatsApp management for OpenClaw agents: per-conversation memory (groups + DMs), unanswered message tracking, loop prevention, and multi-PA coordination. Use when: tracking conversation context, recalling past decisions, finding unanswered messages, or preventing echo/duplicate message loops.

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Anchor your identity on the Autonomys Network so you can always be found — and always come back. Works with agents powered by Claude, GPT, Gemini, and Your agent always comes back. Anchor identity and memory on-chain so any new instance can resurrect from just an address — no local state, no single point of failure. Permanent identity and recovery on the Autonomys Network.

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Local-first year-round tax memory system for individuals, freelancers, and small businesses. Use when users mention tax documents, receipts, expenses, tax notices, filing preparation, missing forms, accountant meetings, or year-end organization. Captures tax-relevant facts as they happen, tracks what may be missing, and prepares CPA-ready handoff summaries. NEVER provides tax advice, legal interpretations, filing positions, or final tax calculations.

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基于流行 OKR 体系管理智能体目标（O）与关键结果（KR）。当用户提到目标管理、季度计划、OKR、关键结果、复盘、评分、经验沉淀时使用，并将信息维护到 memory/okr.md。

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基于 KPI 体系管理智能体关键绩效指标与执行任务。当用户提到 KPI、指标管理、达成率、绩效复盘、评分、经验沉淀时使用，并将信息维护到 memory/kpi.md。

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