Prompt engineering, RAG, context management, and agent memory.
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Repository knowledge engine plugin. Bundles CLI-backed slash commands (rb-setup / rb-init / rb-refresh / rb-ask) plus the agent-repo-init skill. Works in both Claude Code and Codex CLI.
信念对决。当父子节点内容冲突、或两条你都认可的记忆逻辑上不能并存时使用。
记忆审计入口。当我主动决定审视记忆质量时，先读此文件判断应使用哪个子技能。
Advanced PM skills: AI Product Canvas, Multi-Source Signal Synthesiser, Experiment Designer, Design Handoff Brief. For senior PMs working on complex or AI-powered products.
可发现性审计。当disclosure写法有问题、parent放错、alias缺失、子节点过多时使用。
模式提取与失效解药分析。当发现多条记忆在讲同一个教训，或发现自己在一而再再而三地犯同样的错误时使用。
Data & metrics skills: Data Analysis Standard, Retention Analysis, Product Health Analysis. Structure metric deep-dives, funnel analysis, cohort studies and churn investigations.
死数据清洗。当一条记忆读不读你的行为都不会变、感悟没有现实锚点时使用。
Claude Code integration for LycheeMem structured long-term memory.
Forceful operating rules for using LycheeMem as the primary structured long-term memory path inside OpenClaw.
A tool to vectorise repositories for RAG.
Methodology for writing or improving prompts and system prompts that drive any LLM. Use when authoring or revising a prompt for a model task — grouping, classification, extraction, generation, copywriting, labeling, agent instructions, prompt templates, skill instructions — to decide how much to constrain the model based on the task type (open-ended vs single-correct-answer) and write the most fitting instructions. Triggers: "write a prompt", "help me write or improve a prompt", "how should I change this prompt", "this prompt isn't working", "write instructions for the model", "prompt-writer". Not for: answering the user's question directly, or writing articles and documents meant for human readers (those are not prompts that drive a model).
Biologically-inspired memory for Claude Code. Memories decay by default, retrieval strengthens them, errors stick longer. Auto-captures context at session start and learns from errors.
Self-improving Claude Code and Codex plugin that turns corrections into Preferences, Project-specific skills, and Shared skills
Use SwarmVault when the user needs a local-first knowledge vault that writes durable markdown, graph, search, dashboard, review, chat-session, context-pack, task-ledger, static AI export, retrieval, and MCP artifacts to disk from books, notes, transcripts, exports, datasets, slide decks, files, URLs, code, and recurring source workflows.
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Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling
Use when reviewing a proposed REST or GraphQL API change before merge — checks contract clarity, backwards compatibility, errors, pagination, auth, and naming.
Use when first encountering a new dataset — produces a structured profile (schema, missingness, distributions, outliers, gotchas) before any analysis.
Claude Mind - Give Claude photographic memory in ONE portable file. Share, version, and transfer your Claude's brain.
Use after an incident is resolved — drafts a blameless postmortem from timeline notes, alerts, and chat threads.
Use when capturing an architecture decision so it survives turnover — produces an ADR-NNNN.md from context, options considered, and the chosen path.
Persistent semantic memory for Claude Code — auto-injects relevant context at session start, captures git commits, test results, and research via hooks, and provides 30+ MCP tools for memory management.
Use after a session to promote useful episodic notes from logs/episodic/ into distilled, dated entries in MEMORY.md and memory/semantic/.
Use when the user asks for a sourced briefing on a topic that spans multiple web sources and requires citations.
Semantic search for Claude Code conversations. Remember past discussions, decisions, and patterns.
Use before opening a PR to audit the changes for stale comments, unused imports, missing tests, and inconsistencies with neighboring code.
Use when planning the next sprint — turns ticket intake + team capacity into a planned sprint with explicit non-goals.
AI-powered code indexer with semantic search and knowledge graphs
Use when opening a PR — produces a clean PR description (what / why / how to verify / risks) from a branch diff against base.
Use before connecting a new MCP server to your agent — produces a structured security review covering source, permissions, tools, network, and approvals.
Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.
Use when the workflow lacks error handling, has been failing in production, or needs retry logic, fallback strategies, and circuit breakers.
Analyze command history to identify which skills work, which fail, and where to improve.
Use when the workflow needs to self-correct, improve over time, or establish feedback loops and evaluation cycles.
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Use when the workflow needs multi-step processing with sequential, parallel, or conditional tool compositions and proper data flow.
Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.
Use when the user wants to create templates, extract reusable patterns, document solutions, or build a pattern library from working workflows.
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Use when the workflow feels too complex, has accumulated cruft, or has redundant steps and overlapping tools that need consolidation.
Use when the workflow works but needs polish, or as the final step in a diagnose → fix → refine cycle before shipping.
Use when the user wants to tailor a workflow for a specific industry, domain, or vertical with specialized expertise, terminology, and guardrails.
MCP server for Maestro — exposes 25 workflow skills as tools, prompts, and resources for any MCP-compatible AI client
Use when starting a new project with Maestro or when no .maestro.md context file exists yet. Run once per project.
Use when the user wants to find problems, audit workflow quality, or get a comprehensive health check on their AI workflow.