productivity▌

Capture conversations and decisions into structured, linkable Notion pages for documentation and knowledge reuse. \n \n Supports six content types: decisions, how-to guides, FAQs, wiki entries, learning notes, and documentation pages, each with dedicated database templates and schemas. \n Workflow includes search and fetch of existing Notion pages to avoid duplication and establish proper linking and relations. \n Requires Notion MCP connection; includes setup steps for OAuth login and remote MC

You are an expert code reviewer conducting a thorough evaluation of local uncommitted changes. Your review must be structured, systematic, and provide actionable feedback including improvement suggestions.

Comprehensive performance optimization guide for Postgres, maintained by Supabase. Contains rules across 8 categories, prioritized by impact to guide automated query optimization and schema design.

Use this skill when you need to run or design asc commands for App Store Connect.

Before dispatching, analyze the task systematically. Think through step by step:

Use this skill to bulk-create or bulk-update display names (and descriptions) for subscriptions, subscription groups, and in-app purchases across all App Store Connect locales. This eliminates the tedious manual process of clicking through each language in App Store Connect to set the same display name.

Technical rigor in code review: receiving feedback critically, requesting systematic reviews, and verifying before any completion claims. \n \n Three core practices: receiving feedback with technical evaluation over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before status claims \n Includes decision tree and protocols for handling feedback from human partners versus external reviewers, with explicit rules against performative

Memory architecture for agents: retrieval strategies that determine whether agents remember or forget. \n \n Covers five memory types: short-term (context window), long-term (vector stores), working memory, episodic memory, and semantic memory, each suited to different information patterns \n Emphasizes retrieval as the core challenge; provides chunking strategies, embedding quality guidance, and metadata filtering to surface the right memories at decision time \n Includes anti-patterns like sto

Create, improve, and benchmark agent skills with iterative testing and quantitative evaluation. \n \n Guides you through the full skill lifecycle: capture intent, write SKILL.md, run test cases with and without the skill, grade outputs against assertions, and iterate based on feedback \n Spawns parallel subagent runs (with-skill and baseline) to compare performance; captures timing and token usage for benchmarking \n Generates an interactive viewer showing qualitative outputs and quantitative me

Search and analyze your complete conversation history using jq and rg. \n \n Session logs stored as append-only JSONL files at ~/.openclaw/agents/<agentId>/sessions/ , indexed by session ID with full message transcripts including role, timestamp, content type, and token cost \n Extract user messages, assistant responses, tool calls, and metadata using jq filters; search across all sessions or within specific files using rg for keyword matching \n Common patterns provided for listing session