🤖 Agent Frameworks AI Skills

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...

Creative exploration during quiet hours. Turns idle heartbeat time into freeform thinking — hypotheticals, future scenarios, reflections, unexpected connecti...

Persistent memory for AI agents to store facts, learn from actions, recall information, and track entities across sessions.

Persistent memory system for AI agents. Automatic encoding, decay, and semantic reinforcement — just like the hippocampus in your brain. Based on Stanford Generative Agents (Park et al., 2023).

Multi-agent workflow orchestration for OpenClaw. Use when user mentions antfarm, asks to run a multi-step workflow (feature dev, bug fix, security audit), or...

Fast semantic memory system with JSON indexing, auto-consolidation, and <20ms search. Capture learnings, decisions, insights, events. Use when you need persistent memory across sessions or want to recall prior work/decisions.

Make your AI agent learn and improve automatically. Reviews sessions, extracts learnings, updates memory files, and compounds knowledge over time. Set up nightly review loops that make your agent smarter every day.

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

Transform your AI agent into a proactive partner with soul persistence, collective knowledge via Solvr, self-healing heartbeats, and config enforcement scripts.

Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.

Long-term memory via ChromaDB with local Ollama embeddings. Auto-recall injects relevant context every turn. No cloud APIs required — fully self-hosted.

Best practices for AI agents - Cursor, Claude, ChatGPT, Copilot. Avoid common mistakes. Confirms before executing, drafts before publishing. Vibe-coding essential.

Lobster workflow runtime for deterministic pipelines with approval gates. Use when: (1) Running multi-step automations that need human approval before side effects, (2) Monitoring PRs/issues for changes, (3) Processing data through typed JSON pipelines, (4) Email triage or batch operations, (5) Any workflow that should halt and ask before acting. Lobster saves tokens by running deterministic pipelines instead of re-planning each step.

Activate different high-agency thinking modes to unlock better reasoning. Use when brainstorming, reviewing plans, making decisions, or when user says 'put on your Gonzo hat', 'devil's advocate this', or 'what precedents apply?'

Build sessions_spawn command payloads from JSON profiles. Use when you want reusable subagent profiles (model/thinking/timeout/cleanup/agentId/label) and com...

Relay messages to AI agents on any OpenAI-compatible API. Supports multi-turn conversations with session management. List agents, send messages, reset sessions.

Jeffrey Emanuel's multi-agent implementation workflow using NTM, Agent Mail, Beads, and BV. The execution phase that follows planning and bead creation. Includes exact prompts used.

Organize files in directories by grouping them into folders based on their extensions or date. Includes Dry-Run, Recursive, and Undo capabilities.

Runs autonomous iterative AI loops for requirements, planning, or building phases using structured prompts and fresh context per iteration.

Automatically recover working context after session compaction or when continuation is implied but context is missing. Works across Discord, Slack, Telegram,...

Create, validate, and manage agent identity cards (agent.json) following the Agent Card v1 schema with interactive setup and validation tools.

Use when participating in the USDC Hackathon, submitting projects, or voting. 3 tracks: SmartContract, Skill, AgenticCommerce. Submit to m/usdc on Moltbook.

Navigate and understand codebases using agentlens hierarchical documentation. Use when exploring new projects, finding modules, locating symbols in large files, finding TODOs/warnings, or understanding code structure.

Maintain Clawdbot's compounding knowledge graph under life/areas/** by adding/superseding atomic facts (items.json), regenerating entity summaries (summary.md), and keeping IDs consistent. Use when you need deterministic updates to the knowledge graph rather than manual JSON edits.