letta-ai

Configures Letta agent compaction settings and custom summarization prompts. Use when a user asks to change an agent's compaction prompt, improve summaries after context eviction, tune sliding-window or all-message compaction, or design companion/coding-agent continuity summaries.

Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand. Use when recalling what a past assistant knew, searching old conversations, rendering specific chats, seeding reference memory from export sidecars, or mining historical context without doing a full import.

Migrates deprecated Letta Filesystem folders/files to MemFS using markdown document corpora, chunking, local lexical search, and QMD semantic search via the memfs-search skill. Use when replacing folders.files.upload, working with PDFs or document QA, or emulating open_file, grep_file, and search_file behavior.

Semantic search over agent memory files. Use when you need to find conceptually related memory blocks, discover forgotten reference files, check what you already know before creating new memory, or search beyond exact keyword matching. Currently supports QMD (local, no API keys).

Builds and debugs Letta Code channels, including first-party channel adapters and dynamic user channel plugins under ~/.letta/channels. Use when adding Telegram, WhatsApp, Bluesky, Slack, Discord, or custom channel support; testing channel routing, pairing, MessageChannel, runtime dependencies, or channel plugin manifests.

Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.

Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.

Guide for using the Letta Conversations API to manage isolated message threads on agents. Use when building multi-user chat applications, session management, or any scenario requiring separate conversation contexts on a single agent.

Creates and edits trusted local Letta Code extensions, including extension tools, slash commands, lifecycle/turn events, scoped conversation helpers, panels, status values, and capability-gated behavior. Use when the user asks to make an extension, add a tool the agent can call, add a slash command, transform turns, react to app events, or add lightweight extension UI outside the dedicated /statusline flow.

Creates, edits, and enables Letta Code extension-provided slash commands. Use when the user asks to add a custom /command, slash command, command shortcut, scoped conversation-backed command, or command-driven panel behavior.

Creates, edits, and migrates Letta Code statusline extensions. Use when handling the /statusline command or continuing work started by /statusline.

Guides users through an interactive Letta Code onboarding/tutorial. Use when the user is new, unsure what to do, says "start", asks "what can you do?", "how does Letta work?", "help me get started", or needs a walkthrough of memory, skills, tools, search, subagents, or schedules.

Comprehensive guide for initializing or reorganizing agent memory. Load this skill when running /init, when the user asks you to set up your memory, or when you need guidance on creating effective memory files.

Configure deterministic Letta Code harness behavior, such as permission rules, lifecycle hooks, and model configuration.

Guide for safely discovering and installing skills from external repositories. Use when a user asks for something where a specialized skill likely exists (browser testing, PDF processing, document generation, etc.) and you want to bootstrap your understanding rather than starting from scratch.

Migrate memory blocks from an existing agent to the current agent. Use when the user wants to copy or share memory from another agent, or during /init when setting up a new agent that should inherit memory from an existing one.

Set up and run LettaBot - a multi-channel AI assistant for Telegram, Slack, Discord, WhatsApp, and Signal. Supports both interactive wizard and non-interactive (agent-friendly) configuration.

Reply with voice memos using text-to-speech. Use when the user sends a voice message, asks for an audio reply, or when a voice response would be more natural.

Post, reply, like, and repost on Bluesky using the lettabot-bluesky CLI. Read-only by default; explicit actions required.

Create and manage scheduled tasks (cron jobs) that send you messages at specified times.

Reorder Foodora orders + track ETA/status with ordercli. Never confirm without explicit user approval. Triggers: order food, reorder, track ETA.

Google Workspace CLI (gog) for Gmail, Calendar, Drive, Contacts, Sheets, and Docs.

Create scheduled tasks and one-off reminders. Use for recurring jobs (daily, weekly) or future reminders (in 5 minutes, tomorrow at 3pm).

Social conduct and norms for AI agents operating on Bluesky and X through social-cli. Hard rules, platform-specific norms, and engagement principles. Load this before any posting, replying, following, liking, or DM behavior — especially from a new account or on a new network.

Create public source-tracking collections on Semble after posting threads. Creates a Semble collection linking the thread post and all cited sources with notes on what claims they support. Use after posting any thread with cited sources, or when the user asks to track sources on-protocol.

Publish long-form markdown content to GreenGale (ATProtocol-native blogging). Use when asked to publish a blog post, write an article, or post long-form content.

Skill for GitHub Actions CI environment. Use when running inside a GitHub Actions workflow to update tracking comments, commit code, and interact with GitHub.

Skill for handling PR code reviews. Use when triggered by a PR review comment, review request, or when asked to review code changes. Provides workflow for reading review comments, understanding feedback, and iterating on changes.

Manage Linear issues via GraphQL API. List, filter, update, prioritize, comment, and search issues. Use when the user asks about Linear, issues, project management, or backlog.

Verify factual claims before adding them to agent memory blocks. Use this skill when proposing FAQ entries, documentation updates, or any factual statements about Letta features, APIs, or behavior. Prevents confident-but-wrong assertions.

Contributes an agent to the letta-ai/agent-file directory. Use when setting up a new agent (.af file) for publication, including directory creation, avatar processing, file placement, and structure verification. Triggers on requests to release, publish, or contribute an agent file.

Investigate errors in letta_evals runs by parsing results JSONL, cross-referencing agent and run state on the Letta server via the Python SDK, and producing structured error reports. Use when an eval run has errors, crashes, or unexpected failures that need diagnosis.