letta-ai
Review Skills published by letta-ai, their source repositories, maintenance signals, and identity status.
- Skills
- 5
- Repository stars
- 2,962
- Identity status
- Source-linked
Provenance
Source and identity
- Profile type
- Creator
- Canonical name
- letta-ai
- Public sources
- 1
- License context
- Apache-2.0
Source entries
Agent Skills from letta-ai
Repository stars and maintenance signals provide context, but do not automatically become an individual Skill's quality score.
letta-ai/letta-code
creating-mods
Creates and edits trusted local Letta Code mods, including tools, slash commands, local-only model providers, lifecycle/turn events, scoped conversation helpers, panels, and capability-gated behavior. Use when asked to make a mod, add an agent-callable tool, add a slash command, add a local provider/model adapter, transform turns, react to app events, or add lightweight mod UI outside the dedicated /statusline flow.
letta-ai/letta-code
creating-skills
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Letta Code's capabilities with specialized knowledge, workflows, or tool integrations.
letta-ai/letta-code
generating-mod-envs
Generates and reviews mod learning env JSON files for Letta Code local mods. Use when asked to teach, learn, or optimize a mod behavior; create, draft, validate, improve, or explain envs for `/mods learn --env`; or design evaluation scenarios, memory fixtures, requiredResultMarkers, requiredTraceMarkers, negative controls, and candidate diversity hints.
letta-ai/letta-code
initializing-memory
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.
letta-ai/letta-code
self-configuration
Inspect or modify Letta Code's own memory, model, context window, system prompt, compaction, permissions, toolsets, mods, skills, channels, schedules, and local runtime settings. Use when the user asks how this agent or conversation is configured, or asks you to change how you behave or how the harness runs you.
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Repository context
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