Agent Claude Code
Writes tests first, then implementation. Strict TDD.
Agent Claude Code
Writes tests first, then implementation. Strict TDD.
Agent Claude Code
Read-only ALTAI repository exploration. Use when file ownership or existing patterns are unclear before implementing a roadmap item.
Agent Claude Code
Implements one bounded ALTAI roadmap slice and its tests. Default working agent for roadmap PRs.
Agent Claude Code
Read-only diff review for a risky ALTAI PR. Reports actionable findings only.
Agent Claude Code
Triages failing tests/logs into product defects, flaky tests, environment problems, and pre-existing failures.
Plugin Claude Code
Bundles 2 skills, 1 hook · 97 tokens together
Sample plugin for verifying ALTAI's Claude Code plugin loader. Ships one skill, one agent, one command, one hook, one MCP server.
Skill Claude CodeCodex
Use the ALTAI terminal product to run agents, inspect durable workspace state, and automate safe coding workflows through human-readable or JSONL output.
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Flax NNX Filters (filterlib) for selecting Variables in Modules/State using types, tags, paths, predicates, and logical combinations.
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Explains Flax NNX GraphDef (static structure) and GraphState (dynamic values), the internal representation used by the functional API.
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Guidelines for using flax.
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Explains the Flax NNX Functional API (split, merge, state, update, graphdef) for bridging stateful Modules and JAX's functional paradigm.
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Details Flax NNX lifted transforms (nnx.jit, nnx.grad, nnx.vmap, nnx.scan) for applying JAX transformations directly to nnx.Module methods.
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Explains the core Flax NNX abstraction nnx.Module for defining neural network components as stateful Python classes.
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Explains Flax NNX nnx.Optimizer for managing Module, Optax state, and optimizer updates in training loops.
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Details Flax NNX nnx.Rngs for managing PRNG keys within Modules, explaining streams, key generation, and state management.
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Explains Flax NNX nnx.Variable (mutable state container in Modules) and nnx.VariableState (immutable JAX-compatible state).
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Tutorial chapter for google-adk's Agent (BaseAgent / LlmAgent), detailing the core agent abstraction, its structure, hierarchy, and the LLM-powered implementation.
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In the previous chapter, we learned how Event objects capture the history of interactions within a session. Many of these events, particularly agent responses and decisions to use tools, originate from interactions with a Large Language Model (LLM). This chapter introduces BaseLlm, the core abstraction in google-adk…
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Details google-adk's BaseLlmFlow, the core engine orchestrating LLM interactions, processors, tool calls, and agent transfers for LlmAgent.
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Details google-adk's Event object, representing interaction units with author, content, actions, and metadata within a Session.
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In the previous chapter, we explored BaseLlmFlow, the engine that orchestrates the complex interaction between an Agent (BaseAgent / LlmAgent) and the BaseLlm, especially when handling Tools (BaseTool). We saw that the flow logic often needs access to the current session, services, agent configuration, and more.…
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Tutorial chapter for google-adk's Runner, detailing its role in orchestrating agent execution, managing sessions, artifacts, memory, and selecting agents.
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Details google-adk's Session object for conversational history/state and BaseSessionService interface for session management.
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Details google-adk's State object for managing session state with scopes and delta tracking.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: