Help Wanted

AI Summary: The Numax project is seeking community help to redesign its website's UI/UX. The current site is described as generic and blends in with common AI/devtool landing page aesthetics. The goal is to create a fresh, distinctive, and enjoyable browsing experience that reflects the project's personality, breaking away from typical patterns while remaining professional and readable. Contributions in design proposals, frontend implementation, feedback, or examples of prior art are welcomed.

AI Summary: This issue proposes adopting OpenAPI to formally document public REST interfaces, leveraging utoipa for generation from Rust types and oasdiff in CI to prevent breaking changes. The initial focus will be on the scheduler API, aiming for strict API enforcement and clear client-facing descriptions.

AI Summary: This issue proposes changing the default landing page of the Dashboard from the Agent Observability page to the Agent Health dashboard. The goal is to provide users with a more relevant overview of agent statuses upon initial access. The change should include a fallback mechanism to the current behavior if the 'agent_health' capability is unavailable.

AI Summary: This issue is a beginner-friendly request to add a new color theme called "Pagoda Twilight" to the KanaDojo project. The contribution requires no coding and can be completed entirely through the GitHub web interface by editing a JSON file. The task involves adding specific color values and an ID for the new theme.

AI Summary: This RFC proposes developing a Skillware-native benchmark to evaluate how models and agent harnesses utilize registry skills, going beyond simple function calls to include features like manifest files, constitutions, and multi-action flows. The goal is to address the current lack of comparative evaluation and a public leaderboard for Skillware's governed skills, differentiating it from existing benchmarks that focus on generic tool usage.

AI Summary: This issue is a "Good First Issue" that requires adding a new Japanese cultural etiquette tip to a JSON file. No coding is involved, and the task can be completed directly in the browser within a minute by forking the repository, editing the specified JSON file, and submitting a pull request.

AI Summary: This issue proposes adding two new engine functions to ArcadeMaker: one to retrieve the coordinates of an existing path and another to dynamically create a new path from a given set of coordinates. Currently, users can only define paths visually in the IDE and use them with predefined functions, but lack the ability to programmatically access or generate path data. This enhancement would allow for more advanced path manipulation, such as intersection checks and code-based path generation.

AI Summary: This issue requests community help in benchmarking the performance of recent zstd compression and chunker algorithm tuning in borg2. The goal is to gather data across various CPU architectures (x86-64, arm64, etc.) to identify the fastest configurations, as automatic selection based on platform or real-time measurement is not yet implemented. Users are asked to run provided benchmark commands on relatively idle machines.

AI Summary: This issue is a call for users of the Open Code Review project to share their usage details. The maintainers want to understand their user base better to improve the project, attract contributors, and plan future development based on real-world scenarios and scale.

AI Summary: This issue proposes refactoring the registration of outbound adapters for SMTP and Telegram. Currently, these adapters are registered at module import time, leading to inconsistent registration based on import order. The goal is to move this registration logic to the bootstrap process, ensuring a single, sanctioned point of composition for tool and integration wiring.

AI Summary: This issue proposes refactoring the `get_investigation_agent_class` function to avoid constructing an LLM client solely for type checking. The goal is to improve maintainability and performance by deriving the CLI-backed/hosted distinction from configuration and passing it down explicitly, rather than relying on an expensive side effect.

AI Summary: This issue proposes refactoring `DefaultReasoningClientProvider` to inject a client factory in its constructor instead of importing and calling `get_llm` globally within the `get()` method. This change aims to improve maintainability and testability by making the client creation process more explicit and controllable by composition roots.

AI Summary: This issue proposes refactoring the `DefaultToolProvider` in `core/agent_harness/tools/tool_provider.py` to accept a subprocess presenter factory as a constructor argument instead of looking it up mid-flow. This change aims to improve maintainability by adhering to dependency injection principles and removing a service locator smell.

AI Summary: This issue proposes to refactor the agent's LLM client handling by making it a required constructor argument instead of a lazy fallback. This change aims to improve maintainability and ensure configuration errors are caught early during construction, rather than mid-execution.

AI Summary: This issue proposes refactoring the `action_driver.py` module to eliminate a silent fallback to a default LLM factory. Instead, an LLM factory will be required during construction, causing configuration errors to surface immediately rather than during an agent's turn. This change aims to improve maintainability and expose missing wiring issues earlier in the development process.

AI Summary: This issue requests adding type hints to builder parameters in `agent_build_config.py`. Specifically, `session`, `console`, and `observer` should be typed with their correct types (`SessionState`, console Protocol, `ToolEventObserver`). If the console Protocol is not yet merged, only `session` and `observer` should be typed to avoid duplication.

AI Summary: This issue proposes to improve type hinting in `ports.py` for `RunRecordFactory.build` and `StreamAnswerFn.__call__`. The goal is to replace generic `Any` return types with specific dataclasses representing the run object's known shape and to correctly type the `client` parameter with the LLM client type. This change aims to enhance code maintainability and ensure accurate token accounting.

AI Summary: This issue proposes to improve type safety for the `BackgroundRunFn` in the interactive shell's background runner. The goal is to replace the current generic `Callable[..., dict[str, Any]]` signature with a more precise `Protocol` for arguments and a `TypedDict` for the return dictionary, ensuring better code maintainability and catching potential type errors.

AI Summary: This issue proposes replacing a generic `Callable[..., Any]` type hint with a more specific `Protocol` in the `platform/harness_ports.py` file. The goal is to improve type safety by accurately reflecting the stable signature of the registered implementation, which returns an agent LLM client. This change aims to eliminate the use of `Callable[..., Any]` and enhance code maintainability.

AI Summary: This issue proposes introducing a more specific `CancelCapableConsole` protocol in `ports.py` to accurately reflect the limited console functionality used by the agent harness. This change aims to improve type safety and maintainability by defining the protocol based on actual call sites, rather than a generic `Any` type.

AI Summary: This issue proposes to improve type safety in the `ReasoningClientProvider.get()` method. Currently, it returns `Any | None`, but all implementations actually return an `AgentLLMClient`. The change involves updating the return type annotation to `AgentLLMClient | None` and fixing any resulting type errors in the codebase, while ensuring no import cycles are introduced.

AI Summary: This issue aims to prevent the reintroduction of untyped `Callable[..., Any]` in specific Python files related to the agent harness and shell runtime. A new quality test will be added to count existing occurrences and fail if new ones are introduced, guiding developers to properly type these elements.

AI Summary: This issue proposes to refactor the `gather-evidence` agent by removing its local, untyped `LlmFactory` alias and instead importing the correctly typed `LlmFactory` from `core/agent_harness/ports.py`. The goal is to establish a single, canonical type alias for `LlmFactory` across the project and ensure type checking passes after the change.

AI Summary: This issue proposes to improve the type hinting for the `PersistToolCalls` callable alias. Currently, it's typed as `list[tuple[Any, Any]]`, which is too broad. The goal is to refine this to `list[tuple[str, Mapping[str, object]]]` to accurately reflect that the first element is a string (tool name) and the second is a mapping of strings to objects (tool input). This change requires updating call sites that might be affected by the stricter typing and ensuring type checking passes.

AI Summary: This issue proposes to improve type safety in the `SessionState` Protocol within the `core/agent_harness/ports.py` file. The goal is to replace the overly permissive `Any | None` type for `reasoning_effort` with the more specific `ReasoningEffortChoice | None`, aligning it with existing usage and ensuring better static analysis. The change is expected to be minimal, with only minor adjustments needed at call sites.

AI Summary: The user is experiencing an issue with Xiaomi phones where the wireless debugging port changes with each activation. They are looking for a way to permanently bind the device to a specific port, possibly using MAC address or manual configuration, to avoid duplicate device entries in their list.

AI Summary: The Riddle team is requesting an update to their app listing on Pipedream. This includes revising the app's description to better reflect its interactive content capabilities beyond just surveys and forms, updating the logo to their current branding, and providing the correct MCP (Message Channel Protocol) URL for their documentation and server endpoint.

AI Summary: This issue proposes to refactor how outbound adapters are registered in the system. Currently, adapters for SMTP and Telegram are registered at module import time, which can lead to unpredictable behavior. The change involves moving these registrations to a central bootstrap process, ensuring a single, controlled point of configuration and improving maintainability.

AI Summary: This issue proposes refactoring the `get_investigation_agent_class` function to avoid constructing an LLM client unnecessarily. Instead, the distinction between CLI-backed and hosted clients should be determined by configuration at a higher level and passed down explicitly, improving maintainability and reducing hidden dependencies.

AI Summary: This issue proposes refactoring `DefaultReasoningClientProvider` to accept a client factory or client in its constructor, rather than importing `get_llm` globally and creating the client on every call. This change aims to improve maintainability and adhere to dependency injection principles by moving the responsibility of client creation to composition roots.