odh-ai-helpers

Generate AIPCC Commits style commit messages or summarize existing commits

Hello world plugin implementation

Generate comprehensive sprint summaries by analyzing JIRA sprint data, including issue breakdown, progress metrics, and team performance insights.

Manage Konflux application

Manage Konflux component

odh-ai-helpers:rpm-examine

Install and configure TorchTalk MCP server for PyTorch cross-language analysis

Trace a PyTorch function's cross-language binding chain (Python -> C++ -> CUDA)

Investigates Python package repositories to analyze build systems, dependencies, and packaging complexity. Provides comprehensive guidance on how packages can be built from source using integrated analysis skills.

Triage JIRA bugs against repository code to classify AI fixability. Use when reviewing a backlog of bugs to determine which ones an AI agent can fix.

Evaluate CodeRabbit PR comments and fix or reply

Upload a summary or plan from the current conversation as a GitHub Gist using the `gh` CLI.

Perform a shallow clone of a Git repository to a temporary location.

Debug and monitor GitLab CI/CD pipelines for merge requests. Check pipeline status, view job logs, and troubleshoot CI failures. Use this when the user needs to investigate GitLab CI pipeline issues, check job statuses, or view specific job logs.

Summarize Jira ticket activity, including child tickets, to detect stale tickets in the backlog. Use when user asks to review one or more Jira tickets to determine if they are being worked on.

Create Jira issues in the AIPCC project. Infers summary, description, type, and component from conversation context, confirms with the user before creating. Use when the user wants to file a new AIPCC Jira issue.

Export and upload the current chat conversation as a markdown file attachment to a JIRA ticket for later review and documentation.

Compare CVE vulnerabilities between two OCI container images and generate reports showing fixed and new CVEs.

Resolve the full install-time dependency tree for a Python package. Use when the user needs all transitive dependencies, full dependency list, or install requirements resolved for a specific Python version with environment markers.

Find known packaging bugs, fixes, and workarounds for Python projects by searching GitHub issues and analyzing their resolution status

Analyze Python package build complexity by inspecting PyPI metadata. Evaluates compilation requirements, dependencies, distribution types, and provides recommendations for wheel building strategies.

Investigate environment variables that can be set when building Python wheels for a given project. Analyzes setup.py, CMake files, and other build configuration files to discover customizable build environment variables.

Check whether a Python package license is compatible with redistribution in Red Hat products, using the Fedora License Data as the authoritative policy source. Produces a structured six-field verdict with escalation guidance for non-trivial cases.

Deterministically find license information for Python packages by checking PyPI metadata first, then falling back to Git repository LICENSE files using shallow cloning.

Locate source code repositories for Python packages by analyzing PyPI metadata, project URLs, and code hosting platforms like GitHub, GitLab, and Bitbucket. Provides deterministic results with confidence levels.

Analyze PyTorch internals across Python, C++, and CUDA layers using the TorchTalk MCP server. Use when asked about how PyTorch operators work internally, where functions are implemented, what would break if code is modified, or finding tests for PyTorch operators.

Guides the agent to write unit tests that strictly conform to the project's existing testing structure, patterns, and style by learning from similar tests before writing anything new.

Compare vllm requirements files between versions

Generate slack summaries of vLLM CI SIG Slack channel activity for the RHAIIS midstream release team