181 个（alibaba 的仓库） · 按人气排序
Let the host AI agent review Git changes while ocr selects files and resolves review rules. Receive findings grouped by severity, plus coverage details showing which files were reviewed or skipped and why.
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Review Git changes with the ocr command-line tool and a configured AI model. Get line-level findings grouped by severity for working changes, commits, or branch comparisons, with code fixes applied when explicitly requested.
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Investigate Spring configuration and Beans, the objects Spring manages, using Arthas. Check configuration sources, find registered objects by name or type, and inspect registration details while prioritizing read-only queries.
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Investigate high CPU usage in Java applications with Arthas. Identify busy threads, inspect method call paths, and produce an evidence-based diagnosis with specific methods to examine next.
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Retrieve an EagleEye traceId, an identifier linking a request across logs, using Arthas without changing application code. Observe request-handling methods, capture parameters when needed, and inspect method calls and timings to investigate problems.
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Diagnose running Java applications with Arthas. Follow guides for high CPU usage, retrieving EagleEye request tracking IDs, and inspecting Spring configuration and managed objects, then summarize evidence and next steps.
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Find, install, update, and publish AI skills through Nacos, a shared skill registry. Use nacos-cli to manage versions, merge local edits with remote updates, and share reviewed releases across agents, machines, or teammates.
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Clean up local Git branches and remote connections left after pull request reviews. The workflow checks unfinished work, preserves main and the current branch, and requests confirmation before force-deleting branches that have not been merged.
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Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.
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Submit existing local changes as a pull request by reviewing the diff, running project checks, preparing a branch and commit, and pushing to GitHub. It follows contribution rules and preserves template declarations that require human verification.
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Update a project's changelog using GitHub releases, git history, and code differences. Select changes that matter to users, match the existing format, and write concise entries for the requested release.
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Evaluate a proposed software change before implementation. It examines project files and official sources, compares options and trade-offs, and builds a concrete plan covering affected systems, effort, risks, and unresolved questions for the user's approval.
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Archive several OpenSpec changes together after reviewing their documents, task completion, and specification conflicts. The agent checks actual code to decide which specification updates to apply, asks for confirmation, and reports successes, skipped changes, and failures.
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Learn the complete OpenSpec workflow through a small task in an actual codebase. The agent explains each stage while creating a proposal, specifications, design, and tasks, then implements the change and archives its records.
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Implement pending tasks from an OpenSpec change, a planned set of development work. The skill reads relevant specifications and design files, makes focused code changes, and updates task completion as work progresses.
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Continue an existing OpenSpec change by creating one next document. Check progress and dependencies, follow the workflow's template, and report what was completed and which documents are now available to create.
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Archive an OpenSpec change after checking its documents, task completion, and specification differences. The skill offers a choice about updating the main specifications, then moves the change into a dated archive and reports the result.
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Explore development ideas and problems through conversation, code investigation, diagrams, and comparisons. The skill helps clarify requirements, weigh approaches, and surface risks before or during a change, with optional documentation of decisions when requested.
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Set up a new OpenSpec change for a feature, fix, or modification. Create its working directory, show the document sequence and current status, and present the first template so drafting can begin.
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Update main specifications from delta specs, documents describing intended changes. Apply requirement additions, edits, removals, and renames while preserving unrelated content and keeping the change active for further implementation work.
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Create the OpenSpec documents needed to begin implementing a feature or fix. Follow the workflow's dependency order and templates, using completed documents as context until all prerequisites for implementation are ready.
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Review an OpenSpec implementation against its specifications, tasks, and design decisions. The skill checks for missing work, uncovered scenarios, and inconsistencies, then produces a prioritized report with concrete recommendations and code references where available.
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Develop or port CPU kernels, the low-level calculation routines used by MNN. Follow staged implementation guidance, check data layout agreements, register functions, and verify correctness across processor instruction sets and numerical precision settings.
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Run MNN checks and regression tests on a host computer or Android device, with stage summaries and failure logs. A separate workflow benchmarks LLM prompt processing and token generation on real iPhones and iPads, including branch comparisons.
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