Loading...
Loading...
Found 1,262 Skills
Validates and scores Claude Code skill packages for quality, completeness, and best practices compliance. Tests Python scripts, checks YAML frontmatter, and generates quality reports. Use when creating new skills, validating skill packages, or auditing skill quality.
Run the standard post-change validation flow after a fix, refactor, or new feature. Use when implementation work is done and you should validate the latest changes by invoking the repo's review skills, starting with review-changes and then repo-doc-maintainer, before giving the final close-out.
**Mandatory** before any commit or push. Run Definition-of-Done checks from change scope and report exact pass/fail per command.
Inspect or run the manual maintenance workflow. Uses explicitly configured repository checks, applies safe auto-fixes, and archives successful maintenance changes.
Use anime-style multi-role continuous conversational code review to output review opinions with natural technical anchors through strong character interaction
Automated code review assistant that analyzes GitHub pull requests and code changes. Use when: (1) user shares a GitHub PR URL and wants a code review, (2) you need to review code for bugs, security issues, or best practices, (3) performing automated code quality checks before merging, (4) analyzing code diffs for potential improvements.
Used for reviewing GitCode PRs, generating in-depth review conclusions or publishing line-by-line comments by combining PR metadata, diffs, and the context of the entire code repository. It is used when users want to review a GitCode PR, check a GitCode PR link, analyze change risks, or publish review comments to a GitCode PR. Typical trigger phrases include "review this PR", "inspect this PR", "check PR", or directly providing a GitCode PR link, such as https://gitcode.com/owner/repo/pull/123.
Follow this sub-process when fixing bugs—turn the verbal description of "discovered a problem" into a closed loop of verification and repair, leaving three documents in the middle: issue report, root cause analysis, and repair record. This process adds a buffer between "seeing the problem" and "starting to modify code", avoiding several common pitfalls: the problem description in your mind disappears after modification, fixing only the surface without analyzing the root cause, uncontrollable expansion of repair scope that cannot be traced, and not knowing if the fix is correct without verification after modification. This skill only acts as a router, deciding which of report / analyze / fix to proceed with based on existing outputs. For simple problems that can be identified at a glance, a fast track will be taken, skipping the two middle steps and only keeping the fix-note.
Refactor code with safety nets — tests green before and after, no behavior change
Use when restructuring existing code without changing observable behavior, especially when a feature or bug fix is hard because the current design is awkward, duplicated, confusing, or risky to modify.
Ultra-lightweight channel for feature workflows: No need to write design docs, checklists, or conduct phased reviews. Let AI write code directly as it normally would, but before it starts, tell it where the CodeStable knowledge base in the project is and how to search it. This way, the code it writes will have fewer pitfalls and be more consistent with project conventions. Trigger scenarios: Users say "fast mode", "fastforward", "skip all those steps", "just start coding", "help me make xxx" and the requirement is too small to go through the design process.
Comprehensive pull request review using specialized agents