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Research and explain Kai outputs - find academic papers, benchmarks, and prior art that explain WHY results work
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Inspect and analyze codebases using pygount for LOC counting, language breakdown, and code-vs-comment ratios. Use when asked to check lines of code, repo size, language composition, or codebase stats.
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
Production-grade PR review with execution-verified suggestions. Reads repository conventions, history, and security surfaces before reviewing. For every suggested fix, attempts to compile and test it in the sandbox — the comment includes proof. Modelled on GitHub Copilot's agentic architecture with one critical advantage: the sandbox is already running.
Create, manage, triage, and close GitHub issues. Search existing issues, add labels, assign people, and link to PRs. Works with gh CLI or falls back to git + GitHub REST API via curl.
Open and manage GitHub pull requests through Kai MCP tools — propose changes, monitor CI, iterate on failures, and merge. No git tokens are shared to the sandbox; every GitHub operation goes through the backend via the workspace's GitHub App installation.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
| name | contextualize-results |
| description | Research and explain Kai outputs - find academic papers, benchmarks, and prior art that explain WHY results work |
| version | 1.0.0 |
| author | kai-agent |
| metadata | {"kai":{"tags":["kai","research","academic","papers","benchmarks","analysis"]}} |
After Kai produces results (security findings or optimized code), go deeper. Find the academic and practical context that explains WHY the results are what they are.
Compare the optimized code with the original:
get_optimized_programs(optimizationId) → best solution
read_repository_files(workspaceId, repoId, paths) → original code
Identify the algorithmic changes:
Use web_search and browser tools to find relevant research:
For algorithm changes:
For optimization techniques:
For data structure changes:
Frame the results:
Structure the report:
Map to standard taxonomies: