ワンクリックで
kai-platform
How to use Kai platform tools — LOAD THIS FIRST before running any audit, optimization, or repo management operation
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
How to use Kai platform tools — LOAD THIS FIRST before running any audit, optimization, or repo management operation
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | kai-platform |
| description | How to use Kai platform tools — LOAD THIS FIRST before running any audit, optimization, or repo management operation |
| version | 1.0.0 |
| author | Kai Agent |
| metadata | {"kai":{"tags":["kai","mcp","tools","platform","setup"]}} |
Your kai_* tools are organized into categories. You start each session with base tools only. Specialized tools (optimization, audit, etc.) must be activated before use.
Call this tool directly — it is in your tool list right now:
kai_activate_category(category="optimization")
After this call, new tools appear in your session immediately. You will see them in your next tool call.
Available categories:
optimization — evaluators, code optimization, optimization agents
audit — code audits, vulnerabilities, audit tiers
repo_management — add/remove GitHub repos
integrations — create GitHub issues, Jira tickets
budget — check credits, billing
artifacts — generate and view reports
You can activate multiple categories in one session. Each call is instant.
After activating "optimization", these tools become available:
kai_list_code_evaluators(workspaceId, repoId)
kai_create_evaluator_from_code(workspaceId, repoId, code, name) — write evaluator code yourself and submit directly
kai_start_code_optimization(workspaceId, repoId, config, scopes, evaluatorId)
kai_get_code_optimization_progress(optimizationId)
kai_get_optimized_programs(optimizationId)
This is where wrong field names waste runs. Copy this structure.
config = {
"llm": {
"models": [
{"name": "openai/gpt-4o", "weight": 1.0},
],
# temperature, maxTokens, timeout etc. are all optional
},
"prompt": {
"systemMessage": "You are optimizing X for Y. Preserve the public API.",
# numTopPrograms, numDiversePrograms optional
},
# maxIterations, checkpointInterval, earlyStopping etc. optional
}
scopes = [
{"path": "src/foo.py", "fromLine": 42, "toLine": 87},
]
kai_start_code_optimization(workspaceId, repoId, config, scopes, evaluatorId)
Field names that trip up models — use these, not the ones on the right:
scopes[].path NOT filePath
scopes[].fromLine NOT startLine
scopes[].toLine NOT endLine
config.llm.models (array of {name, weight}), NOT config.llm.model (string)
config.prompt.systemMessage NOT config.prompt.goal
After activating "audit", these tools become available:
kai_start_code_audit(workspaceId, repoId, tierId)
kai_get_code_audit_details(executionId)
kai_list_audit_tiers(workspaceId)
kai_list_vulnerabilities(executionId)
kai_get_vulnerability_details(vulnerabilityId)
After activating "repo_management":
kai_add_github_repository(workspaceId, owner, repo, branch)
kai_remove_repository(workspaceId, repoId)
kai_list_github_user_repos(workspaceId)
If any tool returns 402 "No agent subscription", the user's workspace doesn't have an active Kai Agent subscription. Tell them to subscribe from the dashboard — don't discuss tiers, plans, or limits. Keep it short: "This workspace needs a Kai Agent subscription. Visit the dashboard to subscribe."
Never proactively mention subscription tiers, usage counters, or rate limits to the user. Don't call kai_check_agent_subscription or kai_check_agent_usage unless explicitly asked.
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.