llm-gate
llm-gate implements AI-powered quality verification using Claude Code prompt hooks to validate operations before execution. It intercepts commands like git commits, file writes, and destructive operations, running them through Haiku to check compliance with conventions, security policies, and code standards. Use this skill to establish intelligent guardrails that enforce commit message formats, prevent API key leaks, block unsafe deletions, or validate code patterns beyond what traditional linters can detect.
git clone --depth 1 https://github.com/rohitg00/pro-workflow /tmp/llm-gate && cp -r /tmp/llm-gate/skills/llm-gate ~/.claude/skills/llm-gateSKILL.md
# LLM Gate
Use Claude Code's `type: "prompt"` hooks to create intelligent quality gates that use AI to verify operations.
## Trigger
Use when:
- Setting up commit message validation
- Enforcing code conventions beyond what linters catch
- Creating smart guardrails for specific operations
## How Prompt Hooks Work
Claude Code supports hooks with `type: "prompt"` that run a small LLM (Haiku by default) to verify conditions:
```json
{
"PreToolUse": [{
"matcher": "Bash",
"hooks": [{
"type": "prompt",
"if": "Bash(git commit*)",
"prompt": "Check if this git commit follows conventional commit format (<type>(<scope>): <summary>). The commit command is: $ARGUMENTS. Return {\"ok\": true} if valid, {\"ok\": false, \"reason\": \"...\"} if not.",
"model": "haiku",
"timeout": 15
}]
}]
}
```
The hook:
1. Substitutes `$ARGUMENTS` with the JSON hook input
2. Sends to Haiku (fast, cheap)
3. Expects `{"ok": true}` or `{"ok": false, "reason": "..."}`
4. If not ok → blocks the tool call with the reason
## Example Gates
### Conventional Commit Validator
```json
{
"type": "prompt",
"if": "Bash(git commit*)",
"prompt": "Verify this git commit follows conventional commits: type(scope): summary. Types: feat,fix,refactor,test,docs,chore,perf,ci. Summary under 72 chars. Input: $ARGUMENTS",
"model": "haiku"
}
```
### Destructive Command Guard
```json
{
"type": "prompt",
"if": "Bash(rm *)",
"prompt": "Check if this rm command is safe. Flag if it uses -rf on important directories (src/, node_modules/, .git/). Input: $ARGUMENTS",
"model": "haiku"
}
```
### API Key Leak Prevention
```json
{
"type": "prompt",
"matcher": "Write",
"prompt": "Check if this file write contains hardcoded API keys, secrets, passwords, or tokens. Input: $ARGUMENTS. Return ok:false if secrets found.",
"model": "haiku"
}
```
## Agent Hooks
For complex verification, use `type: "agent"` (runs a full agent):
```json
{
"type": "agent",
"if": "Bash(git push*)",
"prompt": "Review all staged changes for security issues before pushing. Check for: hardcoded secrets, SQL injection, XSS vulnerabilities, exposed internal URLs.",
"model": "haiku",
"timeout": 60
}
```
## Setup Guide
1. Choose which operations to gate
2. Write the prompt (keep it focused, under 100 words)
3. Pick the model (haiku for speed, sonnet for accuracy)
4. Set timeout (15s for prompts, 60s for agents)
5. Add to hooks.json under the appropriate event
## Rules
- Use Haiku for simple checks (fast, cheap)
- Use Sonnet only for complex analysis
- Keep prompts under 100 words for reliability
- Always include `if` condition to avoid running on every tool call
- Set reasonable timeouts (15s prompt, 60s agent)
- Test hooks before deploying to avoid blocking workflowsCoordinate multiple Claude Code sessions as a team — lead + teammates with shared task lists, mailbox messaging, and file-lock claiming. Patterns for team sizing, task decomposition, and when to use teams vs sub-agents vs worktrees.
Auto-configure quality gates, hooks, and settings for a new project. Detects project type and sets up appropriate tooling. Use when onboarding a new codebase.
Decompose large-scale changes into independent units and spawn parallel agents in isolated worktrees. Use for migrations, refactors, codemods, and any change touching 10+ files with the same pattern.
Capture a user-reported defect as a durable GitHub issue written in the project's own domain language. Explores the codebase in parallel for context but never leaks file paths or line numbers into the issue. Use when the user reports a bug conversationally, runs a QA pass, or says "file an issue", "log this as a bug", "capture this".
Smart context compaction with state preservation. Saves critical files, task progress, and working state before compaction, restores after. Use before manual compact or when auto-compact triggers.
Analyzes and optimizes context window usage across sessions. Use when context feels bloated, sessions run slow, or approaching compaction limits.
Master the four operations of context engineering — Write, Select, Compress, Isolate. Manage token budgets, compaction strategies, and context partitioning to keep AI sessions sharp and efficient.
Optimize token usage and context management. Use when sessions feel slow, context is degraded, or you're running out of budget.