Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Configurable regex-based alert system for detecting patterns in tool calls and session activity. Supports error triggers, content regex matching, token threshold triggers, and pattern detection with configurable actions.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Read relevant files and understand requirements
Perform the skill's main function using available tools
Return results and save artifacts if applicable
</execution_process>
<best_practices>
</best_practices> </instructions>
<examples> <usage_example> **Example Commands**:# Invoke this skill
/notification-triggers [arguments]
# Or run the script directly
node .claude/skills/notification-triggers/scripts/main.cjs --help
</usage_example> </examples>
Notification triggers are defined in .claude/context/runtime/notification-triggers.json:
{
"version": "1.0",
"triggers": [
{
"id": "unique-trigger-id",
"name": "Human-readable trigger name",
"description": "What this trigger detects",
"type": "error|regex|token_threshold|pattern",
"enabled": true,
"condition": {
"pattern": "regex pattern to match",
"minTokens": 120000,
"minOccurrences": 3,
"scope": "session|task|global"
},
"actions": [
{
"type": "log|alert|spawn_agent|interrupt",
"target": "agent-name|file-path",
"message": "Action message template"
}
]
}
]
}
Condition Fields:
pattern — JavaScript RegExp (error/regex triggers)minTokens — Token threshold for alert (token_threshold trigger)minOccurrences — Pattern match count before triggering (pattern trigger)scope — Where to evaluate: current session, task, or cumulativeAction Types:
log — Write to .claude/context/runtime/notifications.jsonlalert — Display alert in session outputspawn_agent — Create Task() to handle the notificationinterrupt — Pause execution for user inputStep 1: Load Configuration
.claude/context/runtime/notification-triggers.jsonStep 2: Evaluate Conditions For each enabled trigger:
pattern against tool stdout/stderrminTokensminOccurrencesStep 3: Execute Matched Actions If condition is true:
.claude/context/runtime/notification-log.jsonlStep 4: Record Event
{
"timestamp": "ISO-8601",
"triggerId": "id",
"triggerName": "name",
"matched": true,
"context": {
"sessionId": "current session",
"taskId": "current task",
"tokenCount": number
}
}
Log Action
// Append to notifications.jsonl
fs.appendFileSync(
notificationsPath,
JSON.stringify({
timestamp: new Date().toISOString(),
triggerId: trigger.id,
message: renderTemplate(action.message, context),
severity: 'info|warn|error',
}) + '\n'
);
Alert Action
// Write to stdout with standard format
console.log(`\n🔔 NOTIFICATION [${trigger.name}]\n${renderedMessage}\n`);
Spawn Agent Action
// Create async task to handle notification
Task({
task_id: `notify-${trigger.id}-${Date.now()}`,
subagent_type: action.target,
prompt: `Handle notification: ${trigger.description}\nContext: ${JSON.stringify(context)}`,
});
Interrupt Action
// Pause and ask user
const response = await AskUserQuestion({
question: `${trigger.name}: ${action.message}\n\nContinue? [y/n]`,
});
if (response.toLowerCase() !== 'y') {
process.exit(0);
}
{
"id": "token-threshold-warning",
"name": "High Token Usage Warning",
"description": "Alert when session exceeds 120K tokens",
"type": "token_threshold",
"enabled": true,
"condition": {
"minTokens": 120000,
"scope": "session"
},
"actions": [
{
"type": "log",
"message": "Session token count: ${tokenCount}"
},
{
"type": "alert",
"message": "⚠️ Token budget at 60% — consider compression"
},
{
"type": "spawn_agent",
"target": "context-compressor",
"message": "Proactive context compression triggered"
}
]
}
{
"id": "hook-error-pattern",
"name": "Hook Execution Errors",
"description": "Detect repeated hook failures in session",
"type": "pattern",
"enabled": true,
"condition": {
"pattern": "hook.*error|hook.*failed|HOOK_ERROR",
"minOccurrences": 3,
"scope": "session"
},
"actions": [
{
"type": "log",
"message": "Hook error pattern detected: ${patternMatches} occurrences"
},
{
"type": "alert",
"message": "🚨 Hook system unstable — check logs"
}
]
}
{
"id": "security-violation-detect",
"name": "Security Policy Violation",
"description": "Alert on potential security violations",
"type": "regex",
"enabled": true,
"condition": {
"pattern": "shell:\\s*true|JSON\\.parse\\(|eval\\(|exec\\(",
"scope": "task"
},
"actions": [
{
"type": "alert",
"message": "⚠️ Potential security issue detected — review code"
},
{
"type": "spawn_agent",
"target": "security-architect",
"message": "Review potential security issue in task output"
},
{
"type": "interrupt",
"message": "Continue despite security concern?"
}
]
}
For code discovery, follow this priority order:
pnpm search:code "<query>" (Primary intent-based search)ripgrep (for exact keyword/regex matches)Before starting:
cat .claude/context/memory/learnings.md
cat .claude/context/memory/decisions.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
日本語の概要は準備中です。原文の説明を表示しています。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
日本語の概要は準備中です。原文の説明を表示しています。