boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Update ticket status and track progress in Product as Code workflow
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/pac-update-statusContext preview
What this command does when you run it.
Update ticket status and track progress in Product as Code workflow
Update ticket status and track progress in Product as Code workflow
1. **Parse Command Arguments**
2. **Ticket Selection**
3. **Load Current Ticket State**
4. **Status Transition Validation**
5. **Update Ticket Status**
6. **Update Additional Fields**
7. **Task and Criteria Progress**
tasks:
- [x] Create authentication service
- [x] Implement login form component
- [ ] Add session management
- [ ] Write unit tests8. **Update Parent Epic**
tickets:
- id: "[ticket-id]"
name: "[ticket-name]"
status: "[new-status]" # Update this
assignee: "[assignee]"
updated: "[timestamp]"9. **Git Integration**
10. **Generate Status Report**
Ticket Status Updated
====================
Ticket: [ticket-id] - [ticket-name]
Epic: [epic-name]
Status: [old-status] → [new-status]
Assignee: [assignee]
Updated: [timestamp]
Progress:
- Tasks: [completed]/[total] ([percentage]%)
- Criteria: [met]/[total]
Next Actions:
- [Suggested next steps based on new status]11. **Notification Hooks**
12. **Validation and Save**
/project:pac-update-status --ticket ticket-auth-001 --status in-progress /project:pac-update-status --ticket ticket-ui-003 --status review --comment "Ready for code review" /project:pac-update-status # Interactive mode /project:pac-update-status --epic epic-payment --status done
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Analyze semantic position relative to knowledge boundaries to prevent hallucination and identify uncertainty zones.
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles:…