Documents the hook enforcement system, bypass mechanisms, and common failure patterns to help agents avoid getting stuck on protected path writes
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See how to installReview the instructions the agent will receive before you install.
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
/hooks-explainer [arguments]
# Or run the script directly
node .claude/skills/hooks-explainer/scripts/main.cjs --help
</usage_example> </examples>
For code discovery and search tasks, follow this priority order:
Before starting: ```bash cat .claude/context/memory/learnings.md cat .claude/context/memory/decisions.md ```
After completing:
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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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.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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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.
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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
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