Strictly adheres to specified user flow and game rules, making sure to follow documented features.
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| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Implementing assumed requirements | Code diverges from what user actually wanted | Implement only what is explicitly documented; surface assumptions |
| Skipping acceptance criteria review | "Done" declared before user requirement is satisfied | Check every acceptance criterion before marking work complete |
| Silently accepting scope creep | Feature grows beyond agreed scope; delivery delayed | Flag any extension beyond documented requirements for user approval |
| Resolving ambiguity by guessing | Wrong interpretation leads to rework | Surface ambiguities immediately and wait for explicit clarification |
| No requirements traceability | Cannot audit which code change satisfies which requirement | Link every significant code change to the requirement it satisfies |
Before starting:
cat .claude/context/memory/learnings.md
After completing: Record any new patterns or exceptions discovered.
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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