Outlines the monorepo structure and tooling conventions, emphasizing the use of Taskfile.yml, and proper handling of environment variables.
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packages/ directory and app-specific code in app/.Taskfile.yml commands for development, testing, and deployment tasks..env files or similar configuration.
</instructions>packages/ and app entry points in app/ — mixing concerns in a flat root structure breaks Turborepo/Nx caching and makes cross-package imports non-deterministic..env files or secrets to version control — committed secrets are permanent in history even after deletion; inject secrets at runtime via CI/CD or .env.local (gitignored).| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Mixing app and shared code in flat root | Breaks dependency graph; caching incorrect; circular imports likely | Place shared modules in packages/; app entry points in app/; enforce with import rules |
Committing .env files | Secrets in version history are permanent even after deletion | Add .env* to .gitignore; use .env.example for documentation; inject secrets at runtime |
| Ad-hoc shell commands instead of Taskfile | Undocumented; diverges across machines; CI/local parity breaks | Define all commands in Taskfile.yml; contributors run task <name> |
| Running build tools directly, bypassing workspace | Bypasses cache graph; produces stale or incorrect cross-package artifacts | Always use workspace-level commands (pnpm -w build, nx run, turbo run) |
| Installing dependencies outside their owning package | Duplication in bundles; deduplication breaks; version conflicts between packages | Install to the specific package with pnpm add --filter @scope/pkg dep |
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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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