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nikhillinit AI Skills

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Explorez les skills IA publiés de nikhillinit et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

17

Étoiles des dépôts des skills indexés

0

Forks du dépôt

0

Moyenne des étoiles par skill indexé

0

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

17 skills

Explorer

GitHub

bundle-size

nikhillinit

Bundle size analysis, budget enforcement, and regression detection for Vite/Rollup builds. This skill should be used when: (1) analyzing bundle composition or total size, (2) checking size budgets, (3) comparin...

GitHub

code-reviewer

nikhillinit

Comprehensive multi-AI code review system for Python, JavaScript, and TypeScript. Use when reviewing code for quality, security, performance, or best practices. Includes automated analysis scripts, language-spe...

GitHub

phoenix-precision-guard

nikhillinit

Precision and type-safety hardening for Phoenix calculation paths. Use when configuring ESLint/TypeScript, replacing parseFloat in P0 files, or validating Decimal.js precision.

GitHub

phoenix-reserves-optimizer

nikhillinit

Reserve allocation and follow-on optimization for the Phoenix fund model. Use when working on deterministic reserve engine logic and optimal reserves ranking.

GitHub

phoenix-docs-sync

nikhillinit

Syncs calculations documentation and JSDoc with actual behavior. Use after non-trivial changes to calculation modules.

GitHub

phoenix-waterfall-ledger-semantics

nikhillinit

Waterfall ledger semantics and clawback behavior for the Phoenix VC fund model. Use when working on tier or ledger waterfall code, clawback behavior, or waterfall truth cases.

GitHub

phoenix-truth-case-orchestrator

nikhillinit

End-to-end truth-case orchestration for the Phoenix VC fund-modeling platform. Use when running tests/truth-cases/runner.test.ts, computing module-level pass rates, updating docs/phase0-validation-report.md and...

GitHub

phoenix-advanced-forecasting

nikhillinit

Architecture for the 'living model': graduation rates, multi-MOIC analysis, reserves ranking, scenarios, and Monte Carlo. Always sits on top of the deterministic core.

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.