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panda-recipes
Patterns for creating Panda CSS recipes with variants, compound variants, and M3 styling
Skills IA Données et IA
Découvrez les skills Données et IA, comparez leurs dépôts et consultez leurs instructions.
Catégories: Données et IA
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Explorer
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dependency-guardian
Automated dependency management with security scanning, update orchestration, and compatibility validation
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phoenix-brand-reporting
Press On Ventures brand-consistent reporting. Use when designing charts, dashboards, exports, or LP-facing documents.
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bundle-size
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...
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code-reviewer
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...
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phoenix-precision-guard
Precision and type-safety hardening for Phoenix calculation paths. Use when configuring ESLint/TypeScript, replacing parseFloat in P0 files, or validating Decimal.js precision.
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phoenix-capital-exit-investigator
Capital allocation and exit recycling investigator. Use when working on low-confidence modules like capital allocation and exit recycling.
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code-formatter
Multi-language code formatting and style enforcement with automatic linting fixes
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phoenix-reserves-optimizer
Reserve allocation and follow-on optimization for the Phoenix fund model. Use when working on deterministic reserve engine logic and optimal reserves ranking.
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phoenix-docs-sync
Syncs calculations documentation and JSDoc with actual behavior. Use after non-trivial changes to calculation modules.
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phoenix-waterfall-ledger-semantics
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
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...
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documentation-sync
Detects code/documentation drift, validates examples, generates diagrams, auto-updates documentation
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session-learnings
Extract and codify learnings from planning-with-files sessions
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tech-debt-tracker
Automated technical debt identification, tracking, and prioritization system
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phoenix-xirr-fees-validator
Validation for XIRR and fee calculations. Use when working on server/analytics/xirr.ts, server/analytics/fees.ts, or their truth cases.
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phoenix-advanced-forecasting
Architecture for the 'living model': graduation rates, multi-MOIC analysis, reserves ranking, scenarios, and Monte Carlo. Always sits on top of the deterministic core.
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security-scanner
Comprehensive security scanning for SAST, secrets, OWASP vulnerabilities, container and IaC security
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react-refactoring
Guide for refactoring complex React/Next.js components to be clean, modular, and maintainable. Use when code is too long (>200 lines), complex, or hard to maintain.
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website-audit
A comprehensive auditing suite for the JupitLunar website. It performs deep scans for broken links, console errors, accessibility issues, SEO compliance, and feature verification. Use this when the user asks to...
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clean-project
Guide for cleaning up the project structure, removing unused files, and organizing documentation. Use when the user asks to "clean the project", "remove unused files", or "organize the codebase".
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skill-foundry
Create and validate portable Agent Skills. Use when authoring new skills, validating existing skills, or learning skill best practices. Guides through structure, portability, and progressive disclosure.
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hello-world
Example skill showing minimal structure. Use as a template for creating new skills.
GitHub
research
Use when current web info needed to avoid broken implementations
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.