@alirezarezvani
CTO in HealthTech | Engineering AI that helps people move safely and live better . Augmented AI | Agentic Coding | Turning complex problems into simple solution
353 个技能8 个仓库 3.1万
Auto-generate API documentation from code and comments. Use when API endpoints change, or user mentions API docs. Creates OpenAPI/Swagger specs from code. Triggers on API file changes, documentation requests, endpoint additions.
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Generate conventional commit messages automatically. Use when user runs git commit, stages changes, or asks for commit message help. Analyzes git diff to create clear, descriptive conventional commit messages. Triggers on git commit, staged changes, commit message requests.
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Detect exposed secrets, API keys, credentials, and tokens in code. Use before commits, on file saves, or when security is mentioned. Prevents accidental secret exposure. Triggers on file changes, git commits, security checks, .env file modifications.
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Automatic code quality and best practices analysis. Use proactively when files are modified, saved, or committed. Analyzes code style, patterns, potential bugs, and security basics. Triggers on file changes, git diff, code edits, quality mentions.
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Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
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Verify every @path chain import and every markdown link inside every CLAUDE.md in this project resolves to an existing file. Read-only — returns broken links with file:line refs, never edits.
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Re-detect this project's tech stack from package.json / requirements.txt / pyproject.toml / go.mod / Cargo.toml and diff it against the Tech Stack section of every CLAUDE.md. Read-only — returns added / removed / renamed dependencies, never edits.
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Analyzes, generates, and enhances CLAUDE.md files for any project type using best practices, modular architecture support, and tech stack customization. Use when setting up new projects, improving existing CLAUDE.md files, or establishing AI-assisted development standards.
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Behavioral guardrails for LLM-assisted coding. Use when writing, reviewing, or refactoring code in any project to avoid overcomplication, keep changes surgical, surface assumptions early, and execute against verifiable success criteria.
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Audit every CLAUDE.md in this project for drift against the last week of git history. Flags sections that reference deleted files, renamed paths, or removed dependencies. Read-only — returns a punch list, never edits.
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Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.
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Analyze incoming user requests to detect intent, request type (design/validate/debug/document), complexity level, and identify vague requirements or buzzwords that need clarification. Use when cto-orchestrator receives new requests that need classification before routing to specialist agents.
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Generate targeted clarifying questions (2-3 max) that challenge vague requirements and extract missing context. Use after request-analyzer identifies clarification needs, before routing to specialist agents. Helps cto-orchestrator avoid delegating unclear requirements.
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Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.
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Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.
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Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.
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Infrastructure and development cost estimation for technical projects. Use when planning budgets, evaluating build vs buy decisions, or projecting TCO for architecture choices.
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Identify and challenge implicit assumptions in plans, proposals, and technical decisions. Use when strategic-cto-mentor needs to surface hidden assumptions and wishful thinking before they become costly mistakes.
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Generate phased implementation roadmaps with Epic/Story/Task breakdown, effort estimates, and validation checkpoints. Use when cto-architect needs to create actionable technical roadmaps from architecture designs.
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Generate structured 8-section validation reports with verdict (GOOD/BAD/NEEDS MAJOR WORK), strengths, critical flaws, blindspots, and concrete path forward. Use after strategic-cto-mentor has completed validation analysis and needs to produce final deliverable.
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Recommend technology stacks based on project requirements, team expertise, and constraints. Use when selecting frameworks, languages, databases, and infrastructure for new projects.
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Guidance for scaling systems from startup to enterprise scale. Use when planning for growth, diagnosing bottlenecks, or designing systems that need to handle 10x-1000x current load.
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Use when the user brings any fuzzy, half-formed, or interrupt-driven input — an order from a boss, a forwarded email thread, a ticket, a chat message, a "can you look into X", a raw idea — and it needs framing before work starts. Turns the input into a structured brief (Context · Desired outcome · Goal · Owner · Next step · Decision needed). This is the AIOS's front door; run most things through it first. Trigger on "/structure", "frame this", "what's the actual ask here", "structure this", or any vague task that lacks a defined outcome.
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Use when someone asks for an AIOS audit, asks to score their setup against the Four Cs, or says "is my AIOS working" / "audit my setup" / "find gaps in my AIOS". Produces a Four-Cs scoreboard with top-3 fixes ranked by leverage.
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