Alireza Rezvani

Keep README files current with project changes. Use when project structure changes, features added, or setup instructions modified. Suggests README updates based on code changes. Triggers on significant project changes, new features, dependency changes.

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Automatically suggest tests for new functions and components. Use when new code is written, functions added, or user mentions testing. Creates test scaffolding with Jest, Vitest, Pytest patterns. Triggers on new functions, components, test requests, testing mentions.

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Check dependencies for known vulnerabilities using npm audit, pip-audit, etc. Use when package.json or requirements.txt changes, or before deployments. Alerts on vulnerable dependencies. Triggers on dependency file changes, deployment prep, security mentions.

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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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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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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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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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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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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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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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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 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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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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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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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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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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Use when the user brings an external source worth keeping — a URL, a paper, a tweet/thread, a blog post, a docs page — and wants it pulled into the second brain. Fetches the source into the git-ignored capture inbox, admits it to the committed wiki through the admission policy, then refreshes the knowledge graph so it joins the rest of your thinking. Trigger on "/graph-ingest", "ingest this url", "add this paper to my knowledge", "add this tweet/page to my knowledge", "capture this source".

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Use to run the second-brain loop — turn captures in raw/ into clean, cross-linked knowledge in wiki/, and to answer questions from past thinking. Trigger on "/wiki", "process my raw notes", "update the wiki", "add this to my brain/wiki", "what did I think about X", "what do my notes say about X". Follows references/sops/wiki-translate.md. Enforces the admission policy: no PHI, no live financials, no secrets in the committed wiki.

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