executive-mentor
πPluginaneeba-pixel/claude-skills
Installation
/plugin marketplace add alirezarezvani/claude-skills/plugin install executive-mentor@claude-code-skillsAdversarial thinking partner for founders and executives. Stress-tests plans, prepares for board meetings, navigates hard calls, runs postmortems. 5 sub-skills with slash commands.
Part of
aneeba-pixel/claude-skills
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Research orchestrator (hybrid router + fallback). Deterministic SIGNALS classification routes to 6 specialists (pulse/litreview/grants/dossier/patent/syllabus) at >=2 signals, else runs own 8-step plan-decompose-search-synthesize-cite fallback. Routing transparency mandatory. Path-B from megaprompt 13.
Single-file HTML landing-page generator with 4 design styles, brand palette validation, GSAP animation patterns, kebab-slug URL hygiene. Path-B from megaprompt 04.
Video content strategy: video scripts, YouTube channel optimization and SEO, short-form video pipelines (Reels, TikTok, Shorts), and repurposing long-form content into video. NOT for written blog content or caption-only social posts (separate skills).
Relentless plan-and-design interrogator. Walks the decision tree one branch at a time, asking forcing questions sequentially with recommended answers. Explores codebase before asking. Derived from Matt Pocock's MIT-licensed grill-me with: (1) 3 stdlib Python tools (decision-tree extractor across 6 branch kinds, question generator with dependency-aware ordering, JSON-backed session tracker for multi-day grills), (2) 3 references citing 7-8 sources (6 forcing-question patterns, when to stop grilling, companion tooling), (3) cs-grill-master persona agent + /cs:grill-me slash command. Matt's relentless one-at-a-time interview discipline preserved verbatim per MIT.
32 engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright (9 sub-skills), self-improving agent, Stripe integration, TDD guide, tech stack evaluator, Google Workspace CLI, a11y audit (WCAG 2.2), Azure cloud architect, GCP cloud architect, security pen testing, Snowflake development, adversarial-reviewer, ai-security, cloud-security, incident-response, red-team, threat-detection. v2.8.1 audits senior-fullstack / senior-frontend / senior-backend against karpathy-coder + Matt Pocock β each ships a 7-question forcing-question library, 4 customization profiles (JSON), deterministic decision engine, composition map into POWERFUL specialists, plus cs-fullstack-engineer / cs-frontend-engineer / cs-backend-engineer orchestrator agents (context: fork) + /cs:fullstack-review, /cs:frontend-review, /cs:backend-review, /cs:engineer-grill slash commands.
A second brain for Claude Code + Obsidian inspired by Karpathy's LLM Wiki gist. Turn any LLM CLI into a disciplined wiki maintainer: incrementally ingest sources into a persistent, interlinked markdown vault; update entity/concept/source pages; flag contradictions; maintain index and append-only log. Knowledge compounds instead of being re-derived by RAG on every query. Ships 3 sub-agents (wiki-ingestor, wiki-librarian, wiki-linter), 5 slash commands (/wiki-init, /wiki-ingest, /wiki-query, /wiki-lint, /wiki-log), 8 Python tools (stdlib only: init_vault, ingest_source, update_index, append_log, wiki_search BM25, lint_wiki, graph_analyzer, export_marp), 8 reference docs, full vault templates (CLAUDE.md, AGENTS.md, cursorrules, 5 page templates), and a worked example vault. Cross-tool compatible with Claude Code, Codex CLI, Cursor, Antigravity, OpenCode, and Gemini CLI.
Google NotebookLM browser-automation skill. 4 actions (read/extract, add-source, Studio outputs, create notebook). Screenshot-first + find-before-click + fire-and-notify async discipline. Path-B from megaprompt 03.
Terraform infrastructure-as-code β module design patterns, state management, provider configuration, CI/CD integration, and multi-environment strategies.
Course supplementary-reading skill. Topic-grouper + bundled Node.js DOCX generator for syllabus-anchored reading lists. Research-pack convention. Path-B from megaprompt 10.
Cut LLM API spend via model routing, prompt caching, prompt compression, and per-feature cost observability. Use when AI costs are too high, choosing between models, or launching an AI feature without cost architecture. NOT for RAG design or prompt quality (separate skills).