Claude Skills by thedixitjain

Create or edit a Claude Code skill

Create a new Agent Skill from scratch with guided workflow

/cs:write-a-skill <name-or-description> — Author a new agent skill with Matt Pocock's 3-phase workflow (Gather → Draft → Review). Runs 6. Slash command for Claude Code, Codex CLI, Gemini CLI.

Improve and optimize an existing Agent Skill

/cs:write-a-skill <name-or-description> — Author a new agent skill with Matt Pocock's 3-phase workflow (Gather → Draft → Review). Runs 6 review-checklist items + 3 validator tools as a gate. Use when starting a new skill in this repo.

Analyze skill file complexity metrics and generate modularization recommendations for splitting or progressive loading.

Harden skills against rationalization and bypass behaviors

Save a custom approach as a reusable skill

One-command skill creation and packaging for a target platform

Create an AI skill from any source (URL, repo, PDF, video, notebook, etc.)

Test and evaluate Agent Skill performance with benchmarks

>- Interactive 8D coach for running a live G8D or TOPS-8D investigation step by step, validating root cause depth, rejecting weak containment, and checking D7 systemic prevention. Use when running an 8D investigation live, reviewing a draft 8D before customer submission, or training a team on the 8 Disciplines methodology.

Use when designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.

Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

Switch a caller-selected coding-agent

Run the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.

Generate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.

Add a new team member. Describe what you need and ATeam infers the config.

Register a deferred decision in the debt registry. Trigger by judgment, not a marker scan, whenever a future reader would ask \"why this way?\": an unmade decision, stub, loosened type, bypassed check, swallowed error, a default picked \"for now\", or a TODO/FIXME/HACK/XXX marker. Trigger immediately whenever you defer work, or when the user invokes $add. Over-register freely; the developer drops with \"drop A\", \"drop A,C\", or \"drop all\".

| Scaffold production-ready AI agents on Google's Agent Development Kit (ADK): ReAct-style single agents, multi-agent orchestration (Sequential/Parallel/Loop), tool wiring, evaluation, and optional Vertex AI Agent Engine deployment. Use when building, scaffolding, or deploying ADK agents on Google Cloud, or when wiring ADK tools and orchestration patterns. Trigger with \"build an ADK agent\", \"scaffold an agent on ADK\", or \"deploy to Agent Engine\".

'Execute software engineer specializing in creating production-ready ADK agents with best practices, code structure, testing, and deployment automation. Use when asked to \"build ADK agent\", \"create agent code\", or \"engineer ADK application\". Trigger with relevant phrases based on skill purpose. '

> ADK 框架适配层。为 LangChain / EINO / AutoGen / AgentScope / CrewAI 提供框架特定的 代码模板、惯用模式、API 映射和项目结构，供 agent-dev-workshop Phase 5 代码生成使用。 每个框架 reference 文件标注 verified_date 用于版本锁定。

Add docflow to a repo that already has docs, without rewriting them. Adds missing config, folders, helpers, agent guidance, and an adoption review. Use when docs already exist or doctor recommends adopt.

Run a design-challenging Claude Code review of local git changes in this repository. Args: --wait, --background, --base <ref>, --scope <auto|working-tree|branch>, --model <model>, --effort <low|medium|high|xhigh|max>, [focus text]. Defaults to opus + xhigh effort. Use only when the user wants stronger scrutiny than a normal review, such as explicit tradeoff challenge, risky-change review, or custom focus text.

| 敵対的分析手法を統合したレビューの entry skill。認知バイアス対策の3手法 （Pre-mortem / War Game / Logic Torturing）と、宣言・主張と実態の乖離を突く claim-vs-actual 検出3パターン（Self-Contradiction / Refactor-Claim Audit / Cross-File Leakage）へルーティングし、通常のレビューでは見えない設計の盲点・ 防御の穴・論理の弱点・宣言と実装のズレを可視化する。

>- Use when a task is too large for one model pass, needs parallel research or generation across many subtasks (like researching a dozen competitors at once), or the user asks to orchestrate multiple models, split work across a model team, run an advisor-worker loop, have a stronger model review the plan while cheap workers execute, or says \"too big for one model\" or \"fan this out\". Not for single-file edits or tasks one model handles in one pass.

Use when the quantitative model is the bottleneck for an American Economic Journal: Macroeconomics (AEJ: Macro) manuscript — DSGE, New Keynesian, heterogeneous-agent (HANK / Aiyagari-Bewley), or structural estimation — and calibration, parameter identification, solution accuracy, or counterfactual validity need discipline. For empirical shock identification see aejmac-identification.

| Notion-as-source-of-truth dispatch board for running your work like an AI agency. One Tasks database is the source of truth; tasks flow Suggestion through Discussion, To-Do, In Progress, and Done with subtasks, recurring cadences, dependencies, and template subtrees. Batch execution fans approved To-Do rows out to parallel agents with per-task model selection. Use when capturing chat to Notion, running the To-Do queue, suggesting, approving, or discussing tasks, or coordinating multi-task b...

Agent skill for adaptive-coordinator - invoke with $agent-adaptive-coordinator

Agent skill for agent - invoke with $agent-agent

Agent skill for agentic-payments - invoke with $agent-agentic-payments

Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.

Agent skill for analyze-code-quality - invoke with $agent-analyze-code-quality

Agent skill for app-store - invoke with $agent-app-store

Agent skill for arch-system-design - invoke with $agent-arch-system-design

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature.

エージェントおよび LLM アプリケーション向けのフルスタック診断。12 層のエージェントスタックにおけるラッパーリグレッション、メモリ汚染、ツール規律の失敗、隠れた修復ループ、レンダリング破損を監査します。重要度順の発見事項とコードファーストの修正を生成します。エージェントアプリケーション、自律ループ、または LLM を活用した機能を構築する開発者に必須です。

Agent skill for architecture - invoke with $agent-architecture

Agent skill for authentication - invoke with $agent-authentication

Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent

Agent skill for base-template-generator - invoke with $agent-base-template-generator

Agent skill for benchmark-suite - invoke with $agent-benchmark-suite

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Browser automation CLI for AI agents. Use this when asked to test something in a real browser.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Agent skill for byzantine-coordinator - invoke with $agent-byzantine-coordinator

Agent skill for challenges - invoke with $agent-challenges

Agent skill for code-analyzer - invoke with $agent-code-analyzer

Agent skill for code-goal-planner - invoke with $agent-code-goal-planner