Claude Skills by thedixitjain

Create Agent Users in Microsoft Entra ID from Agent Identities, enabling AI agents to act as digital workers with user identity capabilities in Microsoft 365 and Azure environments.

Design and generate .envelope.json AI agent team definitions — the open standard for multi-agent teams with hierarchy, access policies, human-in-the-loop gates, and cron schedules.

Use when working with error debugging multi agent review

EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deploye...

Claude Codeセッションの正式な評価フレームワークで、評価駆動開発（EDD）の原則を実装します

평가 주도 개발(EDD) 원칙을 구현하는 Claude Code 세션용 공식 평가 프레임워크

Eval-driven development (EDD) ilkelerini uygulayan Claude Code oturumları için formal değerlendirme çerçevesi

克劳德代码会话的正式评估框架，实施评估驱动开发（EDD）原则

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Framework formal de evaluación para sesiones de Claude Code que implementa principios de desarrollo orientado a evals (EDD)

> 正式评估框架，实现 eval-driven development (EDD) 原则。 用于定义 pass/fail 标准、测量 pass@k 指标、创建回归测试套件。

Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

> Use this skill to run an honest session-process evaluation (Standard v1, aiat-llm-eval/1.0) — score the last completed orchestrator session against the pre-registered rubric-v1 dimensions, run /eval, evaluate this session, produce an eval report, or re-verify a stored eval run for reproducibility. Deterministic-first with an optional advisory LLM judge; never produces a global score.

lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).

Expert in event sourcing, CQRS, and event-driven architecture patterns. Masters event store design, projection building, saga orchestration, and eventual consistency patterns. Use PROACTIVELY for event-sourced systems, audit trail requirements, or complex domain modeling with temporal queries.

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

> 统一进化资产模型与自进化闭环核心。定义 Gene（策略 DNA）、Capsule（已验证经验快照）、 EvolutionEvent（追加式事件日志）三层资产模型，实现 Detect→Select→Replay→Validate→Solidify→Reuse 闭环，使 agent 遇到已知问题时先复用已验证经验，跳过重新试错。

Cross-agent gene sharing protocol for exchanging validated evolution patterns between Claude sessions with trust downgrades and local revalidation.

> Use this skill when extracting session patterns into reusable learnings. Three modes: analyze (extract from session history), review (edit/manage existing learnings), list (display active learnings). Manages .orchestrator/metrics/learnings.jsonl.

Ring 3 evolution engine. Analyzes session observations, generates and improves evolved skills, shows metrics dashboard. Subcommands: status, history, rollback, reset. Use for post-session review and skill improvement.

Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.

This skill should be used when the user asks to \"demonstrate skills\", \"show skill format\", \"create a skill template\", or discusses skill development patterns. Provides a reference template for creating Claude Code plugin skills.

Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap. Covers theory, numericals, MCQs, coding, and lab prep, ordered Easy → Medium → Hard. Use for last-minute revision, important topics, and question prediction.

/cs:execute <decision> — Generate a 90-day execution plan with weekly milestones, DRIs, and check-in cadence from an approved decision. Use when a logged decision needs to become an operating plan — e.g. turning an approved market-entry call into weekly milestones with DRIs.

../../../c-level-advisor/c-level-agents/skills/execute/SKILL.md

Fetch the full reasoning behind an AxonFlow policy decision — matched policies, risk level, override availability, recent hit count

Exploit Kerberos Constrained Delegation misconfigurations in Active Directory to impersonate privileged users via S4U2self and S4U2proxy extensions for lateral movement and privilege escalation.

Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples. Use when the user runs /si:extract or asks to package a recurring solution from memory into a skill.

> Scans a Python recipe to find every place an environment variable is accessed — including `os.environ.setdefault(\"V\", \"d\")`, whose \"d\" would otherwise be a hidden default a user editing .env.example has no way to discover — then ensures all variables are declared in `.env.example`, that `load_dotenv()` is bootstrapped in the package `__init__.py`, and that `python-dotenv>=1.0.0` is listed in `pyproject.toml`. When a new entry is added to `.env.example`, the extracted default from sour...

../../../engineering-team/self-improving-agent/skills/extract/SKILL.md

Automate Extracta AI tasks via Rube MCP (Composio). Always search tools first for current schemas.

Extract embedded configuration from Agent Tesla RAT samples including SMTP/FTP/Telegram exfiltration credentials, keylogger settings, and C2 endpoints using .NET decompilation and memory analysis.

How to prompt Claude Fable 5 so it ORCHESTRATES a build correctly and cheaply — Fable as the architect/advisor that plans and delegates while Opus 4.8 does the token-heavy execution. Covers the two wiring patterns (advisor mode + architect-and-delegate), effort-level routing as the cost lever, parallel subagents, verification loops, and the failure modes that silently reroute Fable to Opus or tank its output. Use whenever setting up or prompting a Fable-driven pipeline (e.g. the Damon / agent...

Guided interview to Gold Code (100% AI-Readiness). Use when helping users improve their .faf file through questions. Leverages Claude Code's AskUserQuestion for seamless integration. Just type /faf-go and answer questions till done.

Use when adding, changing, deploying, testing, or debugging FastMCP MCP servers in existing Django apps, including ASGI mounting, stdio or sidecar servers, Django ORM access from MCP tools, auth and permissions, Streamable HTTP deployment, and MCP client tests.

Implementation Reviewer

Plan Reviewer

Creates a complete product feature specification with acceptance criteria, scope, dependencies, and risks. Delegates to the Prometeo (PM) agent.

Show federation health — peers, sessions, trust levels, and message metrics. Use when the user asks \"is federation healthy?\", \"show peers\", \"federation status\", or wants to inspect cross-installation agent connectivity.

This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.

Use for file-based context management, dynamic context discovery, and reducing context window bloat. Offload context to files for just-in-time loading.

Use when the user needs to filter data — whether in a structured query, a metric aggregation, or an attribute expression. Covers filter syntax, date handling, and best practices.

Finalize prompt file using the role of an AI agent to polish the prompt for the end user.

Use when an owner asks to find a cofounder or project partner. Assess only that agent's own owner and rank only approved profiles other agents posted for their own owners.

Helps users discover and install agent skills when they ask questions like \"how do I do X\", \"find a skill for X\", \"is there a skill that can...\", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

'Execute firebase platform expert with Vertex AI Gemini integration for Authentication, Firestore, Storage, Functions, Hosting, and AI-powered features. Use when asked to \"setup firebase\", \"deploy to firebase\", or \"integrate vertex ai with firebase\". Trigger with relevant phrases based on skill purpose. '

Guide a new user through their first computer-use agent run (env check, safe browser-only task, then open the trajectory viewer).

Deploy a Velociraptor server and agents and write VQL hunts across a fleet.