Claude Skills by thedixitjain

Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via /cs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't ...

Guides you step-by-step through defining a business metric (aggregation) on a Honeydew entity. Covers SQL expression building and pushes to Honeydew via the MCP tools.

> Query and manage Grafana dashboards and Prometheus metrics for Happy infrastructure. Covers grafanactl CLI usage, direct Prometheus queries through Grafana proxy, and dashboard-as-code workflows. Use when user asks about metrics, dashboards, monitoring, Grafana, Prometheus, or wants to add/modify panels.

Print a debt-ops health summary from the metrics log, covering registration rate, hook feedback action rate, ADR creation, and AI-authored share. Use when the user asks for \"debt-ops metrics\", \"debt health\", \"registry stats\", or invokes $metrics. Read-only, never writes the log.

Create, update, refactor, explain, or review Microsoft Agent Framework solutions using shared guidance plus language-specific references for .NET and Python.

Create agent skills for Microsoft technologies using Learn MCP tools. Use when users want to create a skill that teaches agents about any Microsoft technology, library, framework, or service (Azure, .NET, M365, VS Code, Bicep, etc.). Investigates topics deeply, then generates a hybrid skill storing essential knowledge locally while enabling dynamic deeper investigation.

Use when a repo has a legacy AIBOARDING.md (v1 layout) and should move to the standard AGENTS.md + CLAUDE.md layout with the .aiboarding/state.json sidecar. One-shot, preview-first migration that preserves the existing onboarding content and rewires the hooks.

Migrate supported instruction files, skills, agents, and MCP config into Codex project and global files.

| Unified Minions skill for both deterministic shell jobs and LLM subagent orchestration. Replaces the older `gbrain-jobs` routing intent. Use when: submitting gbrain jobs, shell/background tasks, spawning subagents, checking progress, steering running work, pausing/resuming, parallel fan-out. One durable, observable, steerable queue interface.

Orchestrates full project lifecycle by auto-detecting state and routing to the correct phase. Use when starting or resuming a project mid-workflow.

Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas.

Automate Mistral AI operations -- manage files and libraries, upload documents for fine-tuning, batch processing, and OCR, track fine-tuning jobs, and build RAG pipelines via the Composio MCP integration.

'Implement Mistral AI async patterns, batch API, agents, and event-driven workflows. Use when building async workflows, using the Agents API, batch inference, or handling long-running Mistral AI operations. Trigger with phrases like \"mistral events\", \"mistral async\", \"mistral agents\", \"mistral batch\", \"mistral queue\", \"mistral background jobs\". '

Persistent memory and learning-loop skills for AI coding agents using mnemos MCP tools. Use whenever mnemos_* MCP tools are available and you are doing work worth remembering. Cross-session memory, conventions, corrections, and architectural decisions surface back into context automatically. Triggers on session start (record a goal), session end (record a summary), the user says \"save\", \"remember\", \"record this\", \"we were wrong about\", and on any genuine correction or architectural de...

> Use this skill when performing deterministic mode selection for session-start. Reads Phase A STATE.md recommendations + (future) learnings, sessions, backlog, bootstrap signals and returns {mode, rationale, confidence, alternatives}. Pure-function contract — no side effects, no STATE.md writes. Phase B scaffold (issue #276); full heuristic is follow-up sub-issues.

Use when exploring Honeydew semantic layer, discovering entities/fields, setting up workspace and branch context, or querying data. For creating metrics use metric-creation skill. For creating attributes use attribute-creation skill.

Stream live swarm events using the Monitor tool for real-time observability

AI news tracking skill that monitors 80+ entities across 6 free sources (Reddit, HN, GitHub, HuggingFace, arXiv, X/Twitter). Generates scored daily reports with infographics and message digests. Invoke via /morning-ai.

System health check (MOT) for skills, agents, hooks, and memory

> Anti-over-engineering guardrail that activates when an AI coding agent expands scope, adds abstractions, or changes files the user did not request.

Pay HTTP 402 APIs via Machine Payments Protocol (MPP).

meta_skill (ms) — the skill-search/load

Design and optimize production-grade multi-agent systems with LangGraph, LangChain, and DeepAgents for complex AI workflows.

Simulate a structured peer-review process using multiple specialized agents to validate designs, surface hidden assumptions, and identify failure modes before implementation.

This skill should be used when designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.

This skill should be used when the user asks to \"design multi-agent system\", \"implement supervisor pattern\", \"create swarm architecture\", \"coordinate multiple agents\", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.

Route tasks to specialized AI agents with anti-duplication, quality gates, and 30-minute heartbeat monitoring

Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.

Design n8n AI agents, chains, classifiers, extractors, tool calling, memory, RAG, structured output, and human-review flows.

>- Write a non-conformance report or NCR fast — converts bullet-point inputs into professional objective-evidence language, suggests Critical/Major/Minor severity, recommends disposition, and flags missing required information. Use when writing an NCR quickly or when informal defect observations need to become formal quality records.

>- Knowledge and governance closeout: reconcile project docs, rule files (CLAUDE.md/AGENTS.md), authorized agent memory, and workspace residue with what the code and runtime actually do, so the next session or the next person starts from one current answer. Trigger when the user names \"neat-freak\", \"洁癖\", or \"/neat\" — and also on clear knowledge-closeout intent without the name: syncing or tidying project docs/rules/memory after development (\"把文档和记忆整理一下\", \"收尾时把文档同步掉\", \"docs 和代码对不上了\...

One API and one credential for frontier and open-source LLMs, built into your Neon branch and powered by Databricks. Use when a user wants to call an LLM, add AI/chat/an agent to their app, route between model providers (OpenAI, Anthropic, Google/Gemini, Meta, Alibaba, DeepSeek), or...

Spawn nested sub-agents (agents that spawn sub-agents, up to depth=5) via Claude Code's native Task tool — for context-managed deep delegation

Use when auditing a NeurIPS main-track submission for current-cycle CFP, OpenReview, formatting, anonymity, track, contribution-type, checklist, code/data, dual-submission, and LLM/agent policy compliance.

Runs repeatable AI work as checked, budgeted workflow files. The agent captures a repeated task as a .nika.yaml DAG, audits cost/permits/schema before a single token is spent, and runs it with tamper-evident traces.

No Polling for Background Agents

Use when publishing this package to npm — a version release (npm publish), verifying the registry/pi.dev listing, or diagnosing npm auth failures (E403 2FA/token errors). Token-based flow via NPM_TOKEN in .env.local with a temp userconfig, leakage-gate greps before every publish, post-publish verification and marker/badge upkeep. Trigger on \"publish to npm\", \"npm release\", \"E403 publish error\".

Use NTM as an optional pane adapter for

Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation

Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation

Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement

5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance

All /nw-* commands — what they do, when to use them, which agent they invoke. For the buddy agent to help users pick the right command.

How the nWave buddy agent reads a project to answer questions — detection, order of inspection, and citation discipline.

Wave methodology knowledge for the buddy agent — what each wave does, its inputs and outputs, and how to route questions.

DELIVER wave orchestration workflow -- 9 phases from baseline to finalization. Load when user invokes *deliver command. Covers state tracking, smart skip logic, retry, resume, and quality gate enforcement.

Acceptance test creation methodology for the DISTILL wave. Domain knowledge for the acceptance designer agent: port-to-port principle, prior wave reading, wave-decision reconciliation, graceful degradation, and document back-propagation.

Dispatches one unit of DELIVER work to a specialized agent for TDD execution. Runs a single roadmap.json step through the TDD cycle.

Creates new specialized agents using the 5-phase workflow (ANALYZE > DESIGN > CREATE > VALIDATE > REFINE). Use when building a new AI agent or validating an existing agent specification.

Agent safety boundaries - input validation, output filtering, scope constraints, and document creation policy