100+ agent skills

Multi-bot, multi-chat routing. Forward, mute, and manage grouped DMs. The reference Telegram skill for power users.

Read-only by default. Drafts replies, surfaces important threads, ignores newsletters. Understands priority from context.

Watches PRs across your repos, posts review summaries, drafts changelogs, and flags risky migrations.

Drops a summary comment on every PR, flags risky migrations, and suggests one concrete test to add.

Summarises thread noise, surfaces urgent mentions, and drafts channel updates. Works across multiple workspaces.

Move tickets across columns by chatting. Adds time tracking, auto-links commits to issues, sends standup summaries.

Query, update and chart your spreadsheets by chat. Paste in CSV and ask questions — no formulas needed.

Fetch any URL and get clean Markdown. Handles JS-rendered pages, paywalls, and infinite scroll.

Moderate messages, greet new members, summarise hot threads, and schedule announcements across servers.

Two-way sync. Write meeting notes, read project docs, search across all databases in one query.

Create issues, manage sprint boards, and generate velocity reports through conversational commands.

Parses incoming invoices, reconciles charges, alerts on failed payments, and generates revenue summaries.

Adds, completes, and re-prioritises tasks. Weekly review prompts and inbox zero flows baked in.

Run read-only SQL queries against your Postgres database using plain English. Returns formatted tables.

Log calls, update deal stages, enrich contacts from LinkedIn, and draft follow-up emails — all by chat.

Trigger deployments, check CloudWatch logs, manage EC2 instances, and get cost alerts from a chat interface.

Trigger any Zapier zap from chat. Connect thousands of apps without writing integration code.

Read-only secrets retrieval. Server-side decryption — credentials are never sent to the LLM in plaintext.

Read and write Airtable bases by chat. Great for ops teams who live in Airtable and want AI-driven updates.

Auto-prep notes for every meeting, batch-cancel low-priority blocks, suggest reschedules when conflicts arise.

Transcribes and summarises calls via a Twilio number. Files summaries under the right contact automatically.

Draft support replies, tag conversations, escalate VIPs, and summarise open ticket backlog.

Query orders, process refunds, update inventory, and draft customer reply emails for your Shopify store.

Draft, schedule, and analyse tweets. Thread composer with optimal timing recommendations built in.

Get error digests, assign issues to teammates, and ask why something spiked in plain English.

Daily traffic summaries, anomaly alerts, and natural-language queries over your GA4 property data.

Query deals, log activities, update opportunity stages, and get weekly pipeline health reports.

Extract design tokens, comment on frames, and generate component documentation from your Figma files.

Draft and schedule LinkedIn posts, monitor engagement, and repurpose long-form content into short posts.

Query user events, build funnel reports, and get retention cohort summaries through plain-English questions.

Create and update Webflow CMS items by chat. Great for content teams who want to publish fast.

Parse Typeform responses, route to the right team, and generate summaries of survey results.

Monitor subreddits for mentions, trends, and competitor activity. Get daily digests or real-time alerts.

CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.

Use when writing DALI data loading or preprocessing code with `nvidia.dali.experimental.dynamic` (ndd), or when converting DALI pipeline-mode code to dynamic mode, or when the user asks about DALI dynamic mode, imperative DALI, or ndd. Use this skill any time someone mentions 'ndd', 'dynamic mode', or wants to load/augment data with DALI outside of a pipeline definition.

Guide for adding support for new LLM or VLM models in Megatron-Bridge. Covers bridge, provider, recipe, tests, docs, and examples.

Dev environment setup for Megatron Bridge — container-based development, uv package management, lockfile regeneration, adding dependencies, Slurm container usage, and common build pitfalls.

Bump a pinned dependency (TransformerEngine, Megatron-LM, NRX, etc.), regenerate the lockfile, open a PR, and drive it to green by attaching a watchdog to the "CICD NeMo" workflow and quarantining failing functional tests as flaky until the run is green.

CI/CD reference for Megatron Bridge — pipeline structure, commit and PR workflow, CI failure investigation, and common failure patterns.

Code style and quality rules for Megatron Bridge — ruff configuration, naming conventions, type hints, mypy rules, docstrings, copyright headers, logging, and the code review checklist.

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.

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Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.

External NeMo-RL end-to-end validation workflow for Megatron-Bridge model/provider changes, including downstream compatibility checks, external RL lifecycle behavior, Megatron policy setup, HF import/export, checkpoint/resume, non-colocated vLLM refit, delta weight transfer, optional LoRA/generation variants, and questions such as "does this model work in NeMo-RL", "run NeMo-RL e2e", or "external RL loop validation". Covers running NeMo-RL Megatron policy jobs from a Bridge checkout, choosing GR

Structured framework for verifying numerical parity of HF<->MCore weight conversions. References existing tools and the add-model-support skill.

Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute.

Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.

Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.

Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlap_moe_expert_parallel_comm, delay_wgrad_compute, and flex dispatcher backends such as DeepEP and HybridEP.

Operational guide for enabling hierarchical context parallelism in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Techniques for reducing peak GPU memory in Megatron Bridge — expandable segments, parallelism resizing, activation recompute, CPU offloading constraints, and common OOM fixes.

MoE expert-parallel communication overlap in Megatron Bridge. Covers dispatch/combine overlap, flex dispatcher backends, and expert wgrad scheduling.

Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.

Representative MoE training playbooks by hardware platform and model family. Summarizes rounded throughput bands, parallelism patterns, and common tuning stacks.

Long-context MoE training guidance for Megatron Bridge. Covers CP sizing, selective recompute, dispatcher choices, and practical patterns from DSV3, Qwen3, and Qwen3-Next long-context experiments.

Systematic workflow for MoE training optimization in Megatron Bridge, based on the Megatron-Core MoE paper. Covers the Three Walls framework, parallel folding, recompute strategy, dispatcher choice, and CUDA-graph bring-up.

Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.

Operational guide for choosing and combining parallelism strategies in Megatron Bridge, including sizing rules, hardware topology mapping, and combined parallelism configuration.

Validate and use packed sequences and long-context training in Megatron-Bridge, distinguishing offline packed SFT for LLMs from in-batch packing for VLMs, and applying the right CP constraints.

Operational guide for enabling TP, DP, and PP communication overlap in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Recommend and customize Megatron Bridge recipes for a user's model, GPU count, and training goal. Indexes library recipes (pretrain/SFT/PEFT) and performance recipes.

Resiliency features in Megatron Bridge including fault tolerance, straggler detection, in-process restart, preemption, and re-run state machine.

Testing reference for Megatron Bridge — unit and functional test layout, tier semantics (L0/L1/L2/flaky), script conventions, running tests locally, adding/moving/disabling tests, and pytest conventions.

External verl end-to-end validation workflow for Megatron-Bridge model/provider changes. Covers running a small verl Megatron backend job from a Bridge checkout, choosing LoRA/DDP plus optional save/resume and parallelism variants, setting PYTHONPATH so verl imports the local Bridge tree, and reporting pass/fail evidence.

Create a six-frame storyboard that shows a user's journey from problem to solution. Use when you need a fast narrative for alignment, concept reviews, or demos.

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Bump the NVIDIA PyTorch base image (`nvcr.io/nvidia/pytorch:<YY.MM>-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker/.ngc_version.dev` and GitLab CI in `.gitlab/stages/01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs #4611 and #4688.

Chat-based AWS infrastructure assistance using AWS CLI and console context. Use for querying, auditing, and monitoring AWS resources (EC2, S3, IAM, Lambda, ECS/EKS, RDS, CloudWatch, billing, etc.), and for proposing safe changes with explicit confirmation before any write/destructive action.

Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.

When the user wants to generate, iterate, or scale ad creative — headlines, descriptions, primary text, or full ad variations — for any paid advertising platform. Also use when the user mentions 'ad copy variations,' 'ad creative,' 'generate headlines,' 'RSA headlines,' 'bulk ad copy,' 'ad iterations,' 'creative testing,' 'ad performance optimization,' 'write me some ads,' 'Facebook ad copy,' 'Google ad headlines,' 'LinkedIn ad text,' or 'I need more ad variations.' Use this whenever someone nee

Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.

Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.

Research and draft a response to a GitHub issue or question from an external contributor.

How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.

Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.

Obsidian-first SDD workflow for complex work, feature tickets, task state, and multi-session handoffs under `canon/<project>/...`. Use this skill to drive one active canon task note per session and keep feature/task history in Obsidian instead of local scratch files.

Refresh golden values from a GitHub Actions workflow run (failing-only or all jobs), score the change with average normalized relative differences, and produce a PR-ready summary. Use when the user asks to update goldens for a CI run, refresh golden values from a workflow ID, or generate a golden-value diff summary for a PR description.

When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the user mentions 'PPC,' 'paid media,' 'ROAS,' 'CPA,' 'ad campaign,' 'retargeting,' 'audience targeting,' 'Google Ads,' 'Facebook ads,' 'LinkedIn ads,' 'ad budget,' 'cost per click,' 'ad spend,' or 'should I run ads.' Use this for campaign strategy, audience targeting, bidding, and optimization. For bulk ad creative generation and iteration,

Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.

Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).

Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "accuracy drop", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq) or deploying/serving models (use deployment).

Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher. Covers running evaluations, checking status and live progress, debugging failed runs, exporting artifacts and logs, and analyzing results. ALWAYS triggers on mentions of running evaluations, checking progress, debugging failed evals, analyzing or analysing runs or results, run directories or artifact paths on clusters, Slurm job issues, invocation IDs, or inspecting logs (client logs, server logs, SSH to cluster, tail

Monitor submitted jobs (PTQ, evaluation, deployment) on SLURM clusters. Use when the user asks "check job status", "is my job done", "monitor my evaluation", "what's the status of the PTQ", "check on job <slurm_job_id>", or after any skill submits a long-running job. Also triggers on "nel status", "squeue", or any request to check progress of a previously submitted job.

This skill should be used when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace or TensorRT-LLM checkpoint from a pretrained model using ModelOpt.

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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.

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Expert email management assistant for Apple Mail. Use this when the user mentions inbox management, email organization, email triage, inbox zero, organizing emails, managing mail folders, email productivity, checking emails, or email workflow optimization. Provides intelligent workflows and best practices for efficient email handling.

5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates

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This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

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