Results for “e-e-a-t”

Automate Buildkite CI/CD operations through Composio's toolkit via Rube MCP, including pipeline management and job execution.

Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.

Researches conversation and code context to generate 1-6 targeted clarifying questions when a prompt is vague, then executes the original request.

Guides the creation of high-quality MCP servers, covering design, implementation, testing, and evaluation for Python and TypeScript.

Entry-point router for the local Pump.fun skill suite. Use when the user asks about launching Pump.fun tokens, bonding-curve or AMM trading, quotes, fees, claims, incentives, SDK work, agent/MCP integration, security, testing, or release workflows.

Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

`analysis-agent`/`task-agent`/`review-agent`: use for input authority, parsing, canonicalization, bounds, state checks, writable fields, rejection, or external-response changes.

Via modeling: pads, antipads, ground return vias, GRV placement, and signal integrity for high-speed layer transitions. TRIGGER: user asks to model a via, design a via transition, place ground return vias, analyze via performance, or check signal integrity through layer transitions. Invoke BEFORE writing code — only viaSingleEnded exists (no viaDifferential), and the location format is non-obvious. SKIP: general signal integrity without vias (use matlab-analyze-em), transmission line design (use matlab-design-pcb-transmission-line), PDN analysis (use matlab-analyze-pcb-pdn), material/stackup setup only (use matlab-manage-pcb-material).

Drive Soup (`soup-cli`), a CLI-first tool for fine-tuning and post-training LLMs with one YAML config and one command — SFT, DPO/GRPO/ORPO/SimPO/KTO, QLoRA/DoRA/LoRA+, layer streaming for 4-8 GB GPUs, eval-gated training, and serving. Use when the user wants to `soup init`/`soup train` a model, pick a training method or quantization scheme, estimate cost/memory before training, fine-tune on a small local GPU, migrate a config from Axolotl/LLaMA-Factory/Unsloth, or serve/merge/push a trained adapter. Triggers on: "soup-cli", "soup train", "soup init", "fine-tune an LLM locally", "QLoRA on a laptop GPU", "layer streaming", "soup advise", "soup autopilot", "DPO/GRPO/ORPO training", "merge LoRA adapter".

Kling 3.0 video generation on RunComfy. Kling 3.0 (also called Kling V3.0) is Kuaishou Technology's third-generation multi-shot video model with native synchronized audio and consistent character identity across shots. This skill covers all six Kling 3.0 endpoints, spanning three rendering tiers (Standard, Pro, 4K) and two modes (text-to-video, image-to-video). Calls runcomfy run kling/kling-3.0/<tier>/<mode> through the local RunComfy CLI. Triggers on "kling", "kling 3.0", "kling v3", "kling pro", "kling 4k", "kling text to video", "kling image to video", or any explicit ask to generate or animate with Kling 3.0.

Runs an evidence-based investigation of a bug, failure, or unexpected behavior with investigate and publishes the resulting investigation report to a user-specified Confluence location. Use when the user wants something debugged, diagnosed, or root-caused AND the findings posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not investigate to a local file only — use investigate. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not document an already-understood feature to Confluence — use project-documentation-to-confluence. Does not plan or specify a new feature to Confluence — use plan-a-feature-to-confluence. Does not publish to Jira — use work-items-to-jira.

Trains, evaluates, and runs inference for Mask Auto-Label (MAL) weakly-supervised segmentation models using ViT-MAE backbones with minimal point or box annotations.

Compact the current session into a single detailed handoff message that can be pasted into a fresh agent run. Use when switching context, ending a session, or avoiding context-window loss.

Build or refactor CLI tools designed for AI agent consumption: non-interactive, flag-driven, idempotent, with --json output and --dry-run preview. Use when creating a new script the agent will call, adding agent-friendly flags to an existing tool, or debugging why an agent keeps failing to use your CLI.

OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.

Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.

Pay-per-use access to image generation, real-time X data, and multiple LLMs via a crypto wallet, with optional budget tracking.

Chunks a book into canonical retrieval units with heading-aware structure splitting, recursive token targets, and contextual prefixes for downstream RAG ingestion.

Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (s...

Design and ship production-ready MCP servers from OpenAPI contracts instead of hand-written tool wrappers. Supports Python and TypeScript, schema validation, and safe evolution.

Runs a structured multi-agent deliberation: four specialist agents write independent position papers, two cross-examiners debate them, and the orchestrator synthesizes a final recommendation with implementation plan and risk register.

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

Extend and upgrade Hermes Agent's context compression system — StagedArchiver, knowledge fingerprinting, /uncompress command, look-ahead triggers, and schema migration patterns.

Apply an approved sub-skill grouping by moving user-specified skills into a parent bundle, with timestamped backups of every modified directory.

Navigate the PhysicsNeMo repository by discovering model families, datapipes, and examples through live file search, without writing training code.

autonomous architecture design and refinement for mermate using iterative copilot guidance, local reasoning, repeated low-cost render validation, and final max-quality render selection. use when building, stress-testing, refining, decomposing, validating, or evolving system architectures from simple ideas, complex problem statements, markdown specifications, mermaid drafts, or ambiguous design notes. especially useful when chatgpt should act like a professional architect that thinks step by step, uses mermate repeatedly, compares intermediate diagrams, and decides when to continue refining versus when to finalize with max mode.

This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Enforces research-backed best practices for minimal, high-signal agent documentation.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

Update Han plugin documentation so every skill, agent, guidance doc, index, and cross-reference is current and accurate. On a non-default branch, scopes the pass to entities the branch actually touched. On the default branch, performs a full documentation sweep across the whole plugin. Use when updating, refreshing, syncing, auditing, or verifying Han's docs after changing skills, agents, references, or top-level guidance — including "update the docs", "doc sweep", "refresh documentation", "audit the docs", "make sure the docs are current". This is a repository-maintenance skill for the Han repo itself, not a general documentation skill — use /project-documentation to document features in arbitrary projects, /han-release to cut a release (and update CHANGELOG), and /update-pr-description for PR bodies.

Build type-safe AI agents and graph-based workflows with PydanticAI and PydanticGraph. Agent creation, function tools, capabilities, dependency injection, structured output, streaming, multi-agent patterns, testing, evals, and graph state machines. Use whenever you are building agents, tool-using LLM workflows, or graph-based state machines in Python.

Find out why a coding-agent skill won't fire — grade each SKILL.md A–F on activation, simulate which skill a prompt triggers, and flag collisions where one silently shadows another.

Examine what's actually stored in the index for specific items

Standards for creating new High-Density Agent Skills with optimal token economy.

Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or