Build a Grafana dashboard JSON for a Rust project profiled with hotpath. Writes the dashboard file with context-aware panels (SQL and HTTP time per route, N+1 detection, memory and time per request, channel queues, lock contention, Tokio workers, per-thread CPU and allocations, profiling overhead) limited to what the…
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality…
★not rated 1.9k▲
+15 3d agoASkillSpector: pass100 tokens
originalApache-2.0
One-time onboarding for the executive/manager commitment workflow — delegation-heavy, meeting prep, decision capture, morning and evening digests. Creates a commitments project and installs two dashboard widgets. After successful setup this skill is excluded from selection until the marker file is deleted.
Tune Matplotlib labels on pgbent PG18 charts: scatter point placement, horizontal bar value labels (inside bar with contrast color when they fit), overlap avoidance, and PNG regeneration. Use when fixing crowded scatter or bar labels, pg18-osm-power-.png, pg18-osm-relation-power.png, pg18-osm-relation-scatter.png…
Solve an interactive hidden-rule ARC-AGI-3 game from its game ID. Contains the complete playing doctrine (predict before every action, graded claims, one-page notes, optional executable-rules search) plus the arc harness the agent drives. Use whenever asked to solve, play, or continue an ARC-AGI-3 game.
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get…
Use when asked to run, benchmark, evaluate, or score an LLM with AEON Bench. You run the AEON Bench Pod on the user's machine, point it at a model, run the benchmark, and submit the signed result to the public leaderboard at aeon-bench.com. All work happens on the pod. The mothership only shows the board and accepts…
Create and integrate new modular agents for browseruse-bench (BaseAgent in browserusebench/agents/, registry import in init.py, entry in configs/agentregistry.yaml, runtime config in root config.yaml agents section). Use when a user asks to add/register a new agent, scaffold a custom agent, or implement a new agent…
Operate the belt CLI to evaluate headless coding agents (Claude Code, Cursor, Codex, Gemini, and others) end to end. Use when the user asks to write or run eval scenarios, compare agents, score outputs with rules or LLM judges, register a new agent adapter, interpret reports or benchmark cards, or set up evals in CI.…
Design and create pitlane eval benchmarks that measure whether an AI coding skill or MCP server actually improves assistant performance. Use when the user wants to test a skill, evaluate an MCP server, create a pitlane eval YAML, benchmark an AI assistant, or compare baseline vs challenger configurations. Covers eval…
Plan, explain, build, run, monitor, validate, transfer, and audit reproducible embodied-model studies and batch episode collection. Use when a user wants to connect a local, SSH, or cloud GPU host; understand and compare a policy, VLA, world model, world-action model, or hybrid with a benchmark; discover and reuse…
Maximum-savings variant of eco - the same frugality rules with a tighter reply budget, for routine chores (rename, small fix, quick lookup, boilerplate). Prefer plain eco for hard or high-stakes work. Works in any language.
A local review tool for authorised DOCX files, which are Microsoft Word documents. It checks the document's ZIP and internal structure, applies limited textbook criteria, and exports a review package tied to file hashes.
Persistent multi-agent economy where autonomous AI agents compete for resources, trade on a marketplace, and benchmark decision-making against a standing population of always-on agents. Invite other agents for energy rewards. Auto-registers — no API key needed.
Build a 2D dungeon game where AI agents cooperate, work on businesses, and an orchestrator (human) guides them — Phaser 3 + TypeScript + React + Node backend with LLM agents.
Run a standardized XORCISE agent benchmark — pick a ready-made playbook (or build your own from existing missions), point it at any OpenHands-supported model(s), and get a polished eval-card HTML report of how each model performed across the missions. Warns you about cost before spending anything.
Use when the user asks to lint, audit, test, benchmark, validate, or fix an agent skill — from a single SKILL.md frontmatter check to trigger-accuracy measurement or a corpus-wide audit of installed skills for duplication — driving the shakespii CLI (init, lint --json, test --run, bench) to resolve findings until…
★not rated 2 3mo agoA78 tokens
originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: