analyze-task
Check OSWorld tasks. Validate the evaluation function, verify that the instruction is feasible given the task setup and…
Analyze OSWorld-V2 agent trajectory logs and task results to produce actionable insights. Use this skill whenever the user wants to understand agent performance on OSWorld tasks — including analyzing trajectories, reviewing task results, finding error patterns, comparing code vs
Claude Code
$ npx -y skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj --agent claude-code
Codex
$ npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj -a codex -y
Cursor
$ npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj -a cursor -y
Gemini CLI
$ npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj -a gemini-cli -y
GitHub Copilot
$ npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj -a github-copilot -y
OpenCode
$ npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj -a opencode -y
Windsurf
$ npx skills add AMAP-ML/LongHorizon-Harness --skill analyze-traj -a windsurf -y
name: analyze-traj description: "Analyze OSWorld-V2 agent trajectory logs and task results to produce actionable insights. Use this skill whenever the user wants to understand agent performance on OSWorld tasks — including analyzing trajectories, reviewing task results, finding error patterns, comparing code vs GUI strategies, identifying which tools/commands the agent used, or deciding which task types to scale up in the benchmark."
If only one task is issued, analyze it directly with instruction: [analyze-single-traj.md](prompts/analyze-single-traj.md).
If multiple tasks or a whole results directory are issued, use subagents to analyze them in parallel (one agent for each task). Do not analyze them sequentially by yourself. DO NOT tell it what to do. Just ask the subagent to analyze the task in target directory and use this skill (`analyze-traj`) to do the analysis. Pass any user instructions to every subagent.
After the per-task reports are ready:
The long-horizon computer-use harness. Run AI agents across desktop apps and the CLI for extended periods while preserving task state and making reliable progress on complex workflows. Features fresh-context execution, durable verified state, independent auditing, recoverable progress, and native Claude Code / Codex / OpenClaw integration.
Check OSWorld tasks. Validate the evaluation function, verify that the instruction is feasible given the task setup and…
Migrate an agent from upstream OSWorld into this OSWorld-V2 repository, add matching evaluation entrypoints, and verify the…
Provision and verify an OSWorld-V2 checkout after clone. Use when the user asks for OSWorld-V2 setup, installation, onboarding,…
Reproduce CUA-Harness experiments on WeaveBench from a GitHub checkout. Use when the user wants an AI coding agent to set up…
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…