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From asta-tools
Create, configure, and monitor AutoDiscovery runs — either via the hosted service (`asta autodiscovery`) or via the local CLI (`asta-autodiscovery`). Use when the user asks about their runs, experiments, discoveries, wants to check status, or wants to start a new discovery run.
How this skill is triggered — by the user, by Claude, or both
Slash command
/asta-tools:autodiscoveryThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
AutoDiscovery is an AI-driven scientific discovery platform that runs iterative experiments guided by Bayesian surprise and MCTS optimization.
AutoDiscovery is an AI-driven scientific discovery platform that runs iterative experiments guided by Bayesian surprise and MCTS optimization.
This skill is a router. Inspect the user's request, pick one workflow, then read its .md file in workflows/ and follow it. Do not execute a workflow from memory — always open the file first.
| Name | Purpose | Detailed instructions |
|---|---|---|
| remote | Use the hosted AutoDiscovery service via asta autodiscovery. Requires Asta auth and consumes credits; runs execute on Asta infrastructure. | workflows/remote.md |
| local | Run AutoDiscovery locally via the asta-autodiscovery CLI. Executes on the user's machine with user-provided API keys for making LLM calls | workflows/local.md |
asta-autodiscovery CLI → local.asta autodiscovery (note the space) subcommand → remote.The two workflows are independent. Do not silently switch between them mid-task.
npx claudepluginhub allenai/asta-plugins --plugin asta-toolsRun scientific (software) experiments. Use when the user asks to "run an experiment", "run an investigation", or "research with Asta." Also use this skill to analyze experimental data generate a research report from it. The user may refer to this system by its internal project name, "Panda."
Curated index of autonomous improvement loops and research agents following the keep-or-revert pattern for code optimization, ML experimentation, and any measurable metric.
Run an end-to-end automated ML research project with the AIRAS MCP tools, using backend LLM API keys for the generation steps (hypothesis, experimental design, analysis, paper writing). Use when the user wants to start or continue automated research with AIRAS and LLM provider API keys (OPENAI_API_KEY etc.) are configured in ~/.airas/credentials.json. If no LLM provider key is available, use the auto-research-claude-code skill instead.