autoresearch-toolfinder

name: autoresearch-toolfinder description: Find the right autonomous-research / autoresearch tool, framework, port, or skill for a research or engineering task. Searches a local cached index of two curated awesome-autoresearch lists (alvinreal + yibie, 550+ entries) and returns only the few matching tools, never loading the whole catalog into context. Use when the user wants to pick, compare, or set up an autoresearch loop, an AI-scientist / research-agent system, a domain or hardware port (Apple Silicon, RTX, RL, trading, materials, bio, vision, kernels...), or an evaluation harness, or asks "is there an autoresearch tool for X". version: 1.0.0 license: MIT tags: [autoresearch, research-agents, tool-discovery, scientific-research, token-efficient]

Recommends tools from two curated awesome-autoresearch catalogs (550+ entries) WITHOUT reading the whole list into context. You run a search script and read back only the top matches.

How to use (token-efficient — follow this; do NOT cat the index)

The catalog is large. Never read data/index.json directly (that defeats the purpose). Run the query script from the skill directory; it returns only the top candidates:

python3 scripts/query.py "<keywords from the user's task>"
# options:  --source alvinreal|yibie   --category "<substring>"   --limit 8   --json
python3 scripts/query.py --list-categories      # see sections + counts first

Examples:

Apple-Silicon / MLX port: python3 scripts/query.py "apple silicon mlx mac metal"
End-to-end AI scientist: python3 scripts/query.py "ai scientist paper literature review" --source alvinreal
RL post-training loop: python3 scripts/query.py "reinforcement learning grpo post-training"
Trading strategy search: python3 scripts/query.py "trading strategy backtest" --source yibie
Browse a whole section: python3 scripts/query.py "" --category "Evaluation"

Then: read the handful of name + url + one-liner results, pick the best 1-3 for the user's actual context, and say why. Open a specific repo URL (WebFetch) only if the user wants depth.

When to activate (auto)

Activate when the user is choosing / comparing / setting up: an autoresearch or self-improvement loop; an AI-scientist or research-agent system; a hardware/platform port; a domain adaptation (bio, materials, finance, vision, RL, kernels, robotics...); or an eval harness — or asks "what should I use for autonomous research / overnight experiments on X".

Not this skill: to actually run a full autonomous research project end-to-end, use the sibling autoresearch orchestration skill. This skill is the catalog / finder only.

Keeping it current (update tracking)

data/state.json stores each upstream repo's commit SHA + sync time.

python3 scripts/check_updates.py     # cheap: 1 API call/repo, compares SHA, exits 1 if stale
python3 scripts/update_index.py      # re-fetch + re-parse both repos, rewrite the index

A weekly user systemd timer (systemd/autoresearch-index.timer) refreshes automatically; query.py also prints a hint when the local index is older than 30 days.

autoresearch-toolfinder