Install
$ agentstack add skill-jajupmochi-agent-harness-autoresearch-toolfinder ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
autoresearch-toolfinder
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 ""
# options: --source alvinreal|yibie --category "" --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.
Sources
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: jajupmochi
- Source: jajupmochi/agent-harness
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.