OpenAgentSkill Registry Manifest
Skill: exploratory-autoresearch
Slug: gaasher-exploratory-autoresearch
Category: research
Description: Use when the user wants an autonomous ML research loop that explores the space broadly rather than hill-climbing one approach. A temperature scheduler replaces the usual hypothesis step: it forces several wild, diverse swings (full rewrites, different architectures/regimes) early, then enters an adaptive phase that picks swing / merge / exploit per iteration — with a hard stagnation guard that bans further small-step exploits once they run too long, forcing a pivot back to a swing or merge. Tracks an approaches.md registry and a move_type per iteration; analyses every run before the next move. One change per iteration; loops forever until interrupted. Not for the standard analysis-first ml-autoresearch (which lets analysis alone choose each change), one-off training runs, or sweeps.

Agent fit:
- Decision: 62/100 Prototype first
- Primary fit: Research agents
- Role: Fallback candidate

Supply profile:
- Track: Research and knowledge work
- Scenario: Research agents
- Applicable agents: Claude Code, CLI, Codex, Cursor
- Maintenance: 2mo since push
- Risk: Needs review

Trust:
- Trust score: 75/100 Strong shortlist
- Audit: 76/100 Needs review

Attribution:
- Status: Registry indexed
- Source: github fast track
- Creator: gaasher
- Claim URL: https://www.openagentskill.com/skills/gaasher-exploratory-autoresearch#claim-this-skill

Install:
npx skills add gaasher/Agent-Loop-Skills --skill exploratory-autoresearch

URLs:
- Web: https://www.openagentskill.com/skills/gaasher-exploratory-autoresearch
- API: https://www.openagentskill.com/api/agent/skills/gaasher-exploratory-autoresearch
- Install API: https://www.openagentskill.com/api/skills/gaasher-exploratory-autoresearch/install
- Repository: https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/exploratory-autoresearch