by jordan-gibbs · MCP Server · ★ 4.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install hyperresearch && hyperresearch installTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 4,299 |
| Forks | 428 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 18 |
| Last Updated | 2026-11-01 |
| Created | 2026-04-09 |
| Platforms | browser, claude-code, codex, mcp, python |
| Est. Tokens | ~16k |
The Most Powerful Deep Research Harness Hyperresearch turns Claude Code into a deep research agent. and currently leads the DeepResearch-Bench RACE leaderboard (benchmarked internally). A tier-adaptive 16-step pipeline produces adversarially-audited reports with full source provenance. Every fetched source lands in a persistent, searchable vault that compounds across sessions.
These tools work well together with hyperresearch for enhanced workflows:
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: