Recursive Research

recursive-research

Claude Code **plugin & skill** for recursive research up to PhD level on any topic — science, tech, business, arts, humanities. Source tiering, loop auto-regulation, disk checkpointing, and WDM + Munger inversion for autonomous decisions.

**Version:** 2.2.0 · **License:** [MIT](LICENSE) · **Author:** Joseph Huayhualla ([@Anjos2](https://github.com/Anjos2))

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What it does

You give it a **research seed** (a topic) and the skill:

1. Asks for mode (web / local / mixed), local paths if applicable, priority/excluded sources, and a cycle cap.
2. Identifies 3-5 seed threads applying WDM (Weighted Decision Matrix) + Munger Inversion.
3. Detects available MCPs (Firecrawl, Context7, WebFetch, WebSearch) and prioritizes by speed and quality.
4. Iterates in auto-regulated cycles — each cycle picks the least-covered thread, selects sources, investigates, consolidates.
5. Tiers every source into Tier 1 / 2 / 3 / Rejected with transparent criteria.
6. Saves disk checkpoints every cycle — survives context compaction.
7. Closes when the 5-criteria PhD fitness function is met, or upon hitting the cycle cap.
8. Asks if you want to keep going. Research can be infinite.

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Why it's different

Feature	How it solves it
Works across any domain	Generic source tiering (papers, academic books, official archives, raw data), not code-only
Rejects garbage sources automatically	Explicit criteria: no author, data-less marketing, SEO spam, unsupervised AI content
Survives context limits	Per-cycle disk checkpoint + --resume mode for new sessions
Self-critical	Munger inversion applied to the consolidated knowledge: what do I not know? what bias do my sources share? what's missing?
Asks before assuming	Full Phase 0 interrogation of the user
Transparent	Every non-trivial autonomous decision runs WDM + Munger and shows the reasoning