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 · Author: Joseph Huayhualla (@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

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Installation

This repo is a Claude Code marketplace containing one plugin (recursive-research). The easiest way to install is through Claude Code's built-in plugin manager.

Option A — Via marketplace (recommended)

Inside Claude Code, run:

/plugin marketplace add Anjos2/recursive-research
/plugin install recursive-research

The plugin appears in /plugin → Installed. Invoke it with /recursive-research:recursive-research.

Option B — Via the official Plugin Directory (pending approval)

Anthropic maintains a central Plugin Directory. Once approved there (submission in progress), install becomes a one-liner without the marketplace add step:

/plugin install recursive-research

Option C — As a standalone skill (minimal, no plugin manager)

If you prefer to bypass the plugin ecosystem, copy only the skill markdown:

Linux / macOS:

git clone https://github.com/Anjos2/recursive-research.git
mkdir -p ~/.claude/skills/recursive-research
cp recursive-research/plugins/recursive-research/skills/recursive-research/SKILL.md ~/.claude/skills/recursive-research/

Windows (PowerShell):

git clone https://github.com/Anjos2/recursive-research.git
New-Item -ItemType Directory -Force -Path "$HOME/.claude/skills/recursive-research"
Copy-Item recursive-research/plugins/recursive-research/skills/recursive-research/SKILL.md "$HOME/.claude/skills/recursive-research/"

Invoked without namespace as /recursive-research. Verify with /help inside Claude Code.

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Usage

Standard invocation

/recursive-research:recursive-research     # if installed as plugin
/recursive-research                        # if installed as standalone skill

The skill guides you interactively. Answer in natural language.

Resume a paused research

/recursive-research:recursive-research --resume <slug>

<slug> = kebab-case name of the topic (e.g., episodic-memory-humans).

List saved research

/recursive-research:recursive-research --list

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Usage examples by domain

Domain	Suggested seed
Neuroscience	"Mechanisms of episodic memory in humans"
Philosophy	"Modern application of Stoic philosophy"
Music	"Minimalism in 20th-century music"
Business	"B2B SaaS monetization models in 2025"
History	"Fall of the Western Roman Empire: economic causes"
Biology	"CAR-T cell immunotherapy against cancer"
Technology	"Hexagonal architecture in microservices"
Law	"European AI Act and its extraterritorial impact"

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"PhD level" criterion

The skill only declares PhD when all 5 criteria are met:

1. Coverage ≥80% across all seed threads
2. ≥3 Tier-1 sources per thread
3. New-finding saturation ≤5% for 3 consecutive cycles
4. Munger inversion applied to the knowledge (what I don't know, what sources contradict, what biases exist)
5. ≥3 explicit cross-thread connections between different threads

If any criterion fails, it does not declare PhD and keeps iterating — or asks for confirmation upon hitting the cycle cap (default 20, configurable).

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Source tiering

Tier	Qualifies	WDM weight
1	Peer-reviewed papers · academic books · official standards (W3C, RFC, ISO, WHO) · primary archives · official datasets	5
2	Official repos · blogs from citable authors · recorded conferences · Wikipedia with references · reports with methodology	3
3	Blogs with citations to T1/T2 · high-voted forum answers with sources · recorded interviews with identifiable experts	2
Reject	No author · data-less marketing · SEO spam · tutorials without sources · unsupervised AI content	0

Every consulted source is logged in a per-tier file for later audit.

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Pre-loaded seed sources

The skill auto-suggests reliable sources by domain. Examples:

• Science: arXiv, Semantic Scholar, Google Scholar, Connected Papers, OpenReview
• Medicine: PubMed, Cochrane Library, WHO, ClinicalTrials.gov
• Humanities: JSTOR, SSRN, Project MUSE
• Code: GitHub, Context7, RFCs, W3C specs
• Data: World Bank, OECD Data, Our World in Data, Pew Research
• Art/culture: Europeana, Google Arts & Culture, Internet Archive, Project Gutenberg

The user can add or reject any before starting.

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WDM + Munger inversion

Decision frameworks applied at each non-trivial autonomous step:

• WDM (Weighted Decision Matrix) — enumerate 3+ viable alternatives, criteria with weights, 1-5 scoring, compared totals.
• Munger inversion (Charlie Munger via Jacobi) — ask inverted about the winning option: "how would it fail? what bias does it have? what am I ignoring?"

The skill applies both to:

• Select seed threads
• Select sources per cycle
• Decide when to close the research
• Validate the consolidated knowledge at the end

Reference: Charlie Munger's essay on mental inversion.

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Recommended MCPs

The skill detects what you have and uses them in this order:

1. Firecrawl MCP (highly recommended) — AI-optimized scraping
2. Context7 MCP — official library docs
3. WebSearch + WebFetch (built-in Claude Code) — universal fallback
4. Chrome DevTools MCP — only when content requires real JS execution; generally too slow for large-scale research

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Generated files

In memoria/investigaciones/<slug>/ of the active project:

• estado.md · hilos.md · hallazgos.md
• fuentes-tier-1.md · fuentes-tier-2.md · fuentes-tier-3.md · fuentes-rechazadas.md
• ciclo-01.md, ciclo-02.md, ..., ciclo-N.md (checkpoints)
• sintesis.md · acciones.md · gaps.md (upon closing)

If memoria/ doesn't exist in the project, the skill creates it (notifying the user) — it's an explicit dependency.

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Repository structure

recursive-research/                              ← the repo is a Claude Code marketplace
├── .claude-plugin/
│   └── marketplace.json                        ← marketplace manifest (declares the plugin)
├── plugins/
│   └── recursive-research/                     ← the plugin (named same as the marketplace)
│       ├── .claude-plugin/
│       │   └── plugin.json                     ← plugin manifest
│       └── skills/
│           └── recursive-research/
│               └── SKILL.md                    ← the skill instructions (the actual content)
├── LICENSE                                      ← MIT
├── README.md                                    ← this file
├── PRIVACY.md                                   ← privacy policy (no data collection)
└── .gitignore

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Contributing

Issues and PRs welcome. If you improve a criterion, add a tier, find a new anti-pattern, or want support for more domains: open a PR.

Roadmap

• Native integration with reference managers (Zotero, Mendeley)
• Export research to academic formats (LaTeX, BibTeX)
• Collaborative mode — multiple agents investigating threads in parallel
• Automatic source-quality metrics (h-index, journal impact factor)
• Support for PDFs behind legal paywalls

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GitHub topics

claude-code · claude-code-skill · claude-code-plugin · ai-agent · research-tool · recursive-research · knowledge-management · weighted-decision-matrix · mental-models

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Privacy

recursive-research does not collect any user data. Everything runs locally on your machine. See PRIVACY.md for the full policy.

License

MIT — use, modify, distribute freely. Just keep the copyright notice.

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Acknowledgments

• Charlie Munger — for "Invert, always invert" (via Carl Jacobi)
• Claude Code team — for the skill and plugin format
• Anthropic — for the model that makes this possible

If this skill was useful, consider giving the repo a ⭐.