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recursive-research

Claude Code skill for recursive research up to PhD level across any domain. Source tiering, WDM + Munger inversion for autonomous decisions, and disk checkpointing to survive context compaction.

Install / Use

npx skills add Anjos2/recursive-research

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

81/100

Supported Platforms

Claude Code

Tags

Our assessment of recursive-research

recursive-research scores 81/100 on our quality scale, 385th of 440 Education & Research skills we index.

Its SKILL.md is 9.8 KB long, well organised into 25 sections with 8 code examples: a thorough specification that gives an agent plenty to work with.

It has 53 GitHub stars, a meaningful sign that others use it.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
7/20
Freshness
11/15

Maintenance, license and trust

  • The repository was last updated about 7 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
  • Our last check on 2026-10-18 found the source still online.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 94/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.

AI review by kimi-k2.7-code on 2026-09-24. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

recursive-research compared with similar skills

All 4 of these similar skills score higher than recursive-research; compare them before choosing.

SkillScoreStarsUpdatedFormat
recursive-research (this skill)by Anjos281537mo agoSKILL.md
last30days-skillby mvanhorn10066.7k2d agoCLAUDE.md
algorithmic-artby anthropics100177.9k44d agoSKILL.md
pptxby anthropics100177.9k44d agoSKILL.md
designby nextlevelbuilder100143.4k5d agoSKILL.md

Frequently asked questions

How do I install recursive-research?
Run npx skills add Anjos2/recursive-research. The install tabs above show the steps for each supported agent.
Which AI agents does recursive-research work with?
It is written for Claude Code, as a SKILL.md file. Other agents that read the same format can often use it too.
Is recursive-research safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It is MIT-licensed and scores 94/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is recursive-research still maintained?
The repository was last updated about 7 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.

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)


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.

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 |


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.


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

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" |


"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).


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.


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.


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.


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

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.


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

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

GitHub topics

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


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.


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 ⭐.

Related Skills

View on GitHub
GitHub Stars53
CategoryEducation
Updated6mo ago
Forks4

Trust signals

94/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 low