A. The Claude Skills Ecosystem: An Overview for Researchers

Purpose: Claude Code skills are reusable instruction sets that extend what Claude can do — invoked with a /slash-command or automatically when Claude detects relevance. The ecosystem has grown to 69,000+ published skills in under two years. This document orients you to what exists, what is worth installing, and how specific skills connect to the research lifecycle described in the B.lifecycle series. It is not a complete catalogue — it is a researcher's entry point.

For installing individual skills and writing your own, see A14.skills-for-researchers.

Two principles before you browse

Before installing anything, two principles from the broader skills community are worth knowing.

The distributional convergence problem. Language models reproduce the statistical centre of their training data — what researchers call "distributional convergence." Without intervention, Claude will produce the average response for any given prompt: the most common argument structure, the most generic analytical framing, the prose style that minimises surprise. A good skill is one that shifts this default — that pulls Claude away from the centre toward how you work. The test: does this skill change Claude's default behaviour, or does it just add a slash command you have to remember to invoke? The former is worth installing; the latter probably is not.

The best skills are the ones you create. This is the overwhelming consensus of working researchers and practitioners who have used the skills system extensively. The skills marketplace gives you starting points; your own workflow knowledge gives you leverage. After any productive session where Claude develops a new working method with you — a source analysis approach, a coding scheme, a particular way of structuring your notes — the move is: "Based on this conversation, create a skill so we can do this faster next time." The result is a skill tuned to your data, your field, and your standards — not the statistical centre of 69,000 published skills.

A practical constraint follows from this: every installed skill is loaded into your context window before the conversation begins. Each skill consumes tokens you could have used for your actual work. Install 3–5 skills maximum. Remove any skill you cannot explain what it does. Quality over quantity.

The ecosystem in brief

Skills are SKILL.md files — a YAML header describing when and how the skill activates, followed by markdown instructions that Claude follows when invoked. They live in:

The format is an open standard (agentskills.io), so the same skill file works in Claude Code, Cursor, Gemini CLI, and Codex.

Discovery channels:

Installation:

# Copy a skill folder to your personal skills directory
cp -r path/to/skill-folder ~/.claude/skills/

# Or install via npx
npx skills add [GitHub URL]

The universal foundation: document skills

Before any specialised research skill, four document skills are universally recommended as the starting point for any researcher. These are the most installed Claude skills and work for non-programmers with zero configuration:

Skill What it does How to invoke
pdf Extracts text, tables, and metadata from PDFs; works on image-based PDFs via OCR /pdf [file]
docx Creates and edits Word documents with tracked changes, comments, formatting /docx [task]
xlsx Spreadsheet manipulation with formulas, charts, and data transformations /xlsx [task]
pptx Reads, generates, and adjusts PowerPoint slides /pptx [task]

These are official Anthropic skills. A 48-page PDF that takes 90 minutes to read manually is processed in under a minute. A batch of 40 scanned archival PDFs — the kind of task that appears in B.lifecycle.3.datacapture — can be processed systematically with /pdf driving the extraction.

Three research-specific collections worth knowing

Beyond the document foundations, three collections are specifically worth knowing for academic researchers — two skill collections, and one full plugin that shows what the pattern looks like taken all the way.

K-Dense Scientific Skills

Repository: K-Dense-AI/claude-scientific-skills License: MIT (free to use) Scale: 175 skills covering 250+ scientific and financial databases, 60+ Python packages

The largest dedicated scientific skills collection. Originally oriented toward life sciences and computational biology, but contains a substantial subset relevant to humanities, social science, and network analysis. Designed for Claude Code.

Installation: Clone the repository and copy the relevant folders:

git clone https://github.com/K-Dense-AI/claude-scientific-skills.git
# Then copy specific skills you want:
cp -r claude-scientific-skills/scientific-skills/literature-review ~/.claude/skills/
cp -r claude-scientific-skills/scientific-skills/pubmed-database ~/.claude/skills/
# etc.

You do not need to install all 175 skills — pick the ones relevant to your work.

Imbad0202 Academic Research Skills

Repository: Imbad0202/academic-research-skills License: CC-BY-NC 4.0 Recommended model: Claude Opus 4.6 on Max plan (token-intensive)

A four-skill pipeline covering the full academic workflow: deep research → paper writing → peer review → pipeline orchestration. More powerful than K-Dense for pure writing and review tasks, but heavier on resources and setup. Requires a Claude Max subscription and experimental features enabled. Best for PhD researchers doing intensive publication work.

Installation:

mkdir -p .claude/skills
git clone https://github.com/Imbad0202/academic-research-skills.git .claude/skills/academic-research-skills

neuroflow — a full domain plugin, not just a skill folder

Repository: stanislavjiricek/neuroflow · Project site Field: Neuroscience (EEG, iEEG, fMRI, eye tracking, physiological signal analysis) Scale: ~30 slash commands, ~40 domain skills, ~10 autonomous agents, plus a git-tracked project-memory folder (.neuroflow/)

Not relevant to DISSINET's own work — this is built for neuroscientists, not historians — but worth knowing about as the ceiling example of what the two collections above are a lighter version of. Where K-Dense and Imbad0202 are folders of skills you install and invoke individually, neuroflow is a full Claude Code plugin that orchestrates an entire research lifecycle end to end: ideation → preregistration → experiment design → data collection (BIDS-compliant) → preprocessing → analysis → manuscript (a "brutal" paper-writer → paper-critic review loop before anything is saved) → publication. Commands, skills, and autonomous agents (a literature-search agent, a manuscript reviewer, a project-coherence auditor) all read from and write back to the same structured .neuroflow/ memory folder, so a session six months later has the same project context as the one that started it.

The reason it belongs in this book despite the field mismatch: it is the concrete answer to "what would it look like if a research field built out the commands+skills+agents+project-memory pattern this book only sketches, all the way?" A DISSINET-specific equivalent — commands for corpus ingestion, skills encoding the project's prosopographical conventions, an agent that checks new encoding against the existing database schema, a shared .dissinet/ memory folder — is the same architecture applied to a different domain, not a different idea.

Installation:

claude plugin marketplace add stanislavjiricek/neuroflow
claude plugin install neuroflow@neuroflow

Then run /neuroflow:neuroflow in a project folder to begin.

Skills across the research lifecycle

The following maps specific skills to the lifecycle phases from B.lifecycle. Not all of these will be relevant to every researcher — pick what fits your actual work.

Phase 1: Research question formation (B.lifecycle.1.creativity)

The most relevant skills here are Socratic rather than generative — they stress-test and challenge, rather than produce questions for you.

scientific-brainstorming (K-Dense) Structured brainstorming for scientific problems. Useful as the Socratic partner role described in B.lifecycle.1.creativity: you bring a tentative direction and use the skill to interrogate it. Invoke after you have a rough question, not before.

scientific-critical-thinking (K-Dense) Evaluates arguments for logical consistency, hidden assumptions, and methodological weaknesses. The "what is wrong with this?" prompt made into a systematic workflow.

hypothesis-generation (K-Dense) Structured hypothesis generation for research design. Most useful at the boundary between question formation and research design — when you have a question and need to make it testable.

Deep Research (Imbad0202) 13-agent research team that formulates research questions using the FINER scoring framework (Feasible, Interesting, Novel, Ethical, Relevant), conducts systematic literature searches, and grades evidence. The question-formulation mode is useful; be aware that the questions Claude generates will reflect the centroid of published literature, not the frontier — see B.lifecycle.1.creativity for why this matters.

Phase 2: Literature engagement (B.lifecycle.2.literature)

This is where the skills ecosystem adds the most value for researchers, covering both finding and synthesizing.

literature-review (K-Dense) ★ The most complete literature review skill available. Integrates PubMed, arXiv, bioRxiv, and Semantic Scholar. Full workflow: PICO-framework search planning → multi-database search → deduplication → screening → data extraction → thematic synthesis → citation verification via CrossRef → PDF output with PRISMA flow diagram. Supports APA, Nature, Vancouver, Chicago, and IEEE citation styles.

Important distinction: this skill covers the finding and structuring phase of literature engagement. The interpretation — what the literature means for your specific research question — remains yours. Do not mistake a well-structured literature synthesis for your literature review argument.

openalex-database (K-Dense) Queries OpenAlex, which indexes 240M+ scholarly works with no API key required. Supports author research, institutional output, citation tracking, and open access filtering. The best starting point for social science and humanities literature discovery — broader and less life-science-specific than PubMed. Up to 50 items per query with CSV export.

pubmed-database (K-Dense) Direct API access to PubMed via NCBI E-utilities. Boolean operators, MeSH term support, PICO-framework systematic review searches. 10,000 results per search with API key (3,000 without). Primarily relevant for medical and biological literature; less so for humanities unless your work intersects with medical history or health sciences.

arxiv-database (K-Dense) Searches arXiv with keyword, author, category, and Boolean support. Particularly relevant for computational social science, digital humanities, network science, and methodological literature.

pyzotero (K-Dense) Zotero integration skill. Connects Claude to your Zotero library directly through the API. Complements the MCP-based connection described in A.setup.zotero-mcp — the MCP gives deeper real-time access; this skill is lighter and works without the full MCP setup.

scholar-evaluation (K-Dense) Evaluates scholarly sources for quality, credibility, and methodological rigour. Useful for the verification step after literature search — before committing sources to your synthesis.

Weizhena/Deep-Research-skills (separate repository) Five skills forming a structured human-in-the-loop research workflow: generate an outline → expand items → expand fields → parallel web investigation → synthesise to Markdown report. Designed for paper surveys and benchmark reviews. The human review step between outline and deep investigation is explicitly built in — closer to the researcher-in-control model than fully autonomous research skills.

Phase 3: Data capture (B.lifecycle.3.datacapture)

pdf (Anthropic) ★ Already covered under foundations. The core tool for archival document processing — batch extraction from scanned sources, OCR handling, structured output from PDFs. Combine with Claude Code's batch processing for large corpora.

markitdown (K-Dense) Converts various document formats (Word, Excel, PowerPoint, HTML, image files with OCR) to Markdown. The hub for format conversion in the to-markdown and from-markdown workflow described in A.markdown-central. Useful when incoming material arrives in mixed formats.

open-notebook (K-Dense) Notebook-style research documentation — capturing structured notes from source material in a format that accumulates across a project. Connects to the CLAUDE.md and project history infrastructure.

Phase 4: Data analysis (B.lifecycle.4.dataanalysis)

statistical-analysis (K-Dense) Structured statistical workflows with statsmodels and scikit-learn. Guides through method selection, assumption testing, and result interpretation. For social scientists doing quantitative work: treats the execution/interpretation distinction seriously — it produces analysis, not conclusions.

networkx (K-Dense) Network analysis workflows using the NetworkX library. Specifically relevant for DISSINET's network data work — constructing, analysing, and visualising relational graphs from historical sources. Covers centrality measures, community detection, path analysis, and visualisation.

exploratory-data-analysis (K-Dense) Systematic EDA workflow: column profiling, distribution analysis, missing data assessment, correlation matrices, outlier detection. The right starting point before committing to a specific analytical approach.

scientific-visualization (K-Dense) Publication-ready figure generation. Covers chart selection, accessibility, colour palettes, and output formats. Useful for both exploratory analysis and final publication figures.

Phase 5: Manuscript (B.lifecycle.5.manuscript)

scientific-writing (K-Dense) Transforms research findings into journal-submission-quality manuscripts using IMRAD structure. Enforces a useful discipline: full paragraphs with flowing prose, no bullet points in the final manuscript. Supports CONSORT, STROBE, PRISMA, and other reporting guidelines. Citation styles: APA, AMA, Vancouver, Chicago, IEEE. Output via LaTeX.

Important: this skill handles structure and prose mechanics. The argument — what you are actually claiming and why — must come from you before you invoke it.

peer-review (K-Dense) Seven-stage structured manuscript evaluation: initial assessment → section-by-section review → methodological rigour → reproducibility → figure/data presentation → ethical considerations → writing quality. Output: summary statement, numbered major and minor comments, recommendation. Can be used on your own draft before submission — the manuscript-as-diagnostic function described in B.lifecycle.5.manuscript.

Academic Paper + Academic Paper Reviewer (Imbad0202) 12-agent paper writing skill with a 5-perspective peer review panel including a Devil's Advocate reviewer. The most complete writing and review pipeline available. Token-intensive — requires Max plan. Best for PhD researchers producing journal manuscripts.

markdown-mermaid-writing (K-Dense) Structured writing with Mermaid diagram integration — useful for methodology diagrams, process flows, and conceptual figures that can be embedded in Markdown documents and exported.

Phase 0: Iteration principle (B.lifecycle.0.iteration)

academic-paper-reviewer / peer-review (both collections) The peer review skills double as pre-mortem tools: invoke them on a draft section or research design description before you commit. "What would a sceptical reviewer say about this approach?" as a structured workflow rather than an ad hoc prompt.

scholarly-evaluation (K-Dense) Can be used on your own methodology or preliminary findings — not just on sources — to anticipate the objections your field will raise.

MCP servers: connecting Claude to live data

Skills tell Claude how to behave. MCP servers give Claude access to external data and services. They are complementary, not competing — many research workflows use both.

The distinction in one sentence: A skill is a set of instructions; an MCP server is a live connection. Install a literature-review skill and Claude gains a methodology for structuring its searches. Install a Zotero MCP server and Claude can actually query your library in real time.

What MCP servers can connect to

For research, the useful MCP servers fall into three groups:

Your own library and data

External knowledge and search

Structured data and databases

How MCP servers are installed

MCP servers are configured in your Claude Code settings file — not installed into a project folder like skills. The configuration block goes in ~/.claude/settings.json (global, all sessions) or .claude/settings.local.json (project-specific):

{
  "mcpServers": {
    "zotero": {
      "command": "uvx",
      "args": ["zotero-mcp"]
    },
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "your-key-here"
      }
    }
  }
}

Each entry: a name you choose, the command to start the server, and any environment variables (API keys). Claude Code starts the server process automatically at the beginning of each session.

For the full configuration scope — when to use global vs project-level — see A.concept.global-vs-local. For the conceptual explanation of what MCP servers are and how Claude uses them, see A.concept.under-the-hood.

Skills vs MCP: which to reach for

Situation Use
Claude needs a methodology or workflow Skill
Claude needs access to your data in real time MCP server
You want Claude to format documents consistently Skill
You want Claude to search your Zotero library MCP server
You want Claude to follow your annotation scheme Skill
You want Claude to query a live database MCP server
You're at Level 0–1 on the adoption spectrum Skills first — MCP adds setup overhead
You're at Level 2–3 and have repeatable workflows MCP becomes worth the installation cost

The practical advice: start with skills, which require no configuration beyond copying a file. Add an MCP server when you find yourself repeatedly copying and pasting content from an external source into Claude — that friction is exactly what MCP eliminates.

A note on the automated pipeline skills

The Imbad0202 academic pipeline — 10 stages, 13+ agents, integrity verification, automated revision cycles — represents the maximum extent of current AI-assisted academic work. It is genuinely impressive and, for certain tasks, useful.

For researchers at DISSINET, a few caveats:

Token cost. The full pipeline requires Claude Opus 4.6 on a Max plan and consumes substantial tokens. Running it regularly is not a standard Pro plan use case.

The execution/interpretation problem at scale. A 10-stage automated pipeline produces a complete draft manuscript. That draft will need to be dismantled and rebuilt around your actual argument, your actual evidence, and your actual field position. The pipeline is most useful as a structural scaffold — a fast first pass to react to — not as a finished product.

The autonomy question. Delegating the full research-to-manuscript pipeline to a multi-agent system raises the autonomy and ownership questions from B.autonomy and B.ownership in their sharpest form. The pipeline will produce something that looks like your work. Ensure it is.

Creating your own skills

The most valuable skills you will ever have are the ones that encode your own working methods — not the methods of a generic researcher, but yours, for your data type, your source language, your analytical standards.

The principle

When Claude helps you develop a new approach — a way of extracting structured data from a type of source you work with regularly, a reading protocol that suits your annotation system, a framework for evaluating a particular kind of argument — the method is currently locked inside that session. The next session starts from scratch. A skill converts the method into persistent behaviour.

This is the insight behind the community principle: after any productive session, ask Claude to extract the method into a skill. The skill does not have to be perfect on the first attempt; it gets refined through use.

The tool: skill-creator

Anthropic publishes an official meta-skill called skill-creator that automates the extraction process. Install it once:

# Install via npx
npx skills add skill-creator
# Or copy from ~/.claude/skills/ if already present

Once installed, at the end of any productive session you can invoke:

/skill-creator

Claude will examine the conversation, identify the recurring pattern or method, and draft a SKILL.md file for it. You review and adjust; the output goes into ~/.claude/skills/ (global) or .claude/skills/ (project).

The workflow: from work in progress

You do not need a finished method to create a skill. The workflow works from work in progress:

  1. Do the work once with Claude — process a source, analyse a document, draft a section, code a category. Let Claude develop the approach interactively.
  2. Notice what worked — what did Claude do in this session that you would want it to do by default in future sessions?
  3. Extract the skill — either use /skill-creator or write explicitly: "Based on how we handled this source today, create a SKILL.md that captures this extraction approach as a reusable skill."
  4. Test and refine — invoke the skill on a new source. Adjust the SKILL.md until the behaviour matches what you actually want.
  5. Remove what you do not use — a skill you cannot explain is a skill that costs context window without adding value.

Research-specific examples for DISSINET

Source extraction skill. You process a type of historical source that appears repeatedly in your work — inquisition depositions, notarial records, network co-occurrence lists. After a session in which you and Claude develop an extraction method together, create a skill that encodes the field structure, the language patterns to look for, and the output format. Result: a /extract-deposition or /extract-network command that applies your standard to new sources without re-explaining the whole protocol.

Style-matching skill. Feed Claude three or four paragraphs of your own published prose. Ask it to identify your stylistic patterns — sentence length, argument rhythm, how you handle qualifications, how you transition between claims. Then: "Create a skill that applies this style to any draft I give you." Result: a revision skill that brings drafts toward your voice rather than toward the statistical average.

Grant section skill. After a successful grant section — a research rationale, a feasibility argument, a risk mitigation section — extract the structural logic into a skill. The next grant proposal starts with your established formula as the default.

Annotation scheme skill. If your project uses a defined coding scheme (for network coding, inquisition categories, textual annotations), encode it in a skill. This means Claude applies the same scheme in new sessions without re-reading the protocol document each time.

Skill design resources

The SKILL.md format is simple enough to write by hand once you have seen two or three examples. The YAML header specifies the trigger condition and invocation style; the body is plain markdown instructions. If you have written CLAUDE.md instructions, you already know how to write the body of a skill.

Installation summary

# Clone K-Dense and install selected skills
git clone https://github.com/K-Dense-AI/claude-scientific-skills.git
cp -r claude-scientific-skills/scientific-skills/literature-review ~/.claude/skills/
cp -r claude-scientific-skills/scientific-skills/openalex-database ~/.claude/skills/
cp -r claude-scientific-skills/scientific-skills/peer-review ~/.claude/skills/
cp -r claude-scientific-skills/scientific-skills/networkx ~/.claude/skills/
# Add others as needed

# For Imbad0202 (project-level, Max plan users)
mkdir -p .claude/skills
git clone https://github.com/Imbad0202/academic-research-skills.git .claude/skills/

After installing, verify in Claude Code:

/literature-review help

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