paper-to-mindmap

paper-to-mindmap is a skill for Claude Code from grburgess/mindgap. It costs 113 tokens per session (1,108 once invoked), scanned A, original, MIT.

A workflow for turning a research paper into a node in the Mindgap knowledge graph, a connected record of topics and evidence.

In plain words
What is it for?
Use it after reading a technical paper from arXiv or a local PDF to check relevance, find related nodes, and add the paper with evidence.
Why use it?
It prevents irrelevant papers and duplicate records from cluttering the knowledge graph while preserving useful links to related ideas.

Skill for Claude Code

Written for Claude Code: PostToolUse hook event. Also seen: mentions AGENTS.md.

Part of the mindgap plugin — 12 skills shipped together

Good fit Use it after reading a technical paper from arXiv or a local PDF to check relevance, find related nodes, and add the paper with evidence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/grburgess/mindgap/paper-to-mindmap
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add grburgess/mindgap --skill paper-to-mindmap
Clone the repo
git clone --depth 1 https://github.com/grburgess/mindgap

Made for: Claude Code.

Or install mindgap, the plugin that ships this one along with the rest of its 12 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for paper-to-mindmap

README.md
[![agentmods](https://agentmods.dev/badge/skills/grburgess/mindgap/paper-to-mindmap.svg)](https://agentmods.dev/skills/grburgess/mindgap/paper-to-mindmap)
Your own site
<a href="https://agentmods.dev/skills/grburgess/mindgap/paper-to-mindmap"><img src="https://agentmods.dev/badge/skills/grburgess/mindgap/paper-to-mindmap.svg" alt="Measured on agentmods" height="20"></a>
Per session 113 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,108 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00113 $0.01108
Opus 5 $0.00056 $0.00554
Sonnet 5 $0.00023 $0.00222
Haiku 4.5 $0.00011 $0.00111

Measured 3d ago against content hash 115d485b62b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

paper-to-mindmap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (hook/detect-paper.py, hook/test_detect_paper.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

mindgap-plugin/skills/paper-to-mindmap/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

paper-to-mindmap

Capture a research paper just read into the mindmap knowledge graph: one paper node, linked with evidence to related existing nodes. Distilled from the earlier paper-capture loops. The full ingest protocol is AGENTS.md (binding); the essentials are below.

When this fires

A PostToolUse hook nudges you after a paper read (arXiv / paper host / .pdf). You may also be invoked directly with a URL or arXiv id. Either way, run the relevance gate first — do not ingest reflexively.

Procedure

  1. Relevance gate. Identify the paper (arXiv id / title / URL). Does it teach something relevant to the mindmap's domain or an existing project (ML, CV, remote sensing, property/insurance analytics, MLOps, knowledge graphs, …)? If clearly NOT, stop and report "off-domain, nothing added." Do not pollute the graph.

  2. Read context first. Find existing related nodes before minting:

    • mindmap context "<topic>" and mindmap find "<single salient term>" (single terms are reliable; multi-word context strings often return empty).
    • Dedup: if a node already carries this arXiv id, enrich/upsert that node — never create a duplicate.
  3. Distill the node:

    • type: paper; id: stable kebab-case slug (e.g. softcon-eo-pretraining).
    • Authors (required): lead the body with an **Authors:** <full list>. line — every author, full name as given on the source, comma-separated (e.g. **Authors:** Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick.) — then a blank line, then the prose. The schema has no authors column; this body line IS the author record.
    • body ≥40 words (after the authors line): (a) what the work is/does, (b) why it relates to this graph's domain. Use exact-id [[wiki-links]] to anchors. Do not repeat author names in the prose — the **Authors:** line is the sole author record. Venue/year (e.g. CVPR 2021) may lead the prose; the arXiv id goes in urls, not the prose.
    • urls: [{"label":"arXiv","url":"https://arxiv.org/abs/<id>","kind":"arxiv"}] (use "kind":"web" for non-arXiv sources).
    • confidence: ~0.7. created_by: skill:paper-to-mindmap.

Read the full file on GitHub · 79 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago Changed · +2 lines 115d485b62b0
  2. 7d ago First seen · 77 lines · 113 tokens per session scan A c2bcb7af17a1

Subscribe to this mod's changes

paper-to-mindmap is a skill published in the GitHub repository grburgess/mindgap (0 stars, last pushed yesterday), licensed MIT. It adds 113 tokens to every session and 1,108 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens