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AutoSci: Skill for Claude Code

.claude/skills/ingest/SKILL.md

ingest is a skill for Claude Code from skyllwt/AutoSci. It costs 69 tokens per session (5,345 once invoked), scanned A, original, MIT.

A paper-ingestion workflow for a wiki that turns a research paper into linked pages for the paper, its concepts, methods, and people.

In plain words
What is it for?
Use it with an arXiv URL, local TeX source, or PDF to create wiki entities and cross-references. It supports research papers, including papers hosted on arXiv, a research-paper website.
Why use it?
It removes much of the manual work of adding a paper and connecting its related knowledge in the wiki.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: reads .claude/ paths; mentions subagents.

This is skyllwt/AutoSci's own configuration. It tells Claude Code how to work on AutoSci itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything AutoSci configures →

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is # .venv is gitignored, so a subagent whose cwd is ../.worktrees/<branch>/.

not rated 1.7krepo 1mo ago A scan SkillSpector: pass 69 tokens original MIT
View source ↗ skyllwt/AutoSci
About the project

AutoSci is an AI research platform organized around a wiki, with an agent that supports stages of scientific work such as reading, experimentation, writing, and retaining knowledge across projects. It is for people building or using AI-assisted research workflows, with Claude Code, Codex, and OpenCode adaptations available. The catalogue add-ons extend those agent-specific workflows.

skyllwt/AutoSci · 1,706 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to skyllwt/AutoSci. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/skyllwt/AutoSci/main/.claude/skills/ingest/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/skyllwt/AutoSci

Made for: Claude Code.

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 ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/skyllwt/autosci/ingest/github.svg)](https://agentmods.dev/skills/skyllwt/autosci/ingest)
Your own site
<a href="https://agentmods.dev/skills/skyllwt/autosci/ingest"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ingest/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ingest

Your own site · 80×15
<a href="https://agentmods.dev/skills/skyllwt/autosci/ingest"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,345 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00069 $0.05345
Opus 5.5 $0.00028 $0.02138
Sonnet 5.5 $0.00014 $0.01069
Haiku 4.5 $0.00007 $0.00534

Measured 2mo ago against content hash 05308aaad7e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ingest 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 2mo ago.

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.

.claude/skills/ingest/SKILL.md · 300 lines

How it starts

The opening of the file, as written. The whole thing — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/ingest

Turn one paper into a fully wired set of wiki pages. Emit well-formed entities and correct cross-references; leave semantic audits (backlink symmetry, dangling nodes, field-value policing) for /check.

Use these local references on demand:

  • references/pdf-preprocessing.md — arXiv-ID recovery, tex fetching, prepare-paper handoff for direct PDF drops
  • references/dedup-policy.md — merge-vs-create decision rule for concepts and methods, and the line that separates /ingest shape checks from /check semantic audits
  • references/cross-references.md — forward/reverse link matrix and paper-to-paper edge-type selection
  • references/init-mode.md — manifest-driven handoff from /init and parallel-safety conventions
  • references/error-handling.md — source parse, API, and slug-collision fallbacks

Open runtime/schema/entities.yaml for frontmatter field definitions and runtime/templates/{kind}.md.tmpl for body section structure. For index.md, log.md, and graph/ shapes, see runtime/schema/conventions.yaml and runtime/schema/edges.yaml.

Inputs

  • source: one of — arXiv URL (e.g. https://arxiv.org/abs/2106.09685), local .tex, local .pdf, or a canonical_ingest_path handed off by /init via .checkpoints/init-sources.json(see references/init-mode.md)
  • --discover (optional, default off): after the final report, invoke /discover --anchor <this-paper's-arxiv-id> and append the shortlist to the report as "Related papers you may want to ingest next". Never auto-ingests the suggestions. Skipped automatically in INIT MODE. Treat this as a user-owned flag: do not set it based on repo state.
  • --visualize (optional, default off): after Step 7 rebuild, regenerate Canvas visualization artifacts via tools/visualize.py generate-canvas. Skipped automatically in INIT MODE — the parent /init handles visualization once at fan-in. Treat this as a user-owned flag: do not set it based on repo state. (The interactive web Graph view lives in the SPA at app/modules/graph.js, served by tools/serve.py; it reads wiki/graph/ live and needs no per-ingest regeneration.)

Read the full file on GitHub · 300 lines

Files

What ships with it

5 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. 2mo ago First seen · 300 lines · 69 tokens per session scan A 05308aaad7e0

Subscribe to this mod's changes

ingest is a skill published in the GitHub repository skyllwt/AutoSci (1,706 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 5,345 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-30.

DocumentsResearch
Related

ingest compared with skills from other repositories

ingest next to 6 skills from other repositories
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ingestskyllwt/AutoSciSkill1.7k1mo agoMITA69Claude Code
orbit-notionnexu-io/open-designSkill106ktodayApache-2.0A117Claude Code, Codex
instrument-data-to-allotropeanthropics/knowledge-work-pluginsSkill35k2d agoApache-2.0A123Claude Code
apify-youtube-transcript-scrapersickn33/agentic-awesome-skillsSkill49kyesterdayMITA48Claude Code
legal-holdanthropics/claude-for-legalSkill9.9k3d agoApache-2.0A68Claude Code
ppt-reviewOrkas-AI/OrkasSkill9.0ktodayMITA2Claude Code, Codex
feishuHmbown/CodeWhaleSkill41k5d agoMITA33Claude Code, Codex