AutoSci: Skill for Claude Code

.claude/skills/ask/SKILL.md

ask is a skill for Claude Code from skyllwt/AutoSci. It costs 22 tokens per session (2,695 once invoked), scanned A, original, MIT.

A question-and-answer tool for a wiki knowledge base. It retrieves relevant pages, combines their information into an answer with citations, and can save that answer back into the wiki.

In plain words
What is it for?
Use it to ask questions about the wiki, request table, timeline, or bullet formats, and optionally save the result as an output, concept, idea, method, or research note.
Why use it?
It makes scattered project knowledge easier to query and can preserve useful answers for later work.

Skill for Claude Code

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

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 runs a file that does not travel with it, so clone the repository first. The line is python3 tools/research_wiki.py rebuild-context-brief wiki/.

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/ask/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 ask

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/skyllwt/autosci/ask"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ask.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,695 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 $0.00022 $0.02695
Opus 5.5 $0.00009 $0.01078
Sonnet 5.5 $0.00004 $0.00539
Haiku 4.5 $0.00002 $0.00269

Measured 2mo ago against content hash 49ff6efa7fe2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ask 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/ask/SKILL.md · 211 lines

How it starts

The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/ask

Ask a question to the wiki knowledge base. The LLM reads context_brief.md for global context, retrieves relevant pages, synthesizes an answer with citations. Good answers can be crystallized back into the wiki — written to outputs/, as new concept pages, or appended to an existing idea/method/output note — so exploration compounds like ingestion does.

Inputs

  • question: natural-language question (e.g. "What is the core difference between LoRA and Adapter?")
  • --crystallize (optional): if specified, crystallize the answer back into the wiki (default: answer only, no write)
  • --format (optional): output format, default markdown, options: table / timeline / bullets

Outputs

  • Always: terminal output of synthesized answer (with [[slug]] citations)
  • If crystallize:
    • wiki/outputs/{query-slug}.md — query result page (default crystallize target)
    • or wiki/concepts/{slug}.md — if the answer reveals a new cross-paper concept
    • or appended to an existing wiki/ideas/{slug}.md / wiki/methods/{slug}.md / wiki/outputs/{slug}.md — if the answer adds a finding to an existing entity
    • updated wiki/graph/edges.jsonl (relationships produced by crystallize)
    • updated wiki/index.md and wiki/log.md

Wiki Interaction

Reads

  • wiki/graph/context_brief.md — global compressed context (ideas, gaps, failed ideas, papers, edges)
  • wiki/index.md — page catalog for locating relevant pages
  • wiki/graph/open_questions.md — open questions, helps identify whether the question touches known gaps
  • wiki/papers/*.md — paper pages relevant to the question
  • wiki/concepts/*.md — concept pages relevant to the question
  • wiki/methods/*.md — method pages relevant to the question
  • wiki/topics/*.md — topic pages relevant to the question
  • wiki/people/*.md — if the question involves specific researchers
  • wiki/ideas/*.md — if the question involves research ideas or failed ideas
  • wiki/experiments/*.md — if the question involves experiment results
  • wiki/Summary/*.md — if the question involves domain-wide landscape

Read the full file on GitHub · 211 lines

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 · 211 lines · 22 tokens per session scan A 49ff6efa7fe2

Subscribe to this mod's changes

ask is a skill published in the GitHub repository skyllwt/AutoSci (1,706 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 2,695 once invoked, about $0.0001 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.

Related

ask compared with skills from other repositories

ask next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
askskyllwt/AutoSciSkill1.7k1mo agoMITA22Claude Code
imagegenopenai/codexSkill133ktodayApache-2.0A113Codex
playwright-climicrosoft/playwrightSkill99kyesterdayApache-2.0A19Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k4d agoMITA200Claude Code, Codex
systematic-debuggingobra/superpowersSkill313k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143ktodayMITA95Claude Code
next-partial-prefetching-adoptionvercel/next.jsSkill143ktodayMITA103Claude Code