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.

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.

It makes scattered project knowledge easier to query and can preserve useful answers for later work.

ask — Skill for Claude Code and Codex by skyllwt: A question-and-answer tool for a wiki knowledge base.

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.

agentmods

npx agentmods add skills/skyllwt/autosci/ask

Any agent

npx skills add skyllwt/AutoSci --skill ask

Clone the repo

git clone --depth 1 https://github.com/skyllwt/AutoSci

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/skyllwt/autosci/ask.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.svg" alt="Measured on agentmods" 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. Scan, not verified.

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.

Model	Per session	Once invoked
Fable 5	$0.00022	$0.02695
Opus 5	$0.00011	$0.01347
Sonnet 5	$0.00004	$0.00539
Haiku 4.5	$0.00002	$0.00269

Measured 4d ago against content hash 49ff6efa7fe2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 4d 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.

4d 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 (1,659 stars, last pushed 5d ago), licensed MIT. It adds 22 tokens to every session and 2,695 once invoked, about $0.0001 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 2026-08-30.

Research Writing and docs

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens