ideate

ideate is a skill for Claude Code, Codex from skyllwt/AutoSci. It costs 23 tokens per session (7,420 once invoked), scanned A, original, MIT.

A five-phase pipeline for generating and testing research ideas. It scans existing knowledge and current research, creates ideas with two models, checks them, records them in a wiki, and can run pilot experiments.

In plain words
What is it for?
Use it to explore a research direction, generate and filter ideas, assess feasibility and novelty, write surviving ideas to wiki pages, and optionally run pilots.
Why use it?
It organizes research discovery and keeps rejected ideas with their reasons, reducing repeated work and making the process traceable.

Skill for Claude CodeCodex

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/ideate
Any agent
npx skills add skyllwt/AutoSci --skill ideate
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.

agentmods badge for ideate

README.md
[![agentmods](https://agentmods.dev/badge/skills/skyllwt/autosci/ideate.svg)](https://agentmods.dev/skills/skyllwt/autosci/ideate)
Your own site
<a href="https://agentmods.dev/skills/skyllwt/autosci/ideate"><img src="https://agentmods.dev/badge/skills/skyllwt/autosci/ideate.svg" alt="Measured on agentmods" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,420 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.

ModelPer sessionOnce invoked
Fable 5 $0.00023 $0.07420
Opus 5 $0.00012 $0.03710
Sonnet 5 $0.00005 $0.01484
Haiku 4.5 $0.00002 $0.00742

Measured 4d ago against content hash 9fb2d36c0048, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ideate 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/ideate/SKILL.md · 557 lines

How it starts

The opening of the file, as written. The whole thing — 557 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/ideate

Generates high-quality research ideas through a 5-phase pipeline, grounded in the wiki knowledge base and external search. Phase 1 scans the research landscape (wiki + WebSearch + S2), Phase 2 runs a dual-model brainstorm (Claude + Review LLM independently), Phase 3 applies first-pass filter + deep validation (feasibility, novelty, review), Phase 4 writes ideas to the wiki (including eliminated ideas, with failure reasons recorded as anti-repetition memory), Phase 5 runs pilot experiments on surviving ideas (idea pages already exist) and updates results.

Inputs

  • direction (optional): research direction, keywords, or specific problem description. If omitted, automatically selects the most valuable direction from open_questions.md.
  • --max-ideas N (optional, default 3): maximum number of ideas to write to the wiki
  • --skip-validation: skip Phase 3 Step 2 deep validation (skip /novelty and /review; fast mode: first-pass filter only)
  • --skip-pilot: skip Phase 5 pilot experiments (fast mode: Phase 1–4 only)
  • --auto: fully automatic mode, no pause for user confirmation (used when called by /research)

Outputs

  • wiki/ideas/{slug}.md — one page per idea (status: proposed), covering both top ideas and eliminated ideas
  • wiki/graph/edges.jsonl — new idea → concept/topic relationship edges
  • wiki/graph/context_brief.md — rebuilt compressed context
  • wiki/graph/open_questions.md — rebuilt knowledge gap map
  • IDEA_REPORT (printed to terminal) — pipeline execution summary, ranked results, novelty scores

Wiki Interaction

Reads

  • wiki/graph/context_brief.md — global context
  • wiki/graph/open_questions.md — knowledge gaps, drives idea direction
  • wiki/ideas/*.md — existing ideas, especially status=failed ideas and their failure_reason (banlist)
  • wiki/papers/*.md — existing paper methods and results
  • wiki/concepts/*.md — technical concepts, find cross-domain combination opportunities
  • wiki/methods/*.md — reusable methods, scope candidate inspirations
  • wiki/topics/*.md — research direction maps, SOTA and open problems (including ### Known gaps and ### Methodological gaps)
  • wiki/experiments/*.md — existing experiment results, avoid duplication

Read the full file on GitHub · 557 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. 4d ago First seen · 557 lines · 0 tokens per session scan A 9fb2d36c0048

Subscribe to this mod's changes

ideate is a skill published in the GitHub repository skyllwt/AutoSci (1,659 stars, last pushed 5d ago), licensed MIT. It adds 23 tokens to every session and 7,420 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.

Related

Other skills, from other repositories

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