Scientific Brainstorming
spacering-net/codeg
Creative research ideation and exploration. An agent skill from spacering-net/codeg.
Official agent skill
by aws-samples in aws-samples/amazon-bedrock-agents-healthcare-lifesciences
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selection --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .claude/skills/scientific-problem-selection && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "scientific-problem-selection" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection into .claude/skills/scientific-problem-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scientific-problem-selection", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selectionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .agents/skills/scientific-problem-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "scientific-problem-selection" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection into .agents/skills/scientific-problem-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scientific-problem-selection", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .cursor/skills/scientific-problem-selection && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "scientific-problem-selection" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection into .cursor/skills/scientific-problem-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scientific-problem-selection", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git --path agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .gemini/skills/scientific-problem-selection && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "scientific-problem-selection" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection into .gemini/skills/scientific-problem-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scientific-problem-selection", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selectionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .github/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .github/skills/scientific-problem-selection && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "scientific-problem-selection" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection into .github/skills/scientific-problem-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scientific-problem-selection", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws-samples/amazon-bedrock-agents-healthcare-lifesciences scientific-problem-selection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection .opencode/skills/scientific-problem-selection && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "scientific-problem-selection" agent skill from https://github.com/aws-samples/amazon-bedrock-agents-healthcare-lifesciences/tree/main/agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection into .opencode/skills/scientific-problem-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "scientific-problem-selection", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
scientific-problem-selectionThis skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions.
Scientific Problem Selection is an agent skill from aws-samples/amazon-bedrock-agents-healthcare-lifesciences, published by the product's own GitHub organization. This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what…
Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files (for example `references/01-intuition-pumps.md`, `references/02-risk-assessment.md` and `references/03-optimization-function.md`).
It sits in Research & Science, covering Brainstorming and Hypothesis generation. The licence is Apache-2.0.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 9960565. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Scientific Problem Selection loads about 2.8k tokens when it runs, and up to ~26k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 1,215 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from aws-samples/amazon-bedrock-agents-healthcare-lifesciences at commit 9960565, republished under its Apache-2.0 licence (© aws-samples). 1,215 words, ~2,792 tokens.
.claude/skills/scientific-problem-selection/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.A conversational framework for systematic scientific problem selection based on Fischbach & Walsh's "Problem choice and decision trees in science and engineering" (Cell, 2024).
Present users with three entry points:
1) Pitch an idea for a new project — to work it up together
2) Share a problem in a current project — to troubleshoot together
3) Ask a strategic question — to navigate the decision tree together
This conversational entry meets scientists where they are and establishes a collaborative tone.
Ask: "Tell me the short version of your idea (1-2 sentences)."
After the user shares their idea, return a quick summary (no more than one paragraph) demonstrating understanding. Note the general area of research and rephrase the idea in a way that highlights its kernel—showing alignment and readiness to dive into details.
Then ask for more detail: "Now give me a bit more detail. You might include, however briefly or even say where you are unsure:
From there, guide the user through the early stages of problem selection and evaluation:
See references/01-intuition-pumps.md, references/02-risk-assessment.md, references/03-optimization-function.md, and references/04-parameter-strategy.md for detailed guidance.
Ask: "Tell me a short version of your problem (1-2 sentences or whatever is easy)."
After the user shares their problem, return a quick summary (no more than one paragraph) demonstrating understanding. Note the context of the project where the problem occurred and rephrase the problem—highlighting its core essence—so the user knows the situation is understood. Also raise additional questions that seem important to discuss.
Then ask: "Now give me a bit more detail. You might include, however briefly:
From there, guide the user through troubleshooting and decision tree navigation:
Always include workarounds that might be useful whether or not the problem can be fixed easily.
See references/05-decision-tree.md, references/06-adversity-planning.md, references/07-problem-inversion.md, and references/04-parameter-strategy.md for detailed guidance.
Ask: "Tell me the short version of your question (1-2 sentences)."
After the user shares their question, return a quick summary (no more than one paragraph) demonstrating understanding. Note the broader context and rephrase the question—highlighting its crux—to confirm alignment with their thinking.
Then ask: "Now give me a bit more detail. You might include, however briefly:
From there, draw on the specific modules from the problem choice framework most appropriate to the question:
See the complete reference materials in the references/ folder.
Problem Choice >> Execution Quality
Even brilliant execution of a mediocre problem yields incremental impact. Good execution of an important problem yields substantial impact.
Scientists typically spend:
This imbalance limits impact. These skills help invest more time choosing wisely.
For Evaluating Ideas:
Skills help move ideas rightward (more feasible) and upward (more impactful).
| Skill | Purpose | Output | Time |
|---|---|---|---|
| 1. Intuition Pumps | Generate high-quality research ideas | Problem Ideation Document | ~1 week |
| 2. Risk Assessment | Identify and manage project risks | Risk Assessment Matrix | 3-5 days |
| 3. Optimization Function | Define success metrics | Impact Assessment Document | 2-3 days |
| 4. Parameter Strategy | Decide what to fix vs. keep flexible | Parameter Strategy Document | 2-3 days |
| 5. Decision Tree Navigation | Plan decision points and altitude dance | Decision Tree Map | 2 days |
| 6. Adversity Response | Prepare for crises as opportunities | Adversity Playbook | 2 days |
| 7. Problem Inversion | Navigate around obstacles | Problem Inversion Analysis | 1 day |
| 8. Integration & Synthesis | Synthesize into coherent plan | Project Communication Package | 3-5 days |
| 9. Meta-Framework | Orchestrate complete workflow | Complete Project Package | 1-6 weeks |
SKILL 1: Intuition Pumps
| (generates idea)
v
SKILL 2: Risk Assessment
| (evaluates feasibility)
v
SKILL 3: Optimization Function
| (defines success metrics)
v
SKILL 4: Parameter Strategy
| (determines flexibility)
v
SKILL 5: Decision Tree
| (plans execution and evaluation)
v
SKILL 6: Adversity Planning
| (prepares for failure modes)
v
SKILL 7: Problem Inversion
| (provides pivot strategies)
v
SKILL 8: Integration & Communication
| (synthesizes into coherent plan)
v
SKILL 9: Meta-Skill
(orchestrates complete workflow)Detailed skill documentation is available in the references/ folder:
| File | Content | Search Patterns |
|---|---|---|
01-intuition-pumps.md | Generate research ideas | Intuition Pump #, Trap #, Phase [0-9] |
02-risk-assessment.md | Risk identification | Risk.*1-5, go/no-go, assumption |
03-optimization-function.md | Success metrics | Generality.*Learning, optimization, impact |
04-parameter-strategy.md | Parameter fixation | fixed.*float, constraint, parameter |
05-decision-tree.md | Decision tree navigation | altitude, Level [0-9], decision |
06-adversity-planning.md | Adversity response | adversity, crisis, ensemble |
07-problem-inversion.md | Problem inversion strategies | Strategy [0-9], inversion, goal |
08-integration-synthesis.md | Integration and synthesis | narrative, communication, story |
09-meta-framework.md | Complete workflow | Phase, workflow, orchestrat |
Fischbach, M.A., & Walsh, C.T. (2024). "Problem choice and decision trees in science and engineering." Cell, 187, 1828-1833.
Based on course BIOE 395 taught at Stanford University.
© aws-samples, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 10 other files (references) in agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection of aws-samples/amazon-bedrock-agents-healthcare-lifesciences.
Open the folder on GitHubat commit 9960565
We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in aws-samples/amazon-bedrock-agents-healthcare-lifesciences, which our catalogue first saw on October 7, 2026.
Scientific Problem Selection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Scientific Problem Selection this skillaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | 4 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Scientific Brainstormingspacering-net/codeg | 3.8k | 14 repos | ~2k | Automated safety check: Pass | MIT | |
| Scientific BrainstormingOleafly/Oleafly | 205 | 2 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Research IdeationGalaxy-Dawn/claude-scholar | 5.7k | 3 repos | ~2.4k | Automated safety check: Pass | MIT | |
| News to Research Idea BriefingOpenLAIR/dr-claw | 1.2k | — | ~1.3k | Automated safety check: Notes | Custom licence | |
| Academic GrillExekiel179/psyclaw | 103 | — | ~2k | Automated safety check: Pass | MIT |
spacering-net/codeg
Creative research ideation and exploration. An agent skill from spacering-net/codeg.
Oleafly/Oleafly
Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs.
Galaxy-Dawn/claude-scholar
This skill should be used when the user asks to "brainstorm research ideas", "use 5W1H framework", "identify research gaps", "conduct gap analysis", "start research project", "conduct literature…
OpenLAIR/dr-claw
Clusters the latest news-feed results by topic and writes a briefing of research idea seeds with citations, plus a structured seeds file, without crawling new sources.
Exekiel179/psyclaw
Stress-test an academic research question, proposal, study design, analysis plan, manuscript claim, review protocol, or AI research project through a one-question-at-a-time interview until its…
Orchestra-Research/AI-Research-SKILLs
Offers ten ideation frameworks for exploring new research directions, stress-testing half-formed ideas and finding gaps when you are stuck or changing fields.
aws-samples/amazon-bedrock-agents-healthcare-lifesciences
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Categories
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Scientific Problem Selection is an agent skill from aws-samples/amazon-bedrock-agents-healthcare-lifesciences, published by the product's own GitHub organization. This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions.
Scientific Problem Selection fits situations like: users ask to pitch a new research idea; work through a project problem; evaluate project risks; plan research strategy.
Run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a claude-code`. Or copy the skill folder (agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection in aws-samples/amazon-bedrock-agents-healthcare-lifesciences) into .claude/skills/scientific-problem-selection in your project. Claude Code loads it when a task matches its description.
Run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a codex`. Or copy the skill folder (agents_catalog/36-C4LS-example-agent/C4LS/src/skills/scientific-problem-selection in aws-samples/amazon-bedrock-agents-healthcare-lifesciences) into .agents/skills/scientific-problem-selection in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws-samples/amazon-bedrock-agents-healthcare-lifesciences --skill scientific-problem-selection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scientific-problem-selection, .gemini/skills/scientific-problem-selection, .github/skills/scientific-problem-selection and .opencode/skills/scientific-problem-selection in your project.
SKILL.md names no scripts, command-line tools or credentials: Scientific Problem Selection is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Scientific Problem Selection is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 23k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Scientific Problem Selection: Scientific Brainstorming (spacering-net/codeg, 3.8k stars), Scientific Brainstorming (Oleafly/Oleafly, 205 stars), Research Ideation (Galaxy-Dawn/claude-scholar, 5.7k stars) and News to Research Idea Briefing (OpenLAIR/dr-claw, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
aws-samples (a GitHub organization, an official publisher) maintains it in aws-samples/amazon-bedrock-agents-healthcare-lifesciences, which has 274 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 1, 2026.
Source: aws-samples/amazon-bedrock-agents-healthcare-lifesciences on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.