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A conversational framework for systematic scientific problem selection, project ideation, troubleshooting, and strategic decision making.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
Strategic scientific problem selection, project ideation, and troubleshooting based on the Fischbach & Walsh framework.
Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
You MUST use this before any creative work (features, products, content, strategy, systems, or behavior changes). Start by classifying what we’re brainstorming, then run thorough one-question-at-a-time discovery, propose 2–3 approaches, and converge on a validated plan/spec.
| name | bio-strategy |
| description | A conversational framework for systematic scientific problem selection, project ideation, troubleshooting, and strategic decision making. |
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 using the Knowledge Base below:
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 using the Knowledge Base below:
Always include workarounds that might be useful whether or not the problem can be fixed easily.
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:
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).
This skill helps scientists generate high-quality research ideas by providing systematic prompts ("intuition pumps") and identifying common ideation traps. Based on the framework that most biological and chemical science projects involve perturbing a system, measuring it, and analyzing the data, this skill guides users through structured ideation that can significantly impact how they spend years of their career.
Research advances generally fall into one of these categories, each with two dimensions:
PERTURBATION
MEASUREMENT
THEORY/COMPUTATION
Understanding which quadrant resonates with the user can help identify their niche and guide ideation.
Before diving into intuition pumps, I should gather context by asking the user:
What is the user's general research area or field? (e.g., immunology, synthetic biology, neuroscience, protein engineering)
What excites the user most about science?
What are the user's existing strengths? (Select all that apply)
Current constraints:
On a scale of 1-5, how would the user rate their current idea?
Based on the user's responses, I should guide them through relevant intuition pumps from this list:
Prompt: Take any one-off perturbation or measurement and make it systematic.
Examples:
Prompt for User: What one-off experiment in your field could become a systematic survey?
Prompt: What are the fundamental limitations of technologies you use? These limitations are opportunities.
Examples:
Prompt for User: What technology limitation frustrates you most? How might you turn that limitation into an opportunity?
Prompt: I can't imagine a future in which we don't have ____, but it doesn't exist yet.
Examples:
Prompt for User: What capability seems inevitable but doesn't exist yet in your field?
Prompt: We understand biological "parts lists" but rarely understand dynamic processes.
Key Insight: Most observations are single-timepoint, single-perturbation format. But biological systems are dynamic—like humans flowing through Grand Central Station or money through financial systems.
Examples:
Prompt for User: What dynamic process in your field do we observe as static snapshots? How might you capture the full temporal or spatial dynamics?
This skill helps scientists systematically identify, quantify, and manage project risk through rigorous assumption analysis. The goal is not to eliminate risk—risk-free projects tend to be incremental—but to name it, quantify it, and work steadily to chip away at it. This skill builds directly on the Problem Ideation Document from Module 1.
"Don't avoid risk; befriend it."
The most important concept in problem choice is the two-axis evaluation:
This skill focuses on the X-axis, helping users move their project rightward through systematic risk analysis.
First, I should gather information about the user's project from Module 1:
Project Summary (from Module 1):
Project Horizon:
Initial Risk Sense:
I should work with the user to list EVERY assumption the project makes from inception through conclusion. Assumptions fall into two categories:
Type A: Assumptions About Biological Reality These are facts about the world that either are or aren't true. They won't change during the project. Examples: New cell types exist; a gene regulates the process; two proteins interact.
Type B: Assumptions About Technical Capability These are about whether technology can do what's needed. These CAN change during the project as methods improve. Examples: A cell type can be isolated; sequencing will generate high-quality data.
I should ask:
For each assumption, I should help the user assign two scores:
Risk Level (1-5 scale):
Time to Test (months): How long before the user will know if this assumption is valid?
Once the complete table is ready, I should analyze the risk profile:
Red Flags to Identify:
This skill helps scientists articulate HOW their project should be evaluated and define what success means. While Module 2 focused on likelihood of success (the X-axis), this skill focuses on impact if successful (the Y-axis).
"Pick the right optimization function."
Different types of projects should be evaluated by different metrics.
First, I should determine what type of project the user is pursuing:
Question 1: What is the primary goal? A. Understand how biology works (fundamental knowledge) B. Enable new experiments or capabilities (tool/technology) C. Solve a practical problem (invention/application) D. Something else
Question 2: What would "success" look like in 3-5 years?
Question 3: Who cares if this succeeds?
Framework 1: Basic Science Axes: How much did we learn? × How general/fundamental is the object of study? Philosophy: A high score on EITHER axis yields substantial impact. You don't need both.
Framework 2: Technology Development Axes: How widely will it be used? × How critical is it for the application? Philosophy: Again, high score on EITHER axis is sufficient.
Critical Rule: A tool that won't be widely used AND isn't critical for an application probably isn't worth building.
This skill helps scientists strategically decide which parameters to fix and which to keep flexible in their project. The paradox: too many fixed parameters creates brittleness, but too few causes paralysis.
"Fix one parameter; let the others float."
Common Parameters:
First, let's identify what's already fixed in your current project idea. For each category, indicate if it's FIXED (must stay) or FLOATING (could change).
Diagnostic Questions: Too Many Fixed Parameters (>2):
Too Few Fixed Parameters (0-1 very broad):
This skill teaches you to move fluidly between execution (Level 1: getting stuff done) and strategic evaluation (Level 2: critical thinking).
"Learn the altitude dance"
Move back and forth frequently between:
Identify:
Recommended Schedule:
At each major branch point, instead of endless troubleshooting:
This skill helps you prepare for inevitable crises and reframe them as opportunities.
"Capitalize on the 'adversity feature'"
Adversity in a project is inevitable AND opportune:
List likely adversity scenarios (technical, biological, competitive, resource). Rate likelihood and impact.
For each high-likelihood failure mode: Question: How could you fix this AND make the project better? Example: Cell type can't be isolated -> Develop new isolation method that works for whole class of cell types.
You're not picking ONE project path—you're picking an ENSEMBLE of possible projects. When adversity strikes, you're not failing—you're discovering which path in the ensemble you're actually on.
Three concrete strategies for navigating around obstacles by reframing problems.
"Turn a problem on its head"
Strategy 1: Unfix Parameters (In Crisis Mode) When to Use: Run-of-the-mill issues. Approach: Let a "sacred" fixed parameter float. Example: Unfix technique -> What else could measure these interactions?
Strategy 2: Comparable Goal Substitution When to Use: Existential threats (can't achieve original goal). Approach: Achieve a different but equally valuable goal. Mindset: "The world needs Y instead, which I CAN do."
Strategy 3: Answer Seeking Question When to Use: End-of-project challenges (interpretation). Approach: You got an answer, but not to your original question. What question DOES your data answer? Mindset: "What interesting question does this answer?"
Synthesizes all previous skills into a coherent project plan and communication strategy.
"Tell a compelling story with your choices"
Story Structure for Your Project:
Orchestrates the complete problem selection process, guiding users through Modules 1-8 in a systematic, iterative way.
