Claude-Code-Scientist narrative-framing

Use when starting any research task to establish WHO, STAKES, and STORY. Creates narrative context that makes abstract instructions concrete.

install
source · Clone the upstream repo
git clone https://github.com/rhowardstone/Claude-Code-Scientist
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/rhowardstone/Claude-Code-Scientist "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/narrative-framing" ~/.claude/skills/rhowardstone-claude-code-scientist-narrative-framing && rm -rf "$T"
manifest: .claude/skills/narrative-framing/SKILL.md
source content

Narrative Framing Skill

Abstract instructions produce abstract outputs. This skill establishes the human context that makes research real.

When to Use

  • At the START of any research task, BEFORE decomposing into RQs
  • When research feels aimless or unfocused
  • When outputs are technically correct but feel hollow
  • Before synthesis to remember WHY this matters

The Three Questions

Before any research work, explicitly answer:

1. WHO is this for?

Not "the scientific community" - be specific:

  • "A graduate student trying to decide which doublet detector to use for their PBMC data"
  • "A drug discovery team evaluating whether to add ambient correction to their pipeline"
  • "A methods developer looking for gaps in the literature to address"
  • "Gulnaz, who asked this specific question"

Even if hypothetical, name them. The name creates accountability.

2. What are the STAKES?

What happens if this research is wrong or incomplete?

  • "They waste 2 weeks on a tool that doesn't work for their data"
  • "They miss a key preprocessing step and publish incorrect cell types"
  • "They solve a problem that's already been solved"
  • "They miss the deadline for the grant application"

Make the consequences concrete. Abstract stakes produce abstract work.

3. What STORY are we telling?

Research is narrative. What's the arc?

  • "Methods X and Y claim to solve problem P. We test them head-to-head and find X wins, but only under conditions C."
  • "The field assumes A→B ordering doesn't matter. We show it does, with quantified impact."
  • "Everyone uses method M. We find it fails catastrophically on data type D."

The story shapes what's important. Without it, everything seems equally relevant.

Example Framing

Research Goal: "Benchmark doublet detection methods"

Before Narrative Framing:

  • Just list tools and metrics
  • Report numbers without interpretation
  • No clear recommendation

After Narrative Framing:

WHO: "Maya, a postdoc running her first large scRNA-seq experiment (50K cells, 10 samples multiplexed with HTOs). She needs to choose a doublet detector TODAY because the deadline is Friday."

STAKES: "If she picks the wrong tool:

  • False positives: She loses rare cell types she spent months trying to capture
  • False negatives: Doublets contaminate her trajectory analysis, invalidating her main result
  • Wrong threshold: She either over-filters (loses data) or under-filters (keeps artifacts)"

STORY: "Maya has heard Scrublet is 'standard' but Solo is 'better'. We test both on data LIKE hers and discover: Scrublet is conservative (won't hurt you but misses doublets), Solo is aggressive (catches more but kills some real cells). The RIGHT choice depends on what Maya fears more - and we'll tell her which based on HER priorities."

Result: The same benchmark, but now every figure has PURPOSE. The discussion isn't "here are numbers" but "here's what Maya should do."

Integration with Research Director

When spawning Goal Decomposition, include:

## Narrative Context

**Who:** [Name and situation]
**Stakes:** [What happens if we get this wrong]
**Story:** [The arc we're following]

Now decompose this research goal into 3-8 research questions...

Checklist

Before starting research:

  • Named the person this is for (even if fictional)
  • Articulated concrete negative consequences
  • Wrote a one-sentence story arc
  • Framing documented in $SESSION_DIR/world_model.json

Why This Works

The Local Craig session that produced rigorous results was working "for" a specific context. The K-Dense session that produced beautiful outputs was working for "K-Dense Web" as a brand. My pipeline test that had a citation error? Just "testing the pipeline."

The narrative is the discipline.

When you're writing for Maya who needs results by Friday, you double-check the DOI. When you're "just running a benchmark," you don't.

Anti-Pattern: Skipping Framing

"I don't have time for narrative framing, let me just run the analysis."

This is exactly when you NEED it. The 2 minutes spent on framing saves hours of aimless exploration and produces outputs with actual recommendations instead of "results vary."

Embody the role. The narrative matters.