Good Story

Good Story

Motto: "story is all you need."

Use this general scientific writing skill to extract the strongest research story that the evidence can honestly support, then show why that story works. Treat "story" as the organizing logic of a scientific or scholarly claim, not as decoration or hype. Story is all you need; truth is the boundary condition.

Core Rule

A good scientific story is a resolved tension:

field belief or need -> important gap -> decisive approach -> surprising/clarifying evidence -> new claim/model -> consequence for the field

Never let the story outrun the evidence. If a beautiful story needs data the user has not shown, label it as a candidate story and name the missing proof.

Source Depth Rule

When the user asks for the story of a specific paper, report, dataset, web page, or public material, do not diagnose the story from the title, abstract, press release, citation metadata, or memory alone.

Use the deepest accessible source before producing a story card:

1. Prefer the full text, including methods, results, figures, discussion, limitations, and supplementary notes when they matter.
2. If the full text is not accessible, say what was accessible and label any output as a provisional source-depth-limited read.
3. If only a title, DOI, abstract, press release, or citation page is available, do not invent the evidence ladder. Ask for the full material or give a reading plan instead of a final story diagnosis.
4. When using public sources, paraphrase the material and cite links; do not paste long source passages.

When frameworks conflict, use this priority order:

1. Evidence integrity and transparent reporting.
2. Claim calibration: what the data actually supports.
3. Research argument: why the claim matters in the field.
4. Narrative arc: how the reader travels from problem to resolution.
5. Style and memorability.

Lower layers may sharpen higher layers but may not override them.

Audience Contract Rule

A good paper story must satisfy three different readers at once:

• The editor needs to see why the contribution matters and why the paper deserves attention now.
• The reviewer needs to see that every important claim is supported by evidence, logic, qualifiers, and answers to likely objections.
• The field reader needs to understand and remember the one thing they can now think, measure, predict, build, or do differently.

When diagnosing a story, do not optimize only for drama or only for completeness. Make the story easy to notice, easy to evaluate, and hard to misread.

Project Scope

good-story is a general research-writing skill, not an ecology skill or a fixed list of supported domains. Its core story logic can be used by researchers from any evidence-based field once the field's materials, evidence hierarchy, claim verbs, audience contract, and review risks are calibrated.

Domain examples are domain calibration packs (领域校准包), not skill boundaries. Ecology, remote sensing, AI4Science, social science, biomedical research, and other examples tune the shared story grammar to a field's vocabulary, stakes, evidentiary standards, causal norms, review risks, and legitimate scope of implication. They do not change the core rule, and they must not smuggle field-specific assumptions into another domain.

Quick Workflow

1. Inventory the material.

   • Extract the strongest claims, datasets, methods, contrasts, negative results, controls, limitations, and audience.
   • Separate direct evidence from interpretation, speculation, and background.
   • If a manuscript, outline, figure list, or results table is present, map each part to its current narrative job.
   • Name the audience contract: what an editor, reviewer, and field reader each need from the paper.

2. Find the story candidates.

   • Identify the protagonist: phenomenon, mechanism, method, organism, dataset, theory, or field problem.
   • Identify the antagonist: bottleneck, contradiction, dogma, missing mechanism, noisy evidence, scale barrier, or practical need.
   • Identify the turn: the experiment, comparison, model, or observation that changes what the reader can believe.
   • Draft 2-4 possible story spines before choosing one.

3. Choose the winning story.

   • Prefer the story with the clearest central contribution, strongest evidence chain, broadest honest consequence, and easiest retelling.
   • Downgrade stories that require hidden assumptions, too many equal contributions, chronological lab-history logic, or an audience the evidence cannot satisfy.

4. Build the paper around the story.

   • Title: the distilled central contribution.
   • Abstract: context, gap, approach, key evidence, claim, implication.
   • Introduction: make the reader care, narrow to the gap, show why the gap is solvable now.
   • Results: a sequence of claim-bearing steps, each supported by a figure or analysis.
   • Discussion: answer the gap, state what changed, define scope, handle limitations, and point to the next useful question.

5. Explain why it is good.

   • Name the tension it resolves.
   • Name the evidence that makes the resolution believable.
   • Name the audience it activates.
   • Name how the story serves the editor, the reviewer, and the field reader.
   • Name the retellable sentence a reader could carry away.
   • Name any fragility: missing controls, weak causal link, narrow generality, or overclaim risk.

Story Diagnostics

Use these tests aggressively:

• One-sentence test: Can the paper be retold in one sentence without losing its point?
• So-what test: Does the claim change what readers think, measure, predict, build, or do?
• Gap-lock test: Do the results answer the exact gap introduced?
• Evidence ladder test: Does each figure make the next claim more believable?
• Antagonist test: Is there a real obstacle, contradiction, or uncertainty, not just "little is known"?
• Causality test: Are causal words backed by causal evidence?
• Scope test: Is the claim as general as the evidence, but no more?
• Audience contract test: Would an editor see stakes, a reviewer see warranted logic, and a reader remember the central contribution?
• Autobiography test: Is the manuscript telling how the authors did the project, or how readers should come to believe the conclusion?
• Narrative risk test: Has a clean story hidden negative results, limiting evidence, alternative explanations, or uncertainty the reader needs?
• Memory test: What phrase would a reader remember one year later?
• Time-layer test: Is this paper strong because of modern framing craft, because of a naturally powerful classic problem, or because it has both?