# Good Story

> Use when the user asks to find, sharpen, evaluate, rewrite, or explain the story in scientific or scholarly materials across disciplines, including manuscripts, grants, paper outlines, abstracts, figures, results, discussions, cover letters, research pitches, high-impact writing, journal fit, significance, novelty, mechanism, paper logic, or why a result matters.

- Skill: `lingwei-zheng/good-story` (Agent Skill, multi-file: 46 files)
- Install (CLI): `npx skillmds@latest add lingwei-zheng/good-story`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lingwei-zheng/good-story/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: lingwei-zheng (https://skillmd.com/u/lingwei-zheng)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/lingwei-zheng/good-story

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# 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.

For author-side work, read `../shared/advantage-led-research-narrative.md`.
Organize the paper around its leading advantage and the arena in which that
advantage is meaningful. Keep reviewer-risk analysis internal unless the user
asks for it or a material defect changes the central claim or submission route.

Use the Research Continuity record in
`../shared/advantage-led-research-narrative.md`: preserve question, advantage
state, fair comparison, claim/experiment IDs, evidence version, and retellable
sentence across stages. Update only fields affected by the current task; reuse
project records rather than starting another ledger.

## 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 an inspectable path from each important claim to its
  evidence and necessary conditions; the author does not need to pre-answer
  every imaginable objection in the main narrative.
- 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.

Use this skill to diagnose how a story fits an audience or journal family. When
the user asks for concrete journal names, a ranked submission list, or a
journal ladder, route that decision through `academic-advisor`; it loads the
user's local journal reference and verifies current journal information.

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, and audience.
   - Identify the leading advantage: new capability, clearer mechanism, lower
     cost, better scalability, practical value, or a credible incremental gain.
   - Triage negative results, controls, and limitations by materiality. Keep
     prominent only those that change the central claim, its scope, or required
     reporting.
   - 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.
   - Define the winning arena: task, comparator, metric, population, scale, and
     application setting in which the contribution is both useful and fairly
     evidenced.
   - Prefer the story with the clearest leading advantage, 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, explain where and why the advantage matters,
     then state only the boundaries that materially change interpretation.
   - Assign each experiment, analysis, and figure one argument job: establish,
     explain, demonstrate, distinguish, or bound the central advantage.

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 a material constraint only when it changes the claim, interpretation,
     or next decision. Do not append a generic weakness inventory.

## 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?
- Advantage test: What does this paper do better, newly, more cheaply, more
  clearly, or more usefully, and under which conditions?
- Battlefield test: Are the task, comparator, metric, and setting fair and
  aligned with that advantage?
- Experiment-job test: Does every prominent analysis strengthen, explain,
  demonstrate, distinguish, or bound the central claim?
- 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?
- Materiality test: Has the story omitted evidence that would overturn or
  materially narrow the central claim? Do not elevate non-material checks into
  the main narrative merely to anticipate criticism.
- 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?

## Default Output

When the user gives materials and asks for a story, use the stable handoff in
`references/handoff-contract.md`. Localize visible section labels to the user's
language; for Chinese, use the heading translations in
`references/terminology-style.md`.

1. `Leading advantage`: the value point that carries the paper.
2. `Winning arena`: the task, comparator, metric, population, scale, or setting
   in which that advantage matters.
3. `Best story`: one sharp paragraph.
4. `Story spine`: 5-7 beats from problem to consequence.
5. `Evidence and experiment-job map`: claim -> evidence -> argument job ->
   material condition, when one exists.
6. `Rewrite targets`: title, abstract, section order, figure order, or key paragraphs as relevant.

Add `Material constraint` only when it changes the central claim, route, or
required reporting. Add a reviewer-risk section only when the user explicitly
asks for reviewer resistance, rebuttal preparation, or pre-submission stress
testing.

For early projects, include alternate story candidates and rank them. For nearly finished manuscripts, focus on diagnosis and surgical edits.

## Reference Loading

Load only what is needed:

- Read `references/story-principles.md` when sharpening the story logic, abstract, introduction, results sequence, or discussion.
- Read `references/story-learning-strategy.md` when learning from published papers, comparing recent and classic papers, building an exemplar corpus, or diagnosing whether a paper's story strength comes from framing craft, problem choice, or decisive evidence.
- Read `references/terminology-style.md` when answering in Chinese, translating story diagnostics, or when output would otherwise mix English writing-framework jargon into Chinese prose.
- Read `references/framework-governance.md` when multiple writing frameworks conflict, when the story risks hype, or when the user explicitly emphasizes truth, evidence, limitations, overclaiming, or reviewer resistance.
- Read `references/overclaim-calibration.md` when evaluating whether a story is too strong, rewriting claims, preparing abstracts, cover letters, discussions, rebuttals, or high-impact journal framing.
- Read `references/handoff-contract.md` when another skill or later work block
  must consume the story diagnosis without reinterpreting it.
- Read `references/cross-domain-transfer.md` when users from any field need to use the skill well, when using the skill outside an already calibrated domain, comparing fields, building a new domain calibration pack, or explaining how the same story logic applies to examples such as ecology, remote sensing, AI4Science, social science, biomedical research, materials, geoscience, or humanities.
- Read `references/ecology-story-exemplars.md` when the user asks for ecology examples, biodiversity/ecosystem-function stories, conservation ecology stories, or an ecology calibration pack.
- Read `references/exemplar-paper-patterns.md` when the user asks for high-impact examples, paper archetypes, or why famous papers are memorable.
- Read `references/source-map.md` when citing the public writing guidance behind this skill or expanding the corpus.
- Read `examples/before-after.md` when the user asks what the skill does in practice, wants examples, before/after rewrites, abstract diagnosis, figure-order examples, or examples of calibrated claims.
- Read `examples/field-mini-cases.md` when the user wants field-specific examples, cross-domain examples, ecology, remote sensing, AI4Science, social science, biomedical, or other domain mini-cases.

## Common Mistakes

- Mistaking chronology for story: lab order is rarely reader logic.
- Mistaking importance for stakes: "important topic" is not a gap unless something consequential is unknown or blocked.
- Mistaking data volume for evidence: more panels do not help unless each panel moves belief.
- Mistaking novelty for contribution: new is not enough; the result must change a claim, method, model, or decision.
- Mistaking speculation for implication: implications must be downstream of demonstrated evidence.
- Mistaking smooth prose for story: clarity helps, but the core claim and evidence ladder must work first.

## Style

Be blunt about story strength, but do not humiliate the science. Match the user's language. Prefer concrete story beats over generic writing advice. Use vivid labels for the narrative roles when useful, but keep all scientific claims traceable to evidence supplied by the user or cited sources.

If the user writes in Chinese, answer in polished Chinese by default:

- Use Chinese section headings and Chinese technical terms.
- Translate writing-framework jargon accurately instead of leaving English terms in the main prose.
- Keep English only for paper titles, author names, established acronyms, method names without stable Chinese translation, or when the English term prevents ambiguity.
- When an English term is useful, introduce it once in parentheses after the Chinese term, then use the Chinese term afterward.
- Do not output hybrid labels such as `Best story`, `Story spine`, `Evidence map`, `turn`, `claim`, `caveat`, `overclaim`, or `reviewer` in a Chinese answer unless quoting source text.