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Academic Results Writer

学术 Results 写作、修改与审计技能。根据统计输出、图表、caption 和草稿生成符合发表规范的 Results 段落。覆盖心理学、认知神经科学、睡眠与记忆、VR 实验、EEG/fMRI、心理测量、干预研究、问卷模型、元分析和质性研究等设计。默认输出中文,可选英文及特定期刊风格。支持 Target-pap...

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来源标识:SkillHub/academic-results-writer
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学术 Results 写作、修改与审计技能。根据统计输出、图表、caption 和草稿生成符合发表规范的 Results 段落。覆盖心理学、认知神经科学、睡眠与记忆、VR 实验、EEG/fMRI、心理测量、干预研究、问卷模型、元分析和质性研究等设计。默认输出中文,可选英文及特定期刊风格。支持 Target-pap...

最后校验2026-05-27
来源平台SkillHub
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输入与输出

输入:任务目标、上下文材料、文件路径、约束条件或需要处理的内容。

输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。

示例任务

  • 使用 Academic Results Writer 帮我处理当前任务,并说明需要准备哪些输入。
  • 根据 Academic Results Writer 的说明,先列出使用前的安全检查项。

安装方式

  1. 下载本站提供的 Skill ZIP 并解压。
  2. 把解压后的 Skill 目录放入当前 AI 工具支持的 skills 目录。
  3. 如需在线查看原始内容,可打开 GitHub 的 SKILL.md

在线原始地址:skillhub-academic-results-writer/SKILL.md

风险边界

SkillHub 提供了源站安全报告入口,但本站不替代人工审查。使用前仍需检查权限、外部依赖和敏感数据边界。

SKILL.md 文档介绍

Academic Results Writer (v1.2.1)

Forward-writing companion to paper-results-reverse-engineer v3.0:

  • reverse-engineer: deconstructs published Results structure and writing patterns
  • academic-results-writer: generates Results text from user data in publication-ready style

---

1. When to Use

Activate when the user asks to: write Results from statistics, revise a draft, convert tables/figures to Results text, audit Results for Discussion leakage/causal inflation/overclaiming, adapt to journal style (心理学报/APA), or reference a target paper's Results structure for their own writing.

2. Core Philosophy

1. Results is a reader-guided narrative, not a data dump.

2. Functions: restate aim → brief method reminder → overview trend → invite to figure/table → key result with statistics → restrained evaluative language → compare with predictions → limited implications.

3. Results can include limited interpretation but NOT full Discussion.

4. Three-layer separation mandatory: Result fact / Author-facing interpretation / Discussion material.

5. Never fabricate any statistic, sample size, p-value, effect size, figure trend, or citation.

Supporting-File Loading Policy (Mandatory)

Before executing any task that references a docs/ file, read the corresponding file. The condensed rules in this SKILL.md are summaries; the full validated rule set is in docs/.

Docs reading table — read the file when the trigger condition is met:

| Trigger | Read |

|---------|------|

| Write-from-statistics / any statistical template usage | docs/statistical-templates.md |

| Revise-draft / Revision Mode | docs/revision-mode.md |

| Figure-to-results / table-to-results / figure narrative | docs/figure-table-templates.md |

| Target-paper-style-adaptation | docs/target-paper-adaptation.md |

| Module H bridge workflow | docs/module-h-bridge.md |

| Meta-analysis Results writing | docs/meta-analysis-guardrails.md |

| Sleep EEG / memory / pre-post design Results | docs/sleep-eeg-guardrails.md |

| Journal-style (心理学报 / APA) | docs/journal-style.md |

| Full audit / file-output / completeness check / quality checklist | docs/quality-checklist.md |

Fail-open rule: If the required supporting file cannot be accessed, do NOT claim the full detailed rule set was applied. Continue with the condensed SKILL.md rules and explicitly report: supporting-file unavailable; condensed-rule mode used.

3. Inputs

| Type | Examples |

|------|----------|

| Structured statistics | N, M, SD, SE, CI, r, t, F, β, b, χ², Hedges' g, OR, RR, fit indices, EEG/fMRI/behavioral/VR outputs, qualitative themes |

| Figures / Tables | Screenshots, captions, table content, user-described trends, v3.0 Module D output |

| Rough drafts | User-written Chinese/English/mixed Results drafts |

| v3.0 upstream | Study Profile, Module B/C/D/E from reverse-engineer |

| Target paper | PDF, Results section, captions, figures, v3.0 Module H |

4. Default Output Format

Default: Chinese, standard-depth.

1. 【结果组织建议】

2. 【可直接使用的结果段】

3. 【关键统计报告检查】

4. 【结果与讨论边界提醒】

5. 【可选替代表达】

Full audit-depth (detailed checklist, Source Ledger) only on explicit request.

4.1 File-Output Mode

Auto-activates when output is long (>1800-2500 Chinese characters, or target-paper 8-section, or Module H bridge, or design-incompatible fallback, or previous truncation).

Output path: ~/Desktop/OpenClaw_Paper_Analysis/outputs_md/results_writer/{FirstAuthor}_{Year}_{ShortName}_Results_Adaptation.md

Chat shows only: path + 3-5 core findings + self-check + manual review items. Never paste full long text into chat.

File completeness check: No ...(truncated)..., no TODO/待补充/[填写], all requested sections present. If check fails, patch once; if still failing, report failure in chat.

Full specification: docs/quality-checklist.md

5. Task Router

| User Says | Task Type |

|-----------|-----------|

| "根据统计结果写 Results" | write-from-statistics |

| "润色/修改这段结果" | revise-draft |

| "根据这张表/图写结果段" | table-to-results / figure-to-results |

| "检查结果部分有没有问题" | audit-only |

| "改成心理学报/APA 风格" | journal-style |

| "参考这篇论文的 Results 写法" | target-paper-style-adaptation |

Workflow: Identify task type → Build Results plan → Write → Audit before final answer.

6. Statistical Reporting — Key Guardrails

Templates for all analysis types are in docs/statistical-templates.md. Key guardrails:

  • Correlation ≠ causation. Never write "X 影响 Y" for correlational results.
  • Non-significant ≠ no difference. Never write "证明两组相同" for p > .05.
  • Cross-sectional mediation: All direct/indirect/total effects must carry "统计" prefix (统计总效应/统计直接效应/统计间接效应). Hard self-check.
  • Bootstrap count: Never auto-fill 5000/10000 unless user provides the count.
  • Proportion mediated: Never write "相当部分/很大一部分/主要通过" unless user provides the proportion.
  • ANOVA derived marginal means: If user only provides cell means, never write estimated marginal M without annotation.
  • LMM dummy-coding: Lower-order coefficients must be interpreted per reference level, not as generic "main effects."
  • p > .05–.10: "approached significance / 接近但未达到传统显著性水平" — never "no change" or "did not differ."
  • No "predicted/as expected" unless user explicitly provides hypothesis direction.
  • Figure error bars: Strictly distinguish SD/SE/CI. Never write "标准差参见图" when caption says ±1 SE.
  • No visual judgment without actual image screenshot. Use "根据用户提供的均值" not "从图中可以明显看出".
  • Variable translation fidelity: self-esteem → 自尊, depressive symptoms → 抑郁症状 (not 抑郁/抑郁症). Consistent throughout.
  • p-value format: Never mix p = .021 and p = 0.021 in same output.

Meta-analysis hard-self-check guardrails (output auto-fails if violated):

  • No "校正后效应仍显著" without p-value for adjusted effect
  • No "结果稳健/结论稳定" when I² ≥ 50%
  • No "Q 检验显著,因此选择随机效应模型"

Full meta-analysis rules: docs/meta-analysis-guardrails.md

Sleep EEG guardrails:

  • No "睡眠促进/巩固/导致" without wake/sleep control design
  • No "仅出现在/不存在于" for EEG-behavior correlation differences without Fisher z comparison context
  • Default pre-post wording: "睡前至睡后行为变化" not "睡后记忆提升"

Full sleep/EEG rules: docs/sleep-eeg-guardrails.md

7. Writing Templates

Chinese: docs/writing-templates.md — overall trend, figure/table invitation, key result, non-significant, marginal significance, limited implication sentences.

English: docs/writing-templates.md — APA-style templates for all common scenarios.

8. Figure/Table Narrative

Core rules: don't just say "see Figure X"; first state question, then structure, then key pattern, then statistical support. Never fabricate statistical values invisible from figure. Full specification: docs/figure-table-templates.md

9. Results vs Discussion Boundary

Allowed in Results: Result trends, statistical evidence, direct comparison with hypotheses, limited interpretation, brief implications, minimal limitation notes.

Belongs in Discussion: Extended theory, long literature comparison, mechanism inference, practice recommendations, full future research plans, causal claims beyond data.

10. Certainty Continuum

| Strength | English | 中文 |

|----------|---------|------|

| Strongest | demonstrates / shows | 表明 / 显示 |

| Moderate | suggests | 提示 |

| Weaker | appears to | 可能提示 |

| Tentative | may suggest | — |

| Cautious | is consistent with | 与……一致 |

| Weakest | raises the possibility that | 提供了初步证据 |

  • Experimental/RCT: stronger wording allowed, with operationalization boundaries
  • Cross-sectional/correlational: only "相关/关联/预测/提示"
  • Mediation models: NOT real causal mechanisms
  • Qualitative: "参与者叙述显示/研究者解释为"

11. Do-Not Rules (Core)

See Failure Modes table below for full list. Most unique / frequently violated:

  • ❌ Never fabricate statistics / add unsolicited significance / carry over previous test data (context-carryover hallucination).
  • ❌ Never write correlation as causation / p > .05 as "proven no effect" / drop "统计" prefix from cross-sectional mediation.
  • ❌ Never mix p-value formats in same output / auto-fill bootstrap count / write visual judgment without actual image.
  • ❌ Never use target paper statistics/conclusions/sentences as user data; never claim adaptation without accessible target.
  • ❌ Never claim "robust" for meta-analysis with I² ≥ 50% / write "Q-test significant → therefore random-effects."
  • ❌ Never write "sleep-enhanced/consolidated" without control design.
  • ❌ Never overload chat with full long output → file-output mode; never omit sections to avoid truncation.
  • ❌ Never ignore Module H H7 risk flags or H8 recommended mode.

12. Failure Modes

| # | Failure | Description |

|---|---------|-------------|

| 1 | Statistical hallucination | Fabricating statistics |

| 2 | Over-claiming | Exaggerating results |

| 3 | Discussion leakage | Discussion content in Results |

| 4 | Causal inflation | Correlation written as causation |

| 5 | Null-result misuse | Non-significant written as "proven no difference" |

| 6 | Figure misreading | Misreading charts |

| 7 | Template mismatch | Wrong template for analysis type |

| 8 | Journal-style mismatch | Ignoring target journal format |

| 9 | Over-polishing | Sacrificing accuracy for style |

| 10 | Missing main result | Only auxiliary analyses reported |

| 11 | Unclear hierarchy | Main vs auxiliary mixed |

| 12 | Unsupported implication | Implications without data support |

| 13 | Context-carryover hallucination | Previous test data leaking into current revision |

| 14 | Target-paper over-imitation | Copying original sentences, data, or conclusions |

| 15 | Design-mismatch transfer | Forcing incompatible structure (fMRI → survey) |

| 16 | Target-data contamination | Target paper statistics written as user results |

| 17 | Target-paper risk replication | Replicating target paper's reporting errors |

| 18 | Target-metadata hallucination | Inferring target metadata from domain knowledge |

| 19 | Target-source collapse | Mistaking user data/draft for target paper |

| 20 | Missing-target false adaptation | Claiming adaptation without accessible target |

| 21 | Remote-source ambiguity | web_fetch without reporting source/coverage |

| 22 | Partial-extraction overclaim | Claiming full extraction on partial read |

| 23 | Design-incompatible overtransfer | Presenting incompatible target as driving structure |

| 24 | Test-context carryover | Internal test names in formal output |

| 25 | Chat truncation loss | Sections lost due to chat truncation |

| 26 | False complete after truncation | Claiming complete after truncation |

| 27 | File-output omission | Missing sections in file-output |

| 28 | File-output echo | Pasting full file content back to chat |

13. Quality Checklist (Summary)

Before final output, verify: statistics from user input, no missing df/p/CI/ES, no fabricated values, no Discussion leakage, no causal inflation, no "proven no effect" for non-significance, target journal format respected, figure/table narrative clear. Full checklist: docs/quality-checklist.md

14. Integration with paper-results-reverse-engineer v3.0

When v3.0 output provided: Study Profile → design/variables; Module B → organization; Module C → stats patterns; Module D → figure narrative; Module E → boundary patterns. Risk Flag Rule: flagged errors/contradictions must NOT be replicated. Write: "目标文献该部分存在报告风险,不建议迁移。"

Full spec: docs/module-h-bridge.md, docs/target-paper-adaptation.md.

15. Module H Bridge Workflow

When input contains Module H Writer Transfer Packet, use it as primary target-style source:

| H Field | Maps To |

|---------|---------|

| H1 | Source Ledger + extraction coverage |

| H2 | Design-match judgment |

| H3–H5 | Results organization + paragraph/figure/table narrative |

| H6 | Results–Discussion boundary |

| H7 | Risk flags → "Do not transfer" |

| H8 | Writer mode / output depth selection |

Prefer Module H over full A–G. Never copy H wording directly into Results. If H8 says design-incompatible, never force normal adaptation. Full spec: docs/module-h-bridge.md.

16. Journal-Specific Style

心理学报: Chinese, p = 0.001 format, restrained tone, "结果表明" preferred.

APA 7th: English, p = .001 format, effect sizes mandatory.

Format consistency rule: Never mix p = .021 and p = 0.021 in same output.

Full specification: docs/journal-style.md

17. Revision Mode

Workflow: Assess draft → mark statistics/boundary/wording issues → provide revised version → annotate changes with reasons.

Output Format

1. 【草稿评估】

  • 优点: what the draft does well (clear structure, correct stat reporting pattern, etc.)
  • 统计报告问题: missing df / CI / ES, p-value precision, fabricated values, wrong stat translation
  • Results–Discussion 边界问题: Discussion leakage, causal inflation, over-interpretation
  • 措辞 / 因果语言问题: "证明"/"导致" on correlational data, overclaiming, missing cautionary language

2. 【修订版】

  • Directly replaceable Results paragraph(s)
  • 不自动补入本轮未提供的统计值 — leave placeholders or mark as "需补充"
  • 不把教学性提醒写进正式 Results 正文 — keep teaching notes in【修改说明】or【边界提醒】

3. 【修改说明】

  • 按句或按问题说明修改原因
  • 标注哪些内容建议移到 Discussion
  • 标注哪些统计值本轮未提供、需用户确认 (category B/C)

Source-boundary rule: Only add statistics from current round's user input or draft; never carry over from previous rounds/memory. Missing statistics → report as "本轮未提供" (category B) or "需用户确认" (category C).

Null-result warnings default to【统计报告检查】/【修改说明】, not formal Results text.

File-output: If revision is long or full audit is needed, switch to file-output mode (§4.1).

Full specification: docs/revision-mode.md

18. Target-Paper Results Style Adaptation Mode

Core principle: structure/style modeling, NOT content imitation.

Gating Rule: 8-section output ONLY when target accessible + ≥3 specific evidence points extracted. Otherwise → fallback: Source Ledger status + reason + standard Results.

Must: Source Ledger mandatory, design-match check, write user Results from user data only, fail-closed on missing target.

Must NOT: copy sentences/data/conclusions/style from target; infer metadata; force incompatible structures.

Full specification (all 19 subsections): docs/target-paper-adaptation.md. See also §19 Source Integrity.

19. Source Integrity & Anti-Plagiarism

1. Transfer organization logic only — never copy original sentences

2. Reference reporting order only — never copy target statistics

3. Adapt figure narrative approach only — never copy figure interpretations

4. Never write target's theoretical interpretations or conclusions into user Results

5. Never mimic author-specific personal writing style

6. Write "参考目标文献的 Results 结构" not "模仿作者写法"

7. Incompatible design → must state non-transferable

8. "尽量像原文一样写" → "保留相似结构和语气,但使用全新表述和用户自己的数据"

9. Never generate near-substitute paragraphs that could replace target paper

20. Example Usage

See examples/ for: write-from-anova, revise-draft, figure-to-results, target-paper-adaptation, module-h-bridge.

---

> Public version: 1.2.1 | Internal version: academic-results-writer-v1.2.1-stable

> Scope: Academic Results section writing for psychology and behavioral science

> Default: Chinese output, standard-depth, file-output when long

> Key features: Target-paper Results Style Adaptation Mode, Module H bridge workflow, anti-plagiarism guardrails, design-incompatible fallback, hard-self-check meta-analysis and EEG guardrails

> Documentation: docs/ for full specifications, examples/ for usage examples, CHANGELOG.md for version history

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