Install
openclaw skills install academic-results-writerAcademic Results Writer
Writes, revises, and audits academic Results sections from statistical outputs, figures, tables, captions, and rough drafts. Designed for psychology, cognitive neuroscience, sleep and memory, VR experiments, EEG/fMRI, psychometrics, intervention studies, survey models, meta-analyses, and qualitative studies. Default output is Chinese, with optional English and journal-specific styles. Supports Target-paper Results Style Adaptation Mode and Module H Writer Transfer Packet integration from paper-results-reverse-engineer v3.0+.
Academic Results Writer (v1.2.0)
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
- Results is a reader-guided narrative, not a data dump.
- Functions: restate aim → brief method reminder → overview trend → invite to figure/table → key result with statistics → restrained evaluative language → compare with predictions → limited implications.
- Results can include limited interpretation but NOT full Discussion.
- Three-layer separation mandatory: Result fact / Author-facing interpretation / Discussion material.
- 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.
- 【结果组织建议】
- 【可直接使用的结果段】
- 【关键统计报告检查】
- 【结果与讨论边界提醒】
- 【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 = .021andp = 0.021in 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
- Transfer organization logic only — never copy original sentences
- Reference reporting order only — never copy target statistics
- Adapt figure narrative approach only — never copy figure interpretations
- Never write target's theoretical interpretations or conclusions into user Results
- Never mimic author-specific personal writing style
- Write "参考目标文献的 Results 结构" not "模仿作者写法"
- Incompatible design → must state non-transferable
- "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"
- 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: 1.2.0 | Internal version: academic-results-writer-v1.2.0-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.mdfor version history