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Paper Results Reverse Engineer

作者 bin77-chris · GitHub ↗ · v3.0.3 · MIT-0
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功能描述
Stable v3.0.3 release of the psychology Results reverse-engineering skill. Uses three-axis classification, study-profile-first workflow, design-adaptive bran...
使用说明 (SKILL.md)

Paper Results Reverse Engineer (v3.0.3)

Deconstruct and learn from the Results section of psychology papers across all subfields. The Results section is a guided narrative, not a data dump — this skill reverse-engineers that narrative.

Supported subfields: Cognitive / Social / Personality / Developmental / Educational / Clinical / Psychometrics / Cognitive Neuroscience / fMRI / EEG / Meta-analysis / Qualitative / Mixed methods / Methodological / Simulation.

When to Use

Activate when the user pastes a Results section, uploads a PDF, provides figure captions + paragraphs, or requests "拆这篇结果部分" / "这张图怎么讲" / writing-strategy extraction / PPT scripts / statistical reporting checks.

Output Depth Modes

Mode Trigger Output
quick "快速看一下" / "大概拆一下" Study Profile + B + D (core figures) + E + self-check. No Module C or F.
standard (default) (no mode specified) / "正常生成" Study Profile + A–G. Module C: paragraph/cluster level (2–4 clusters per ¶). Module F: PPT page suggestions + one-liners + evidence boundaries only.
close-reading "逐句拆解" / "完整精读" / "做 PPT" / "汇报讲稿" Study Profile + A–G at max depth. Module C: sentence-level. Module F: full verbatim scripts + Q&A + backup slides. Phased execution allowed for long papers.

Mode-adaptive chat prompt (mandatory): After each output, add one line indicating the current mode and available alternatives. See docs/execution-constraints.md for the full template.

Execution Constraints (Hard Limits)

  1. Self-Check: Max 1 per file. Auto-Patch: Max 1 after failed check, then stop and report.
  2. No recursive self-invocation — prompt user for new analysis rounds.
  3. PDF read limit: 2 reads per round max.
  4. Chat output: file-first — only path + 3–5 core findings + self-check + manual-review items.
  5. Context overflow: stop, prompt user to split into phases.

Full details: docs/execution-constraints.md

Supporting-File Loading Policy (Mandatory)

Before executing any branch-specific rule, module specification, or guardrail 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
Branch B/C/E/F active docs/branch-a-b-c-d-e-f.md
Branch G active docs/branch-g-meta-analysis.md
Branch H active docs/branch-h-qualitative.md
Branch I active docs/branch-i-simulation.md
Module H triggered docs/module-h-spec.md
G0–G8 self-check / source verification / B0 heading detection docs/source-verification.md
Causal-language audit / PPT three-layer separation docs/causal-language-guardrails.md
G3 anti-template contamination check docs/anti-template-contamination.md
Long PDF / phased execution / Phase cleanup docs/execution-constraints.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.

Input Types

# Input Handle by
1 Full Results section Study Profile → Modules A–G
2 Single Results subsection B–E; flag missing context
3 Figure caption + paragraph D + C; brief Study Profile
4 Abstract + Methods + Results Study Profile → Full A–G
5 PDF excerpt (pasted) Treat as type 1–3
6 PDF file upload pdftotext → detect Results → A–G
7 Figure/table screenshot Vision model → cross-ref caption/text → D
8 Open-ended request Clarify; route to appropriate modules

Prompt templates: references/prompt-templates.md


Workflow: Study Profile First

Phase 0.3: Three-Axis Classification (Mandatory — Before Study Profile)

Classify every paper on three independent axes before filling the Study Profile. Never use single-axis labels.

Axis Name Values (non-exhaustive)
Axis 1 Article Type Experiment / Survey / Longitudinal / RCT / Psychometric validation / Meta-analysis / Methodological simulation / Qualitative / Mixed / Review
Axis 2 Substantive Domain Cognitive / Social / Developmental / Educational / Clinical / Cognitive Neuroscience / fMRI / EEG / Psychometrics / Health / Sleep / Meta-science
Axis 3 Data/Method Modality Behavioral accuracy/RT / Questionnaire scores / Clinical diagnosis / fMRI activation / fMRI RSA/MVPA / ALE coordinates / EEG/ERP / SEM/mediation / Meta-analytic ES / Monte Carlo simulation / Qualitative themes

Axis 1 determines Adaptive Branch (A–I). Axis 2 and 3 are secondary tags guiding terminology, chart types, and interpretation boundaries.

Critical distinction: Never conflate Axis 1 with Axis 2. Studying meta-analytic methods ≠ doing a meta-analysis (→ Branch I, not G). fMRI experiment ≠ fMRI coordinate-based meta-analysis (→ Branch F, not G subbranch).

Study Profile Template

## Study Profile

### 三轴分类
| 轴 | 类别 | 值 | 来源 |
|----|------|----|------|
| Axis 1 | Article Type | ... | [原文Methods] |
| Axis 2 | Substantive Domain | ... | [原文推断] |
| Axis 3 | Data/Method Modality | ... | [原文Methods] |
| Primary Branch | Branch A–I | ... | [教学性说明] |

### 基本信息
| 维度 | 内容 | 来源 |
|------|------|------|
| 样本信息 | N, population, age, sex, inclusion/exclusion | [原文Methods] |
| 任务或测量工具 | Task/questionnaire/interview/intervention | [原文Methods] |
| 核心变量 | IV/DV or predictor/outcome or mediator/moderator | [原文Methods] |
| 主要统计方法 | t/ANOVA/regression/SEM/meta-analysis/thematic analysis etc. | [原文Methods] |
| Results 小节标题 | (list all Results subsection titles) | [原文直接报告] |
| 核心表格和图表 | (Table/Figure numbers + brief description) | [caption] |
| 理论/模型预期 | (Introduction 中的理论预测 — NOT study's own hypotheses) | [原文Introduction] |
| 本研究直接检验的问题 | (Author's explicit questions in Introduction/Results) | [原文直接报告] |
| ⚠️ 假设性质说明 | (If "Intriguingly"/"Surprisingly" appear, note possible non-a-priori) | [正文推断] |
| Results 直接发现 | (1-2 sentence summary) | [原文直接报告] |
| Discussion 中的解释 | (Author's interpretation in Discussion) | [原文Discussion] |

Rules: Every field with source tag. N from Methods, never from df. [无法确定] if unavailable. Split hypothesis fields: 理论预期 / 检验问题 / Discussion 解释 are distinct. Task terminology must match paper (recognition ≠ recall).

Phase 0.5: Evidence Validation Rules (Mandatory)

Rule 1: Day/Session Strong Evidence Rule

Only write "Day1/Day2" / "两天实验" when the paper explicitly uses these markers. For procedural "then/after/subsequently" without day markers → use phase-based description. Also never infer "同一天" from absence of markers.

Rule 2: Stimulus Pool vs Actual Task Exposure

Separate "候选材料池" from "实际任务数量." If Methods says "12 videos created, participants viewed 10" → report "10 videos per house," not "12 videos."

Rule 3: Study Design Taxonomy

Use precise labels. Never write "observational" for controlled laboratory tasks. "Within-subject experiment" is valid. Only use "RCT" with random assignment. Only use "observational" with no stimulus/condition manipulation. Distinguish cluster vs individual randomization.

Rule 4: Closed-Loop Phase Precision Guardrail

For CL-TMR / closed-loop auditory stimulation papers, always separate into four timing components:

  • (a) Detection phase — when the algorithm detects a target event (e.g., SO up-state)
  • (b) Stimulus onset delay — fixed or variable delay between detection and stimulus delivery
  • (c) Stimulus duration — actual length of the auditory stimulus
  • (d) Actual stimulation phase variability — where does the sound actually fall relative to the ongoing oscillation?

Never infer "down-state stimulation" unless the paper explicitly reports it. Never infer "phase-locked stimulation" unless the paper reports measured phase precision metrics.

Supplementary guardrail: If Supplementary material contains phase analysis but was not read in quick/standard mode, mark ⚠️ Supplementary phase analysis not read; actual stimulation phase unverified in G5 manual review. Never draw conclusions about stimulation phase from the main text alone.

Rule 5: Sham-Control Trial Type Distinction

Distinguish between:

  • Physiological sham/control trials — within-participant control trials where target EEG events are detected but no stimulus is delivered (e.g., "sham" in CL-TMR)
  • Behavioral control conditions — separate experimental conditions manipulating task parameters during wake
  • Active acoustic control — a different sound delivered during sleep (e.g., white noise, reversed speech)

If sham = no sound (silent SO detection), do NOT label it as "active control" or "acoustic control." Label it as "physiological sham (no stimulus delivered)." Note the limitation: silent sham cannot control for non-specific arousal effects of sound presentation.


Phase 1: Adaptive Branch Selection

Based on Axis 1 (Article Type):

Branch Article Type Key Focus
A Experiment with random assignment Manipulation check, main effect, interaction, simple effects, post-hoc, ES
B Survey / Correlational Descriptive, reliability, correlation, regression, mediation, moderation
C Intervention / RCT Baseline, CONSORT flow, primary outcome, secondary, AE, follow-up
D Developmental / Educational Age/grade differences, growth curve, multilevel, measurement invariance
E Psychometric / Scale Development Item analysis, EFA/CFA, reliability, validity (convergent/discriminant/criterion), invariance
F Neuroimaging / fMRI / EEG Task phase, neural measure, ROI/electrode, activation/RSA/ERP, multiple comparison correction
G Meta-analysis / Systematic Review Inclusion/exclusion, k, pooled ES, heterogeneity (Q/I²/τ²), moderator, bias, sensitivity
H Qualitative Coding, themes, subthemes, quotes, saturation, triangulation, reflexivity
I Methodological / Simulation Simulation factors, performance metrics (Type I error, power, RMSE, coverage), method comparison

Branch-Specific Key Rules

Branch Key focus Full spec
B (Survey) Cross-sectional mediation guardrail, hypothesis direction, measurement quality, internal inconsistency (B1–B9) docs/branch-a-b-c-d-e-f.md
C (RCT) AE/safety, clinical significance 6-layer, active comparator, Module B 14-block (C1–C6, C1a–C1h) docs/branch-a-b-c-d-e-f.md
D (Developmental) Age/group comparisons, longitudinal wording, nesting, measurement invariance docs/branch-a-b-c-d-e-f.md
E (Psychometric) Evidence taxonomy, diagnostic wording, cutoff, classic scale rule, table orientation (Rules 1–9) docs/branch-a-b-c-d-e-f.md
F (fMRI/EEG) Task-phase, correction method, ROI source, brain-behavior wording, mechanism guardrail docs/branch-a-b-c-d-e-f.md
G (Meta-analysis) Moderator guardrail, PRISMA, publication bias, coordinate-based meta subbranch (G1–G17) docs/branch-g-meta-analysis.md
H (Qualitative) Theme detection, reflexivity grading, intercoder reliability, demographic audit, IPA/GT/CGT subtypes (H1–H23) docs/branch-h-qualitative.md
I (Simulation) N/A rule, heatmap precision, evidence boundary, anti-template 4-tier (I1–I6) docs/branch-i-simulation.md

Phase 2: Modules A–G

All module content references the Study Profile and selected branch. Never carry over terms or statistics from a previous paper.

Module A: Study Profile Extended

The Study Profile from Phase 0, extended with three-axis fields first, then traditional fields. Always use source tags.

Module B: Results Structure Map

For each subsection/paragraph cluster: subsection title, question answered, data/analysis used, corresponding table/figure, main result (1–2 sentences), author's intended conclusion, annotation (original heading vs teaching supplement).

B0: Results Heading Detection Rule (Universal): Scan for ALL heading signals (bold, standalone phrases, Title Case, functional labels). Do not rely on Markdown ##/###. Always separate "原文显式小节标题" from "Skill 教学性补充分块 [教学性补充]". Never write "原文无显式小节标题" without full-text scan. Full specification: docs/source-verification.md

Module C: Results Paragraph/Sentence Annotation

Print label legend first. Then annotate per mode: quick → skip; standard → paragraph/cluster level (2–4 clusters per ¶, function label + one note); close-reading → sentence-level with individual annotations.

14 Function Labels: 1-Restate aim/Q | 2-Restate method | 3-Overview trend | 4-Invite to view figure/table | 5-Report specific result | 6-Report statistical evidence | 7-Evaluative emphasis | 8-Compare with prior work | 9-Compare with prediction/model | 10-Explain/interpret | 11-Note non-significant/inconsistent | 12-Acknowledge limitation | 13-Hint at implication | 14-Transition to Discussion

Label Rules: L1 — "presented in"/"see Fig." → Label 4 takes priority. L2 — missing-data/cannot-compute → Label 11+12. L3 — dual-purpose sentences may carry multiple labels. L4 — "Interestingly"/"Surprisingly" → add Label 7 + flag as potentially exploratory.

Detailed examples: references/function-labels.md

Module D: Table/Figure Explanation

For core figures/tables: question answered, structure, author's guide sentence, key pattern, primary vs auxiliary, PPT narrative logic, 1-minute script (Chinese), easily misinterpreted points.

Figure analysis modes: Core hypothesis figures → full image mode (vision model). Supplementary → caption + body text mode. Flag: ⚠️ 未对此图进行图像分析 and use [caption] / [正文推断] tags only. Figure fallback rule: if image recognition fails → ⚠️ 图像识别失败; describe only what caption/body text confirms; never fabricate visual details.

Module E: Evidence Strength & Interpretation Boundary

Three layers separated: 原文直接结果 [原文直接报告] / 作者解释 [原文Discussion] / 教学性总结 [教学性说明].

Seven items: 1) Core claim 2) Evidence type 3) Alternative explanations 4) Evidence chain strength 5) Causal language audit 6) Missing links 7) What this study does NOT prove.

See docs/causal-language-guardrails.md for the full causal language ladder and three-layer separation rules.

Module F: PPT / Presentation Scripts

Output depth per mode. All modes enforce: three-layer separation (Result/Interpretation/Teaching), causal language check, branch-specific presentation angles. PPT scripts must never present Discussion interpretation as Results fact.

See docs/causal-language-guardrails.md for PPT causal language check rules.

Module G: Self-Check & Anti-Template Contamination

G0: Source verification — compare generated claims against original paper (not generated file). Use the verification template with verbatim source quotes. G1: File completeness — search for truncated/TODO/待补充. G2: Module completeness checklist. G3: Anti-template contamination — Tier a (pollution, delete) / b (method background, allow) / c (N/A contrast, allow) / d (audit checklist only, allow). G4: Task type confusion check. G5: Manual review with Critical/Important/Minor grading. G6: Time-structure audit. G7: Source verification audit. G8: Three-axis classification self-check.

Full specification: docs/source-verification.md and docs/anti-template-contamination.md


Source Attribution Conventions

Tag Meaning
[原文直接报告] Directly from Results/Methods
[原文Discussion] Author's interpretation from Discussion
[原文Methods] Factual details from Methods
[图片识别] Read from figure via vision model
[正文推断] Inferred from body text
[教学性说明] Agent's educational commentary
[无法确定] Cannot determine from available sources

Author-disclosed vs skill-inferred rule: Never tag a limitation as [原文Discussion] unless authors explicitly state it. Full rules: docs/source-verification.md.


Statistical Language Rules

  • Derived Clinical Metric Rule: NNT/NNH/ARR/RR/OR/d calculated by skill → [Calculated by skill / 教学性计算].
  • Standardized Effect Size Precision Rule: When no Cohen's d/OR/RR reported → "No standardized between-group effect size was reported" (NOT "No effect size reported"). List what clinical effect information WAS reported.
  • Never fabricate statistics. Never rewrite one statistic as another (r ≠ t ≠ F). For model fit, report multiple indices, not just χ².
  • Full spec: docs/causal-language-guardrails.md

Causal Language Ladder (Summary)

Design Allowed Prohibited
Cross-sectional / Correlational / Survey 相关、关联、预测 导致、影响、证明机制
Experimental (random assignment) 操纵X导致Y差异 (still note boundary conditions)
Longitudinal X预测后续Y X导致Y变化 (without experiment)
RCT 干预效果显著 (note attrition, baseline, blinding)
Meta-analysis 总体证据显示、pooled effect 提示 单一实验因果证明、证明方案最优
Qualitative 主题显示、参与者叙述反映 统计因果
Simulation 在这些模拟条件下 证明某方法最好、证明某效应不存在

Universal prohibition (all non-manipulation studies): "证明" / "直接导致" / "确定是因为". Full specification: docs/causal-language-guardrails.md

Mechanism-Wording Guardrail for EEG/ERP/ERSP Studies

When describing brain-behavior relationships in EEG/ERP/ERSP/MEG/fMRI studies:

  • Prohibited: "mediate/mediates/mediation" — unless the paper explicitly reports a formal statistical mediation model (e.g., bootstrap indirect effect, Sobel test, SEM path model)
  • Use instead: "correlate of," "marker of," "associated with," "may be related to," "predictor of" (for within-subject time-frequency analyses), "electrophysiological signature of"
  • When a formal mediation IS reported: still audit whether temporal precedence can be established (EEG data within same sleep epoch may not satisfy mediation assumptions)
  • Applies to: Module B (results structure), Module C (paragraph commentary), Module D (figure narration), Module E (evidence strength), Module F (PPT scripts), Study Profile (理论预期 field)

Module H: Writer Transfer Packet (Optional)

Compressed transfer packet for academic-results-writer Target-paper Results Style Adaptation Mode. Triggered by user request for "写作迁移包" / "给 academic-results-writer 使用".

Structure: H1-Source Identity, H2-Design Transfer Summary (with compatibility rating), H3-Results Organization Template (Transfer/Partial/Do not transfer), H4-Paragraph Writing Patterns (abstracted), H5-Figure/Table Narrative Patterns, H6-Results–Discussion Boundary, H7-Risk Flags, H8-Recommended Writer Mode.

Constraints: 1–2 pages max. No target paper original sentences — abstract function labels only. No target paper statistics for writer to apply. All target paper risks in H7. Partial extraction → H1 must mark coverage: partial. Design-incompatible → H8 must recommend fallback.

Full specification: docs/module-h-spec.md


File Output Template

→ Module A–H headers listed in Phase 2 above. Metadata Date Safety Rule: never fabricate generation date; use [无法确定] if not reliably confirmable via date/session_status.


Do-Not Rules (Core)

See Failure Modes table below for full list. Most critical:

  • ❌ Don't invent data / fabricate statistics / pull N from df / fabricate generation date.
  • ❌ Don't write Discussion as Results (three-layer separation).
  • ❌ Don't write correlation as causation; don't use "mediate" for EEG/ERP without formal mediation model.
  • ❌ Don't carry over previous paper terms (G3 anti-template contamination).
  • ❌ Don't mislabel: simulation ≠ meta-analysis (I vs G); lab task ≠ observational; sham ≠ active control.
  • ❌ Don't infer stimulation phase / day-session / metadata without explicit paper evidence.
  • ❌ Don't skip Study Profile, Module G, or label legend in Module C.
  • ❌ Don't print full analysis in chat (file-first). Self-check against original paper, not generated output.

Failure Modes (Summary)

Failure Prevention
Fabricated statistics Enforce [无法确定]
N from df Pull from Methods
Template pollution G3 search
Discussion → Results Three-layer separation
Correlation → causation Causal ladder
"Mediate" without mediation model Mechanism-wording guardrail
Phase inference without Supplementary Closed-loop phase precision guardrail
Sham = "active control" Sham-control distinction rule
Inferred limitation → [原文Discussion] Author-disclosed vs skill-inferred rule
Fabricated generation date Metadata date safety rule
Wrong branch Study Profile → Axis 1
Day/Session invented Rule 1: require explicit markers
Stimulus pool = task count Rule 2: separate pool vs actual
Lab task = observational Rule 3: precise design taxonomy
Simulation → meta-analysis I vs G distinction
ALE → pooled ES G10
Phase titles in merged file Phase 5 merge back
Phase files not cleaned Cleanup verification

Output Directory & Naming

~/Desktop/OpenClaw_Paper_Analysis/
├── outputs_md/reverse_engineer/{FirstAuthor}_{Year}_Results_Reverse_Analysis.md
├── outputs_md/results_writer/
├── logs/
├── figures_notes/
└── templates/

Phase temp files: temp/{FirstAuthor}_{Year}/. Final output: only one H1 title, no Phase N titles.


Long PDF: Phased Execution

When PDF > ~20 pages or context is tight:

Phase Content Output
Phase 1 Study Profile Study Profile table
Phase 2 Module A–B A + B
Phase 3 Module C–D C + D
Phase 4 Module E–G E + F + G
Phase 5 Merge + final self-check Complete Markdown

Phases write to temp/, merged to final output. Clean temp files on success unless debug_mode: true.

Full details: docs/execution-constraints.md


Patch Mode

When user says "小修改" / "优化一下": modify only pointed-out issues. Append Revision log table at file end. Keep original structure.


Public version: 3.0.3 Internal version: psychology-results-reverse-analysis-v3.0.3-bridge Scope: General psychology literature (all subfields) Analysis mode: Study Profile first, three-axis classification (Article Type × Domain × Data Modality), design-adaptive branching (A–I), source-verified evidence Output mode: File-first (Markdown to desktop folder; chat = summary only) Key features: Cross-type validated across all 9 branches (A–I). Default standard mode. Retains quick/standard/close-reading modes. Optional Module H Writer Transfer Packet for academic-results-writer integration. Documentation: Branch-specific rules in docs/, examples in examples/, changelog in CHANGELOG.md

安全使用建议
Install only if you are comfortable with the agent reading PDFs you provide or point it to, running pdftotext locally, and saving analysis files under OpenClaw_Paper_Analysis on your Desktop. Prefer uploading explicit files instead of giving broad local paths, and avoid using it on confidential papers unless you understand where outputs and temporary files may be stored.
能力标签
crypto
能力评估
Purpose & Capability
The core purpose is coherent: analyze psychology paper Results sections, figures, and PDFs, then produce structured Markdown analysis with source-verification guardrails.
Instruction Scope
The PDF workflow explicitly supports any local PDF path and tells the agent to use local reading plus exec with pdftotext, which is broader than uploaded-document-only processing and lacks clear path scoping.
Install Mechanism
The package appears to be Markdown-only skill content with no dependencies, installer script, network calls, credentials, or executable payloads; static scan metadata was clean and VirusTotal telemetry was null.
Credentials
Using pdftotext is purpose-aligned for PDFs, but raw shell execution and arbitrary local path support are higher authority than necessary without sandbox or allowlist language.
Persistence & Privilege
The skill is file-first and writes analyses to a fixed Desktop directory plus temp/intermediate paths, with cleanup rules, but it does not require explicit user consent before persisting potentially sensitive paper content.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install paper-results-reverse-engineer
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /paper-results-reverse-engineer 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v3.0.3
No major user-facing changes detected in this version. - Version bump to 3.0.3 with updated internal version metadata. - No file or rule changes observed—functionality and workflow remain unchanged.
v3.0.2
Branch D+F cross-validation complete. All 9 branches (A–I) validated. Version unified to 3.0.1. Added recommended first prompts.
v3.0.1
# Paper Results Reverse Engineer (v3.0.1) ↓ 一句话定义 Deconstruct and learn from the Results section of psychology papers. ## When to Use → 用户判断"这工具是不是我要的" ## Output Modes → quick / standard / close-reading ## Module H → 与 writer 的桥 ## Recommended First Prompts → 用户直接复制使用 ## Quick Example → 一看就懂 ## Directory Structure → 技术用户参考 ## Version → 3.0.1
元数据
Slug paper-results-reverse-engineer
版本 3.0.3
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Paper Results Reverse Engineer 是什么?

Stable v3.0.3 release of the psychology Results reverse-engineering skill. Uses three-axis classification, study-profile-first workflow, design-adaptive bran... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 75 次。

如何安装 Paper Results Reverse Engineer?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install paper-results-reverse-engineer」即可一键安装,无需额外配置。

Paper Results Reverse Engineer 是免费的吗?

是的,Paper Results Reverse Engineer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Paper Results Reverse Engineer 支持哪些平台?

Paper Results Reverse Engineer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Paper Results Reverse Engineer?

由 bin77-chris(@bin77-chris)开发并维护,当前版本 v3.0.3。

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