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research-e-coding
ResearchPilot Research Assistant [Phase E]: Coding
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
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ResearchPilot Research Assistant [Phase E]: Coding
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
| name | research[E]-coding |
| description | ResearchPilot Research Assistant [Phase E]: Coding |
| version | 2.0.0 |
| license | LICENSE |
user_requirements.md priority: All user constraints recorded in
docs/user_requirements.md(direction preferences, implementation requirements, document format, etc.) take precedence over any default instruction in this skill. Always read that file before generating any output to ensure compliance with confirmed user constraints.
Implement code file by file according to docs/implementation.md, maintain the
development log, and conduct a proactive code review (runnable + logically correct)
when all files are complete.
Prerequisite: docs/implementation.md exists and the pre-coding checklist
(Phase D end) has been completed.
ResearchPilot-Skills splits a complete academic research project into seven independent stage skills. The current skill is one link in that chain.
| Skill | Phase | Main Output |
|---|---|---|
/research[A]-exploration | Direction Exploration | docs/idea_report.md Part 1 |
/research[B]-idea | Idea Deepening | docs/idea_report.md Part 2 |
/research[C]-experiment | Experiment Design | docs/idea_report.md Part 3 |
/research[D]-implementation | Implementation Design | docs/implementation.md |
/research[E]-coding | Coding | code/ + docs/dev_log.md |
/research[F]-iteration | Code Iteration | dev_log.md iteration records |
/research[G.0]-plan | Paper Planning | manuscript architecture + notebooks/figures.ipynb |
/research[G.1]-method | Method | manuscript Method section |
/research[G.2]-experiments | Experiments | manuscript Experiments section |
/research[G.3]-abstract | Abstract | manuscript Abstract |
/research[G.4]-introduction | Introduction | manuscript Introduction |
/research[G.5]-related | Related Works | manuscript Related Works |
/research[G.6]-conclusion | Conclusion + References | manuscript Conclusion |
/research[G.7]-review | Full-paper Review | review report |
docs/
idea_report.md # Research report in three parts:
# Part 1: Motivation, RQs, Key Works (Phase A)
# Part 2: Introduction, Related Works, Method (Phase B)
# Part 3: Datasets, Experiment Design, Resource Estimate (Phase C)
implementation.md # Coding guide: file- and function-level implementation plan (Phase D)
dev_log.md # Dev log: progress, decisions, "How to Run" section (Phase E)
user_requirements.md # User constraints: collected and maintained by Claude
papers/ # Downloaded paper PDFs or abstract TXTs
manuscripts/ # Paper drafts, each revision archived separately
# (e.g. v1.0-initial-draft.md, v1.1-revision.md)
code/
src/ # Core model and training code
scripts/ # Run scripts (train.sh, evaluate.sh, ablation.sh)
configs/ # Hyperparameter config files
baselines/ # Baseline model implementations
notebooks/ # Visualization notebooks; paper figure/table generation
data/ # Datasets (gitignored)
results/ # Experiment results (gitignored)
logs/ # Training logs (gitignored)
README.md # Environment setup and run commands
requirements.txt # Dependencies (library names only, no torch family)
/research[E]-coding
An optional natural-language instruction may follow the command. The AI will treat it as an additional constraint or supplement for this invocation.
E-0 Create README.md and notebooks/ directory
E-1 Create dev_log.md (with progress table and "How to Run" chapter)
E-2 Implement files in the order specified by implementation.md
After each file: update dev_log.md + update README.md if needed + add notebook
After each module: validate consistency with implementation.md
E-3 requirements.txt rules (library names only, no versions, no torch family)
E-4 Design issue found → pause and inform user, do not work around it
E-5 implementation.md error found → fix the document first, then fix the code
E-6 Post-file sync (README + "How to Run" chapter in dev_log.md)
E-7 Proactive code review after all files complete
E-8 Poor experiment results → backtrack through B/C/D (no code-only patches)
Full step-by-step instructions: references/phase-E.md.
✅ Done can only be marked after a file is written and runs without errorsreferences/template-flexibility.md take precedence over any
specific template instruction.After the code review passes:
Phase E complete. Code review passed — code is runnable and logically
consistent with the design.
→ Use `/research[F]-paper` to start paper writing.
references/phase-E.mdreferences/template-flexibility.mdResearchPilot Research Assistant [Phase G.0]: Paper planning
ResearchPilot 科研助手[阶段 G.0]:论文规划
Global-installable, project-level academic rebuttal strategy skill for AI/ML/CV/NLP/Robotics papers. Use when authors need a workspace-local .awesome-rebuttal state folder, paper/code/review/venue-rule intake, JSON memory, snapshots, LaTeX/template handling for one-page rebuttals, reviewer stance analysis, strategy planning, experiment triage, safe author response drafting, or AC summaries under confirmed venue rules.
Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.
Use when the user wants a paper audited for integrity issues — image misuse, numerical anomalies, logical gaps — and needs a reviewable evidence report. Works on external papers (PDF / DOI / arXiv) and on outputs from a local paper-writer run. Single-stage skill.
Use when the user wants a comprehensive literature survey on a specific research topic. Outputs a complete PDF survey (6–20 pages, 60+ real citations, 100+ recommended) with LaTeX source, taxonomy figures, and a classified literature table. Single-stage, no Python runtime.