From research-writing-assistant
Plans experiment protocols, result tables, mock data, evaluation setups, and results sections before real data is available for ML papers.
npx claudepluginhub norman-bury/research-writing-skillThis skill uses the workspace's default tool permissions.
This skill designs the experiment/result layer before final metrics exist. It may generate mock planning data, but never presents mock data as real experimental evidence.
Orchestrates full research pipeline from Brainstorming to Reporting via Planning, Implementation, Testing & Visualization phases with user checkpoints. Configurable for physics, AI/ML, statistics, math domains, depth, and agent personas.
Analyzes experiment results from tables, stats, or descriptions to generate LaTeX discussion paragraphs for academic papers via two-phase workflow: extracts findings for user confirmation, then writes grounded analysis.
Performs strict statistical analysis on ML/AI experimental results, generates real scientific figures, checks significance, validates comparisons, and produces analysis bundles.
Share bugs, ideas, or general feedback.
This skill designs the experiment/result layer before final metrics exist. It may generate mock planning data, but never presents mock data as real experimental evidence.
Before writing Results or Discussion, create:
plan/experiment-protocol.mdplan/review/method-experiment-traceability.mdtables/table-schema.mdfigures/data-manifest.mdmock_* filesThe protocol must include:
Each contribution in Introduction must map to at least one experiment or limitation note.
Use these gates in plan/stage-gates.md for result-heavy papers:
plan/review/method-experiment-traceability.md.tables/table-schema.md, figures/data-manifest.md, and data files.plan/review/<section>-peer-review.md.Create:
| Contribution | Method module | Experiment | Table/Figure | Allowed claim | Evidence status |
|---|---|---|---|---|---|
Do not let a contribution survive in Introduction if no experiment, limitation note, or future-work boundary supports it.
Mock or synthetic values are allowed only for planning figures and table layout.
Rules:
mock_ or synthetic_.PLANNING DATA - replace before submission.[待真实实验替换].For each table, define:
| Table | Purpose | Rows | Metrics | Data source | Replacement owner |
|---|
Do not create a table unless it supports a claim in the manuscript.
Recommended table fields include mean ± std or confidence intervals when repeated runs are expected. Record aggregation rules in tables/table-schema.md.
Data figures must go through figures-python:
figures/data-manifest.md.figures/<section>/<figure>.py.Model architecture and flow diagrams use figures-diagram prompts instead of synthetic data plotting.
For real data:
The method achieves X under condition Y, compared with baseline Z. The improvement is mainly associated with [module], while [failure case] remains visible in [metric].
For planning data:
[待真实实验替换] This paragraph will compare Table N after real experiment logs are inserted.
Never leave "experiment purpose", "discussion prompt", or "table position" instructions inside final chapter files.