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experiment-results-planning

Skill0 starsChanged 40 days agoMITView on GitHub

Use when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final

2 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

9 rules. About 0 are common, 7 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈7

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Before writing Results or Discussion, create:

    line 12
  2. Very specific:

    Use these gates in plan/stage-gates.md for result-heavy papers:

    line 37
  3. Distinctive:

    Do not let a contribution survive in Introduction if no experiment, limitation note, or future-work boundary supports it.

    line 62
  4. Very specific:

    Every mock table must contain a note: PLANNING DATA - replace before submission.

    line 71
  5. Distinctive:

    Do not describe mock values as "results show", "实验结果表明", or "verified".

    line 73
  6. Distinctive:

    Do not create a table unless it supports a claim in the manuscript.

    line 82
  7. Distinctive:

    Write or receive CSV/JSON data.

    line 90
  8. Distinctive:

    Write a caption that states what the figure measures, not what the author hopes it proves.

    line 94
  9. Distinctive:

    Never leave "experiment purpose", "discussion prompt", or "table position" instructions inside final chapter files.

    line 112

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Experiment Results Planning

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.

Hard Gate

Before writing Results or Discussion, create:

  • plan/experiment-protocol.md
  • plan/review/method-experiment-traceability.md
  • tables/table-schema.md
  • figures/data-manifest.md
  • real data files or clearly labeled mock_* files

Experiment Protocol

The protocol must include:

  • Dataset and split strategy.
  • Baselines and why each is fair.
  • Metrics and imbalance handling.
  • Main comparison.
  • Efficiency evaluation.
  • Ablation studies for each claimed module.
  • Generalization or robustness checks.
  • Explainability evaluation if XAI is a contribution.

Each contribution in Introduction must map to at least one experiment or limitation note.

Recommended Experiment Gates

Use these gates in plan/stage-gates.md for result-heavy papers:

  1. Gate D0: Experiment Protocol Locked
    • Required: datasets, split rules, Non-IID construction, seeds, baselines, metrics, hardware/software, log schema.
  2. Gate D1: Method-Experiment Traceability
    • Required: plan/review/method-experiment-traceability.md.
    • Map each contribution to method modules, experiments, tables/figures, and allowed claims.
  3. Gate D2: Table/Figure Data Contract
    • Required: tables/table-schema.md, figures/data-manifest.md, and data files.
  4. Gate D3: Main/Efficiency/Ablation/Generalization/XAI Results
    • Each result family needs raw logs, aggregation rule, table update, figure script, and prose update.
  5. Gate D4: Result Chapter Decontamination
    • No "实验目的", "表位", "回填模板", "讨论提示", or planning notes in the chapter body.
  6. Gate D5: Peer Review Pass
    • Required: plan/review/<section>-peer-review.md.

Method-Experiment Traceability

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 Data Boundary

Mock or synthetic values are allowed only for planning figures and table layout.

Rules:

  • File names must start with mock_ or synthetic_.
  • Every mock table must contain a note: PLANNING DATA - replace before submission.
  • Manuscript prose using mock values must keep [待真实实验替换].
  • Do not describe mock values as "results show", "实验结果表明", or "verified".

Table Schema

For each table, define:

TablePurposeRowsMetricsData sourceReplacement 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.

Figure Handoff

Data figures must go through figures-python:

  1. Write or receive CSV/JSON data.
  2. Record it in figures/data-manifest.md.
  3. Generate figures/<section>/<figure>.py.
  4. Export PNG and SVG.
  5. Write a caption that states what the figure measures, not what the author hopes it proves.

Model architecture and flow diagrams use figures-diagram prompts instead of synthetic data plotting.

Results Prose Pattern

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.

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