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Experiment Results Planning

CommunityPopular
Norman-bury
experiment-results-planning

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

Overview

PublisherNorman-bury
Repositoryresearch-writing-skill
Skill nameexperiment-results-planning
Stars
3.2K
Forks
214
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Norman-bury on GitHub. Read the source before you install it.

Installation

Install the Experiment Results Planning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Norman-bury/research-writing-skill.git /tmp/research-writing-skill
mkdir -p .claude/skills
cp -r /tmp/research-writing-skill/skills/experiment-results-planning .claude/skills/experiment-results-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Experiment Results Planning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Experiment Results Planning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Experiment Results Planning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

markdown
| 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:

text
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:

text
[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.

Frequently asked questions

What does the Experiment Results Planning AI skill do?

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

Why use Experiment Results Planning on TypingMind?

Because you install it once and use it with any model. Experiment Results Planning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Experiment Results Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Norman-bury/research-writing-skill/tree/main/skills/experiment-results-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Experiment Results Planning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Experiment Results Planning?

As many as you like. As long as a model supports skills, you can use Experiment Results Planning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Experiment Results Planning AI skill free?

Yes. It is published on GitHub by Norman-bury under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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