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Agent skill

Experiment Results Planning

by Norman-bury in Norman-bury/research-writing-skill

A skill your agent uses when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final

MITAuto-check passedAgent Workflows

Install Experiment Results Planning

skills CLI
$ npx skills add Norman-bury/research-writing-skill --skill experiment-results-planning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Norman-bury/research-writing-skill experiment-results-planning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Norman-bury/research-writing-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/experiment-results-planning .claude/skills/experiment-results-planning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
experiment-results-planning
GitHub stars
3.4k
Token cost
~990 tokens
SKILL.md length
411 words
Files
1
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final

  • Works in 6 steps: Gate D0: Experiment Protocol Locked → Gate D1: Method-Experiment Traceability → Gate D2: Table/Figure Data Contract → …
  • Designing experiments
  • SKILL.md covers Hard Gate, Experiment Protocol, Recommended Experiment Gates and Method-Experiment Traceability, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Experiment Results Planning is an agent skill from Norman-bury/research-writing-skill. Use when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final

Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. The repository describes itself as: 科研写作助手 (Research Writing Assistant). The licence is MIT.

When your agent uses it

  • Designing experiments
  • Mock planning data
  • Evaluation protocols
  • Results sections before real data are final

Example prompts

  • “/experiment-results-planning”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Gate D0: Experiment Protocol Locked
  2. Gate D1: Method-Experiment Traceability
  3. Gate D2: Table/Figure Data Contract
  4. Gate D3: Main/Efficiency/Ablation/Generalization/XAI Results
  5. Gate D4: Result Chapter Decontamination
  6. Gate D5: Peer Review Pass

What it can do on your machine

Read from SKILL.md and the folder at commit 6f79595. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Experiment Results Planning loads about 990 tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 411 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~990

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Norman-bury/research-writing-skill at commit 6f79595, republished under its MIT licence (© Norman-bury). 411 words, ~990 tokens.

Download SKILL.mdSave it as .claude/skills/experiment-results-planning/SKILL.md (or your agent's skills folder).
name
experiment-results-planning
description
Use when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final

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.

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.

Show full SKILL.md (182 more words)Show less

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.

© Norman-bury, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/experiment-results-planning of Norman-bury/research-writing-skill.

Open the folder on GitHubat commit 6f79595

Compare with similar skills

Experiment Results Planning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Experiment Results Planning compared with similar skills
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Experiment Results Planning this skillNorman-bury/research-writing-skill3.4k—~990Automated safety check: PassMIT
MCP Server Builderanthropics/skills180k63 repos~2.3kAutomated safety check: PassApache-2.0
Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k10 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k36 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers297k2 repos~5.1kAutomated safety check: PassMIT
Skill CreatorAzure/azqr79689 repos~8.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Experiment Results Planning

What does Experiment Results Planning do?

A skill your agent uses when designing experiments, result tables, mock planning data, evaluation protocols, or results sections before real data are final. Experiment Results Planning is an agent skill from Norman-bury/research-writing-skill.

When should I use Experiment Results Planning?

Experiment Results Planning fits situations like: designing experiments; mock planning data; evaluation protocols; results sections before real data are final.

How do I install Experiment Results Planning in Claude Code?

Run `npx skills add Norman-bury/research-writing-skill --skill experiment-results-planning -a claude-code`. Or copy the skill folder (skills/experiment-results-planning in Norman-bury/research-writing-skill) into .claude/skills/experiment-results-planning in your project. Claude Code loads it when a task matches its description.

How do I install Experiment Results Planning in Codex?

Run `npx skills add Norman-bury/research-writing-skill --skill experiment-results-planning -a codex`. Or copy the skill folder (skills/experiment-results-planning in Norman-bury/research-writing-skill) into .agents/skills/experiment-results-planning in your project. Codex loads it when a task matches its description.

Can I use Experiment Results Planning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Norman-bury/research-writing-skill --skill experiment-results-planning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/experiment-results-planning, .gemini/skills/experiment-results-planning, .github/skills/experiment-results-planning and .opencode/skills/experiment-results-planning in your project.

What does Experiment Results Planning need to run?

SKILL.md names no scripts, command-line tools or credentials: Experiment Results Planning is instructions for the agent only.

Does Experiment Results Planning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Experiment Results Planning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Experiment Results Planning use?

Experiment Results Planning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Experiment Results Planning use?

About 990 tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Experiment Results Planning?

Skills that share tags, products or a category with Experiment Results Planning: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Experiment Results Planning?

Norman-bury (a GitHub user) maintains it in Norman-bury/research-writing-skill, which has 3,384 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on June 10, 2026.

Source: Norman-bury/research-writing-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.