Executing Analysis

Organization

K-Dense-AI

executing-analysis

Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents

Overview

Publisher	K-Dense-AI
Repository	`science-superpowers`
Skill name	`executing-analysis`
Stars	360
Forks	37
Bundled files	Instructions only
Links	Skill source Repository Homepage

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 K-Dense-AI on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/K-Dense-AI/science-superpowers/tree/main/skills/executing-analysis
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.

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/K-Dense-AI/science-superpowers.git /tmp/science-superpowers
mkdir -p .claude/skills
cp -r /tmp/science-superpowers/skills/executing-analysis .claude/skills/executing-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Executing Analysis 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 Executing Analysis 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 Executing Analysis 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.

Executing the Analysis

Overview

Load the pre-registered plan, review it critically, execute all steps exactly as registered, report when complete.

Announce at start: "I'm using the executing-analysis skill to run this analysis."

Note: Tell your human partner that Science Superpowers works much better with access to subagents — the protocol-compliance and rigor reviews catch more when run by fresh agents. If subagents are available, use science-superpowers:subagent-driven-analysis instead.

Prerequisite

The plan MUST be pre-registered and frozen (science-superpowers:preregistering-analysis) and the workspace set up (science-superpowers:setting-up-reproducible-analysis). If either is missing, stop and do it first. Executing before freezing turns the analysis exploratory.

The Process

Step 1: Load and Review

Read the analysis plan and the frozen pre-registration.
Review critically — any concern about the design, the decision rule, or feasibility?
If concerns: raise them with your human partner before starting. A change to the registered analysis is a documented deviation and renders that part exploratory.
If no concerns: create a TodoWrite with all steps and proceed.

Step 2: Execute Steps

For each step:

Mark in_progress.
For nontrivial estimators: validate the pipeline on known/simulated data first.
Run the step exactly as registered. Keep the seed. Read immutable raw / named derived; write a new artifact.
Validate the output (shape, ranges, counts) — nothing changes silently.
Apply the registered decision rule as written; do not re-decide it after seeing the number.
Use science-superpowers:verifying-results-before-claiming before marking done.
Commit. Mark completed.

Checkpoints

After each natural group of steps (e.g., data prep, then primary model), pause and report results to your human partner before continuing. Keep confirmatory and exploratory results clearly separated in every report.

Step 3: Complete

After all steps complete and verified:

Use science-superpowers:requesting-red-team-review on the whole result.
Then science-superpowers:reporting-and-archiving-findings.

When to Stop and Ask

STOP immediately when:

A result is surprising, impossible, or contradicts a sanity check → use science-superpowers:investigating-anomalous-results (do NOT quietly drop data or tweak parameters)
The plan requires deviating from the pre-registration to proceed
A validation fails repeatedly
An instruction is unclear

Ask rather than guess. A guess that changes the analysis silently destroys the result's credibility.

Remember

Pre-registration frozen before execution
Validate pipelines on known data
Run steps exactly as registered; never silently deviate
Label anything unregistered as exploratory
Don't skip validation or verification
Stop on anomalies; investigate by root cause

Integration

Required workflow skills:

science-superpowers:setting-up-reproducible-analysis — run first
science-superpowers:preregistering-analysis — frozen before execution
science-superpowers:investigating-anomalous-results — on surprising results
science-superpowers:verifying-results-before-claiming — before any completion claim
science-superpowers:reporting-and-archiving-findings — after all steps complete

Frequently asked questions

What does the Executing Analysis AI skill do?

Use when you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents

Why use Executing Analysis on TypingMind?

Because you install it once and use it with any model. Executing Analysis 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 Executing Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/science-superpowers/tree/main/skills/executing-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Executing Analysis?

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 Executing Analysis?

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

Is the Executing Analysis AI skill free?

It is published on GitHub by K-Dense-AI. Check the repository for licensing terms. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Executing the Analysis

Overview

Prerequisite

The Process

Step 1: Load and Review

Step 2: Execute Steps

Checkpoints

Step 3: Complete

When to Stop and Ask

Remember

Integration

Frequently asked questions

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