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Develop Method To Sota

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gxCaesar
develop-method-to-sota

Use when a method is not yet beating its baseline and the next change has to be chosen, including improving a model, reaching state of the art, deciding which component to add next, measuring headroom before building, designing an ablation control that can void a component, reading error by slice instead of one aggregate, or deciding whether to keep iterating or stop. Use during exploratory development on a development split, before any confirmatory run is frozen.

Overview

PublishergxCaesar
Repositoryopen-research-skills
Skill namedevelop-method-to-sota
Stars
63
Forks
3
Bundled files
8
LicenseApache-2.0
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gxCaesar on GitHub. Read the source before you install it.

Installation

Install the Develop Method To Sota 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.
    https://github.com/gxCaesar/open-research-skills/tree/main/skills/develop-method-to-sota
  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/gxCaesar/open-research-skills.git /tmp/open-research-skills
mkdir -p .claude/skills
cp -r /tmp/open-research-skills/skills/develop-method-to-sota .claude/skills/develop-method-to-sota
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Develop Method To Sota 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 Develop Method To Sota 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 Develop Method To Sota 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.

Develop a Method to State of the Art

Turn a validated pilot into a method worth freezing. The loop is: measure what is available to win before building anything, read the error where it actually lives, add one component at a time with a mechanism hypothesis and a control that could void it, and stop for a stated reason rather than from fatigue.

This skill governs the exploratory phase only. Iterating after a confirmatory run is frozen is an unblinding event, not development.

Select one mode

ModeResponsibilityRead first
headroom-measureEstablish the ceiling and the measured noise floor before designing anythingreferences/headroom-and-noise-floor.md
error-slicesLocate the loss by stratum rather than in one aggregate numberreferences/error-slices.md
single-componentDesign, implement and test one component with a control that can void itreferences/component-iteration.md
exit-decisionDecide freeze, continue, or return to the charter, and name which exit appliesreferences/exit-decisions.md

Run headroom-measure first on any task the loop has not yet measured. A component designed before the ceiling is known is a guess about where the loss is.

Resolve the installed skill and components

Resolve SKILL_DIR as the directory containing this SKILL.md. Every runtime resource is below that directory; never resolve through a package parent.

Measure before building

Two numbers precede design work, and both are measured rather than claimed.

Headroom is how much of the metric is still available on this task with this representation. It is a property of the task and the representation together, not of the task alone, so it is measured per task and never extrapolated from a neighbour. Ask first whether the current representation is the strongest one obtainable, because a weak representation manufactures headroom that a better one would remove.

The noise floor is the spread of the same code, on the same split, across seeds, library versions and environments. A reported margin smaller than the measured noise floor carries no information. A bootstrap standard error is not a noise floor: it describes resampling of one run, not re-execution of the pipeline. Pair the runs when pairing is possible instead of differencing two point estimates.

When headroom is at or below the measured noise floor, the task is saturated for this representation, and that is one of the three exits below.

Order the search

Cheap and general changes come before expensive and specific ones. Architecture is last because it is the most expensive place to be wrong.

  1. Recipe: optimiser, schedule, regularisation, batch composition
  2. Objective alignment: what the loss actually rewards versus what the metric scores
  3. Data: quantity, cleaning, augmentation, weighting, label quality
  4. Ensembling and aggregation
  5. Test-time computation
  6. Architecture

Record the order number of every candidate. A candidate at order 6 does not get a second attempt while lower orders remain unexplored.

One component, one mechanism, one control

Each iteration states a mechanism hypothesis before the run: which failure the component is supposed to remove, and on which slice the improvement should therefore appear. A component that improves the aggregate but not its stated slice has not been shown to work by the mechanism claimed.

The control arm destroys the mechanism and preserves everything else. The second half of that sentence is the half that gets violated: a control that still applies some other real intervention will recover most of the gain and will make a working component look dead. Never issue a void verdict from a single seed.

Deduplicate candidates by construction shape rather than by name. Write one line saying what the candidate operates on and what it does to it; a candidate with the same shape as a killed one inherits that kill unless you can say in one sentence which computation differs.

Three legal exits

Stopping requires naming which exit applies. If none applies, the loop continues.

  1. Measured headroom is at or below the measured noise floor.
  2. The pre-registered budget is exhausted. This returns the decision to a person; it is not a licence to freeze on your own.
  3. The only component that worked has been published by someone else. This returns to the charter, which is also not a stop.

"Several things were tried and none worked" is not an exit. Before claiming saturation, an oracle arm must have been run — a model given an input that is unavailable at deployment — so that the remaining gap is known to exist.

Boundaries

This skill does not decide whether an idea is novel, does not run the confirmatory campaign, and does not write the paper. It reports what a development split shows. A development-split result is not a finding until a frozen run reproduces it.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Develop Method To Sota AI skill do?

Use when a method is not yet beating its baseline and the next change has to be chosen, including improving a model, reaching state of the art, deciding which component to add next, measuring headroom before building, designing an ablation control that can void a component, reading error by slice instead of one aggregate, or deciding whether to keep iterating or stop. Use during exploratory development on a development split, before any confirmatory run is frozen.

Why use Develop Method To Sota on TypingMind?

Because you install it once and use it with any model. Develop Method To Sota 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 Develop Method To Sota in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gxCaesar/open-research-skills/tree/main/skills/develop-method-to-sota. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Develop Method To Sota?

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 Develop Method To Sota?

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

Is the Develop Method To Sota AI skill free?

Yes. It is published on GitHub by gxCaesar under the Apache-2.0 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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