Ds Full Pipeline

OrganizationPopular

OpenLAIR

ds-full-pipeline

Full DeepScientist research pipeline: scout → baseline → idea → experiment → analysis → optimize → write → review → finalize. End-to-end autonomous research lifecycle.

Overview

Publisher	OpenLAIR
Repository	`dr-claw`
Skill name	`ds-full-pipeline`
Stars	1.2K
Forks	136
Bundled files	Instructions only
License	MIT
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 OpenLAIR on GitHub. Read the source before you install it.

Installation

Install the Ds Full Pipeline 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/OpenLAIR/dr-claw/tree/main/skills/ds-full-pipeline
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/OpenLAIR/dr-claw.git /tmp/dr-claw
mkdir -p .claude/skills
cp -r /tmp/dr-claw/skills/ds-full-pipeline .claude/skills/ds-full-pipeline

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

Use it in TypingMind

Enable Ds Full Pipeline 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 Ds Full Pipeline 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 Ds Full Pipeline 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.

DeepScientist Full Pipeline

End-to-end autonomous research workflow for: $ARGUMENTS

Overview

This skill chains all DeepScientist research stages into a single pipeline:


/ds-scout → /ds-baseline → /ds-idea → /ds-experiment → /ds-analysis-campaign → /ds-optimize → /ds-write → /ds-review → /ds-finalize

Pipeline

Stage 1: Scout

Frame the research problem, survey literature, identify datasets/metrics, discover existing baselines.


/ds-scout "$ARGUMENTS"

Output: Problem framing, literature map, baseline shortlist, evaluation contract.

🚦 Gate 1: Present the research landscape to the user. Wait for confirmation before proceeding.

Stage 2: Baseline

Reproduce or import the most relevant baseline from Stage 1's shortlist.


/ds-baseline

Output: Working baseline with verified metrics, comparability contract.

Stage 3: Idea

Generate concrete research hypotheses based on the literature gaps and baseline analysis.


/ds-idea

Output: Ranked candidate ideas with selection rationale.

🚦 Gate 2: Present top ideas to the user. Wait for confirmation of which idea to pursue.

Stage 4: Experiment

Implement and run the main experiment for the selected idea.


/ds-experiment

Output: Experiment code, results, evidence artifacts.

Stage 5: Analysis Campaign

Run follow-up experiments: ablations, robustness checks, error analysis.


/ds-analysis-campaign

Output: Ablation results, robustness data, writing-facing evidence slices.

Stage 6: Optimize (Optional)

If results are promising but not yet strong enough, run algorithm-first iterative improvement.


/ds-optimize

Skip this stage if main experiment results already meet the success criteria.

Stage 7: Write

Draft the paper from accepted evidence.


/ds-write

Output: LaTeX paper draft with figures and references.

Stage 8: Review

Run an independent skeptical audit of the draft.


/ds-review

Output: Review report with severity-graded feedback.

If review identifies critical issues → fix and re-review (max 2 rounds).

Stage 9: Finalize

Consolidate final claims, limitations, and recommendations.


/ds-finalize

Output: Final paper, summary state, resume packet.

Key Rules

Gate checkpoints after Scout and Idea stages. Do not proceed without user confirmation on research direction and idea selection.
Stages 4-9 can run autonomously once the user confirms the idea.
Evidence-first writing. Every claim in the paper must trace to an experiment artifact.
Fail gracefully. If any stage fails, report clearly and suggest alternatives rather than forcing forward.
Git as memory. Commit after each stage so progress is durable.

Typical Timeline

Stage	Duration	Autonomous?
1. Scout	20-40 min	Wait for Gate 1
2. Baseline	15-60 min	Yes
3. Idea	15-30 min	Wait for Gate 2
4. Experiment	30 min - hours	Yes
5. Analysis	30-60 min	Yes
6. Optimize	0-60 min	Yes (optional)
7. Write	30-60 min	Yes
8. Review	15-30 min	Yes
9. Finalize	10-20 min	Yes

Frequently asked questions

What does the Ds Full Pipeline AI skill do?

Full DeepScientist research pipeline: scout → baseline → idea → experiment → analysis → optimize → write → review → finalize. End-to-end autonomous research lifecycle.

Why use Ds Full Pipeline on TypingMind?

Because you install it once and use it with any model. Ds Full Pipeline 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 Ds Full Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenLAIR/dr-claw/tree/main/skills/ds-full-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ds Full Pipeline?

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 Ds Full Pipeline?

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

Is the Ds Full Pipeline AI skill free?

Yes. It is published on GitHub by OpenLAIR 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

DeepScientist Full Pipeline

Overview

Pipeline

Stage 1: Scout

Stage 2: Baseline

Stage 3: Idea

Stage 4: Experiment

Stage 5: Analysis Campaign

Stage 6: Optimize (Optional)

Stage 7: Write

Stage 8: Review

Stage 9: Finalize

Key Rules

Typical Timeline

Frequently asked questions

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