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Research Pipeline Runner

Community
WILLOSCAR
research-pipeline-runner

Run a research Workflow end to end when the user requests a survey, brief, review, tutorial, or idea exploration; route an unbound goal, execute one eligible Unit at a time, and stop at checkpoints or diagnosed failures.

Overview

PublisherWILLOSCAR
Repositoryresearch-units-pipeline-skills
Skill nameresearch-pipeline-runner
Stars
513
Forks
39
Bundled files
Instructions only
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 WILLOSCAR on GitHub. Read the source before you install it.

Installation

Install the Research Pipeline Runner 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/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/research-pipeline-runner
  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/WILLOSCAR/research-units-pipeline-skills.git /tmp/research-units-pipeline-skills
mkdir -p .claude/skills
cp -r /tmp/research-units-pipeline-skills/.codex/skills/research-pipeline-runner .claude/skills/research-pipeline-runner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Pipeline Runner 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 Research Pipeline Runner 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 Research Pipeline Runner 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.

Research Pipeline Runner

The runner turns one requested outcome into an auditable Workspace. Its leading word is continuation: every action must leave enough durable state for the next command or agent to continue without reconstructing the Run from chat.

Inputs

  • The user Goal, including the target deliverable and constraints.
  • An existing Workspace, or a path under workspaces/<name>/ for a new one.
  • The selected Pipeline when already recorded in PIPELINE.lock.md.

Outputs

  • A Workspace containing GOAL.md, PIPELINE.lock.md, UNITS.csv, STATUS.md, CHECKPOINTS.md, and DECISIONS.md.
  • Pipeline-declared intermediate and target Artifacts.
  • Run Evidence under .harness/ and output/.

Steps

1. Bind the Goal to one Workflow

Read the Goal and existing Workspace state. When no valid Pipeline lock exists, use pipeline-router; load docs/PIPELINE_TAXONOMY.md only for that routing branch. Preserve an existing valid lock.

Completion criterion: PIPELINE.lock.md names exactly one Pipeline and its Unit template, and UNITS.csv belongs to that contract.

2. Reconcile before execution

Run the Workspace Doctor or the equivalent Harness inspection. Resolve recoverable projections first. Route integrity errors to an explicit repair; retain all earlier Attempts and Decisions.

Completion criterion: the Workspace has either one eligible next Unit or one specific blocking condition with a recorded next action.

3. Execute one Unit

For the next eligible Unit:

  1. read its owner, dependencies, inputs, outputs, acceptance rule, and Skill;
  2. open the Attempt through the Pipeline adapter;
  3. follow that Skill's SKILL.md and selectively load its context pointers;
  4. verify every required output and acceptance condition;
  5. commit Completion through the Pipeline adapter.

Completion criterion: the Unit is committed as DONE with matching Attempt, Manifest, and Artifact evidence, or it is BLOCKED with a diagnosable Failure.

4. Respect the stop line

The stop line is the first HUMAN checkpoint, unresolved Decision, failed quality contract, missing required Artifact, or terminal execution error. Summarize what exists, name the evidence to inspect, and leave one concrete resume command or question.

Completion criterion: the Run never advances beyond a stop line, and another operator can identify the blocker from Workspace files without chat history.

5. Continue to the declared outcome

Repeat reconciliation and single-Unit execution while an eligible Unit exists. After the final Unit, run Audit and Artifact Pack generation.

Completion criterion: target Artifacts exist, required Evaluations pass, Audit contains no blocking issue, and the Artifact Pack indexes the delivery and Run Evidence.

Branch Pointers

  • Workflow selection: read docs/PIPELINE_TAXONOMY.md, then the chosen pipelines/*.pipeline.md. The taxonomy owns the routing catalog; this Skill does not duplicate it.
  • Survey strict mode: read the locked Survey Pipeline's quality contract and the active delivery profile before writing. Branch-specific markers and gates live there.
  • Offline retrieval: use the selected retrieval Skill's documented import path and preserve source provenance.
  • Manual Unit execution: use unit-executor while retaining the same Completion Protocol.

Operator Commands

Use the outcome-first CLI for normal operation:

bash
uv run rh goal create --topic "<topic>" --workflow <workflow> --workspace workspaces/<name>
uv run rh run start --workspace workspaces/<name>
uv run rh run resume --workspace workspaces/<name>
uv run rh run status --workspace workspaces/<name>
uv run rh evidence inspect --workspace workspaces/<name>
uv run rh improve diagnose --workspace workspaces/<name>

Use scripts/pipeline.py for maintainer debugging, explicit approvals, and manual status transitions. The Pipeline adapter remains the only supported path for changing Unit state.

Frequently asked questions

What does the Research Pipeline Runner AI skill do?

Run a research Workflow end to end when the user requests a survey, brief, review, tutorial, or idea exploration; route an unbound goal, execute one eligible Unit at a time, and stop at checkpoints or diagnosed failures.

Why use Research Pipeline Runner on TypingMind?

Because you install it once and use it with any model. Research Pipeline Runner 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 Research Pipeline Runner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/WILLOSCAR/research-units-pipeline-skills/tree/main/.codex/skills/research-pipeline-runner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research Pipeline Runner?

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 Research Pipeline Runner?

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

Is the Research Pipeline Runner AI skill free?

It is published on GitHub by WILLOSCAR. 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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