Research Orchestration

Community

Firstp1ck

research-orchestration

Agents should invoke this skill for broad multi-claim research projects needing planning, parallel investigation, source merging, gap closure, citation audit, and final synthesis when narrower research skills are insufficient.

Overview

Publisher	Firstp1ck
Repository	`pi-coding-agent-forge`
Skill name	`research-orchestration`
Stars	93
Forks	22
Bundled files	6
License	MIT
Links	Skill source Repository

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.
6 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Firstp1ck on GitHub. Read the source before you install it.

Installation

Install the Research Orchestration 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/Firstp1ck/pi-coding-agent-forge/tree/main/pi-skill-research-orchestration/skills/research-orchestration
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Firstp1ck/pi-coding-agent-forge/tree/main/pi-skill-research-orchestration/skills/research-orchestration

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/Firstp1ck/pi-coding-agent-forge.git /tmp/pi-coding-agent-forge
mkdir -p .claude/skills
cp -r /tmp/pi-coding-agent-forge/pi-skill-research-orchestration/skills/research-orchestration .claude/skills/research-orchestration

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

Use it in TypingMind

Enable Research Orchestration 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 Orchestration 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 Orchestration 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 orchestration

Single workflow entry point for end-to-end investigations. Detailed rules live in workspace-researcher/AGENTS.md (task class, parallel investigation, Deep Research Protocol, citation audit); this skill is the ordered checklist for executing them.

When to use

Broad question with several independent sub-questions or claim clusters.
Mixed sources (web + docs + papers) and one final report or contract output.
User or delegating agent asked for deep research without pointing at a single comparison or single paper.

Prefer tech-deep-dive, competitor-analysis, or paper-summarizer when the task clearly matches one of those shapes.

Pipeline

Classify depth — Quick / standard / deep (AGENTS.md task-class table). Honor explicit quick / standard / deep or /deep-research quick|standard|max when the user names them. Set expected tool budget.
Plan sub-questions — List facets and claims to verify; note which are high-stakes (citation audit mandatory). Prefer internal → official → web (AGENTS.md data source order) when gathering evidence.
Parallel pass — Batch web_search / web_fetch per independent facet; optional subagents with tight scopes and structured handoff.
Merge — One synthesis pass; resolve contradictions; prefer primary sources.
Gap closure — List unresolved material gaps; targeted follow-up searches or explicit “blocked” notes.
Citation audit — For mandatory cases: every key finding → source row or unsupported / inferential (AGENTS.md).
Deliver — Default report shape, contract JSON, or consumer-specific packet from AGENTS.md; include Limitations and Research trace when depth is standard/deep or requested (APPEND_SYSTEM.md).
Log — Append a row to workspace-researcher/MEMORY.md → Research History (topic, task class, tool bucket, major gaps, notable sources).

Scout scripts → pipeline step

Run from the installed skill directory, or adjust paths to absolute locations. Policy defaults to ./scripts/policy.json next to the bundled scripts.

Step	Script	When
After depth + topic fixed	`./scripts/scout_query_plan.py`	Emit ordered queries (`Q001`…) and `plan_hash` for the research trace
After URL collection	`./scripts/scout_normalize_sources.py`	Dedupe and sort sources before the final table
Long / multi-session runs	`./scripts/scout_evidence_bundle.py`	Freeze fetch records (JSONL → `evidence_bundle.json` + `content_sha256`)
Before high-stakes delivery	`./scripts/scout_citation_audit.py`	Gate: every `key_findings[]` row has `source_ids` or `unsupported` / `inferential`

Details and exit codes: ./scripts/README.md.

Handoff schema (optional, for subagents)

markdown
## Scope
[One sentence]

## Findings
- [Bullet + URL]

## Gaps
- [What could not be verified]

## Sources touched
- [Title](URL) — quality flag

Scout skill — Orchestrated research pipeline · 2026-04-10

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 Research Orchestration AI skill do?

Why use Research Orchestration on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Firstp1ck/pi-coding-agent-forge/tree/main/pi-skill-research-orchestration/skills/research-orchestration. 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 Research Orchestration?

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 Orchestration?

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

Is the Research Orchestration AI skill free?

Yes. It is published on GitHub by Firstp1ck 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

Research orchestration

When to use

Pipeline

Scout scripts → pipeline step

Handoff schema (optional, for subagents)

Bundled files

Frequently asked questions

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