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Research Agent

Community
ngtiendong
research-agent

Use this skill as a human-guided research agent for Master and PhD work in computer science, AI, mathematics, engineering, and related technical fields. Use it for research scoping, source ingestion, literature grounding, novelty review, mathematical formalization, reality and feasibility audits, experiment planning or execution, reframing a paper, reviewer simulation, and claim verification—especially when a plan looks polished but may depend on unverified data, metrics, interventions, access, resources, or compound hypotheses.

Overview

Publisherngtiendong
RepositoryAcademic-Research-Agent-Skill
Skill nameresearch-agent
Stars
69
Forks
10
Bundled files
64
LicenseMIT
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.

  • 64 bundled files

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

  • Open source

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

Installation

Install the Research Agent 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/ngtiendong/Academic-Research-Agent-Skill/tree/main
  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/ngtiendong/Academic-Research-Agent-Skill.git \
  .claude/skills/research-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Agent 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 Agent 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 Agent 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 Agent Skill

Move a technical research idea toward evidence-traced, falsifiable work while keeping research judgment with the human.

Core contract

  • Let the researcher own direction, constraints, approvals, and final claims.
  • Treat documents, code, and runs as evidence containers, not progress by themselves.
  • Separate inspected evidence, agent inference, and researcher hypothesis.
  • Prefer the cheapest decisive falsifier before broad planning or implementation.
  • Stop, drop, or narrow work when a mandatory claim node fails; do not salvage every null result into a new story.
  • Maintain a traceable chain from research question and contribution to formal claim, closest competitor, falsifier or experiment/result ID, evidence artifact, paper location, and residual risk.

Start every task

  1. Read the project state and local instructions.
  2. Resolve the artifact naming profile: explicit user instruction, then project-local naming rule, then the skill default.
  3. Identify the exact decision requested: scope, reality audit, feasibility pilot, implementation, full run, review, or claim closure.
  4. Read the smallest authoritative evidence set required for that decision.
  5. State the current gate, allowed work, prohibited work, and next decisive action.

Read workflow.md for lifecycle decisions. Read reality_gate.md before reframing, implementation planning, or any feasibility-sensitive experiment. Read the project-local file naming rule before creating or renaming artifacts.

Default workflow

  1. Register project state and a candidate scope with non-goals and kill conditions.
  2. Ingest and inspect sources before using them as evidence.
  3. Ground the candidate against closest prior work.
  4. Formalize the minimum claim, measurement, and assumptions needed to falsify it.
  5. Run the novelty gate.
  6. Run the Research Reality Gate on the actual experimental unit, prerequisite capability, measurement, intervention, preprocessing/access path, competitor delta, valid yield, and joint dependencies.
  7. Execute only the bounded evidence-recovery task or feasibility pilot authorized by that verdict.
  8. Freeze surviving claims and their evidence links before creating risk, work-breakdown, and code-execution plans.
  9. Execute approved implementation and full runs only after their separate gates pass.
  10. Simulate reviewers, verify claims, and write only to the strength of inspected evidence.

When a novelty nucleus, experimental unit, or central claim changes, return to scope, evidence, formalization, novelty, and reality gates. Do not patch downstream plans.

When a model, scale, dataset, processor, or configuration changes, record the substitution immediately and reopen every certificate or claim whose validity depended on the old choice. Do not assume transfer silently.

Gate language

Keep novelty and execution decisions separate.

  • Before a gate has enough evidence to be attempted, record NOT_ASSESSED; do not use REVISE or BLOCK merely as a synonym for missing prerequisites.
  • Novelty: PASS, REVISE, or FAIL with an explicit residual delta.
  • Reality/execution: BLOCK, FEASIBILITY_PILOT_ONLY, EXECUTION_READY, or FULL_RUN_READY.
  • Claims: hypothesis, supported, partially-supported, contradicted, unverified, or dropped.

Never call an engineering smoke test a feasibility pass. Never let a numeric score override a failed critical gate.

Plan-depth limits

  • Under BLOCK, authorize only corrective evidence work with a cost ceiling and stop condition.
  • Under FEASIBILITY_PILOT_ONLY, create only artifacts needed to close unknown reality certificates.
  • Create a broad WBS, code architecture, model grid, or full draft only after the relevant execution gate passes.
  • Require a claim-eligible pilot, frozen primary contrast, complete provenance, and human approval before a full run.

Artifact rules

  • Materialize only the artifact needed for the current decision; never create the whole lifecycle package because the filenames are known.
  • For a raw idea with uninspected sources, create/update only state and candidate scope. Add a source index only when source identities or files can be recorded. Keep preliminary blockers inline rather than manufacturing formalization, novelty, Reality Gate, or pilot artifacts.
  • Create literature artifacts only after sources are inspected; create a formalization only after the question and measurement obligations are stable enough to define; create a formal Reality Gate only when a measurable candidate and a named unit/access path exist or the user requests a feasibility-sensitive decision.
  • Create an experimental-unit audit plan only when an actual dataset/access target is named. Create a feasibility-pilot protocol only under FEASIBILITY_PILOT_ONLY or mark a user-requested future protocol explicitly NOT AUTHORIZED. Create mechanism-method routing only after a signal survives or when the researcher explicitly requests a conditional branch map.
  • Treat artifact type as semantic; numbering is a naming profile, not a license to repurpose a reserved slot.
  • Reuse the project-local profile when it exists.
  • In an isolated reframe folder, let the folder carry the candidate name; do not repeat the acronym in every filename.
  • Do not create empty files to fill numbering gaps.
  • Do not duplicate a state, scope, claim ledger, or decision log when an equivalent artifact exists.
  • Validate an artifact set with scripts/validate_artifact_set.py when creating or renaming multiple files.

Output contract

For substantial work, return:

  1. Status and current gate;
  2. Evidence inspected;
  3. Decision and exact authorized scope;
  4. Prohibited work until the next gate;
  5. Researcher decision, only when required;
  6. Learning value;
  7. Next step with owner, acceptance criterion, maximum cost, and stop condition;
  8. artifacts changed and validation performed.

References

Load only what the task needs:

Bundled files

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

and 4 more files.

Frequently asked questions

What does the Research Agent AI skill do?

Use this skill as a human-guided research agent for Master and PhD work in computer science, AI, mathematics, engineering, and related technical fields. Use it for research scoping, source ingestion, literature grounding, novelty review, mathematical formalization, reality and feasibility audits, experiment planning or execution, reframing a paper, reviewer simulation, and claim verification—especially when a plan looks polished but may depend on unverified data, metrics, interventions, access, resources, or compound hypotheses.

Why use Research Agent on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ngtiendong/Academic-Research-Agent-Skill/tree/main. 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 Agent?

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 Agent?

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

Is the Research Agent AI skill free?

Yes. It is published on GitHub by ngtiendong 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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