Phd Vibe Research Workflow

Vibe Research Workflow

Overview

Vibe Research is the modern research workflow where large language models and AI coding tools handle mechanical tasks (implementation, figure rendering, language polish) while the researcher retains full ownership of research direction, problem framing, experimental design, and factual accuracy. The goal is a two-to-five times productivity gain on routine tasks without compromising academic integrity.

The skill has three sub-flows: Vibe Coding (AI-assisted code), Vibe Figure (AI-assisted figure production), Vibe Writing (AI-assisted prose polish). Each is governed by six behavioural rules that draw a hard line between acceptable use (mechanical acceleration, auxiliary suggestions, style correction) and academic misconduct (fabricated citations, outsourced scientific judgment, hidden AI authorship).

This skill is a meta-skill that orchestrates tool selection, flow design, and integrity enforcement across a research session. It consolidates the the curriculum's Vibe Research section into a single invocable procedure.

When to use this skill

• Starting a new AI-assisted work block (a morning of coding, a figure day, a writing session).
• The user asks 'how to use AI for research', 'Vibe Coding tips', 'AI-assisted writing workflow', 'which AI tool for this'.
• The user is choosing between Cursor, Claude Code, Codex, Figma, Gemini, or other tools.
• The user wants a workflow plan for a multi-day project involving AI.
• The user suspects AI output has drifted into unacceptable territory (fabricated citations, outsourced reasoning).

When NOT to use this skill

• The user wants AI to generate the paper directly. Reject politely; the integrity rules forbid it. Redirect to phd-intro-drafter, phd-tech-paper-template, or phd-pre-submission-reviewer depending on intent.
• The user wants a code implementation done. This skill guides the process; it does not replace the implementation itself.
• The user wants to evaluate research direction. Use phd-idea-evaluator (see phd-idea-evaluator and its reference files for deeper disruptive-innovation evaluation).

Core procedure

Step 1: Phase classification

Decide which phase the user is in: coding, figure, writing, or mixed.

Step 2: Behavioural rules recap

See: references/behavior-guidelines.md for the full six-rule set.

State the six rules succinctly at the start of the session:

1. AI-assisted work is permitted for literature search and organisation, code and debugging support, language and expression polish.
2. Research ideas, problems, designs, technical paths, experimental plans, core conclusions, and novelty must be the user's own and fully understood.
3. Every AI-generated or AI-assisted passage is verified by the user against the actual research process, experimental results, and facts.
4. No fabricated citations; references come from the user's own reading and confirmation.
5. No academic misconduct, including fabricated data, experimental results, or plagiarism concealment.
6. Venue or school AI-disclosure requirements are honoured.

These rules are non-negotiable and enforced in the integrity gate.

Step 3: Phase-specific procedure

For Vibe Coding, see: references/vibe-coding.md.

For Vibe Figure, see: references/vibe-figure.md.

For Vibe Writing, see: references/vibe-writing.md.

Each phase has its own core techniques (Plan Mode, Small Steps, Clear Requirements for coding; four-step figure workflow; red-line rules for writing).

Step 4: Tool selection

See: references/tool-selection.md for the tool matrix.

Match tool to phase:

• Coding: Cursor (IDE-native) or Claude Code (agentic CLI) or Codex.
• Figure: PowerPoint plus Figma for static figures; Matplotlib plus Seaborn for experimental results; Gemini for first-draft sketches.
• Writing: Claude or ChatGPT for language polish; Grammarly for grammar; Overleaf for LaTeX.

Step 5: Integrity gate

Before closing the session, run the checks in the Integrity gate section below.

Step 6: Output

Emit the workflow plan in the Output format below.

Integrity gate

This skill is a behavioural nudge, not a verification engine. Most bullets are tagged [user-attest] because the LLM cannot actually observe the user's private verification work. [inspection] tags apply only to checks the LLM can confirm from its own outputs and the user's session history.

Before ending the session:

1. [inspection] The six behavioural rules have been stated at the start of the session.
2. [user-attest] No fabricated citation has been introduced or accepted. (The LLM cannot verify citations without web access; the user confirms via DBLP or arXiv.)
3. [user-attest] The user's research direction, framing, and contributions are owned by the user, not by AI.
4. [user-attest] Every AI-generated code block has been reviewed and tested by the user.
5. [user-attest] Every AI-drafted paragraph has been rewritten or at minimum sentence-by-sentence verified by the user.
6. [attestation] Venue or school AI-disclosure rules have been checked. The skill asks the user to name the venue and surfaces known policy types; the user confirms compliance.
7. [user-attest] The user's own expertise is still driving the project; AI is an accelerator, not a replacement.

Any red-line violation (rules 1-6) stops the session. The user should fix the violation or consult an advisor before continuing. Because most bullets are [user-attest], the skill's effective value is the reminder at session start, not a runtime block.

Output format

1. Phase

2. Behavioural rules recap

3. Workflow plan

4. Tool recommendations

5. Red-line reminders

6. Integrity gate plan