Agent Skills

vibe-research-workflow

Guides AI-assisted research across three sub-flows, Vibe Coding, Vibe Figure, and Vibe Writing, with behavioural rules that keep the user in charge of academic judgment while delegating mechanical work to AI. Recommends the right tool (Cursor, Claude Code, Codex, Figma, Gemini) for the current stage. Use when the user asks 'how to use AI for research', 'Vibe Coding tips', 'AI-assisted writing workflow', 'which AI tool for this', or starts an AI-assisted work session.

Install

npx skills add https://github.com/hkustdial/supervisor-skills --skill vibe-research-workflow
SKILL.md

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 paper prose drafted. That is legitimate under this plugin's evidence discipline and belongs to the drafting skills: route to paper-writer (any section) or intro-drafter (Introductions), whose rules forbid fabricated substance (every factual claim traces to the user's materials, verified retrieval, or field common knowledge). Remind the user that venue and school AI-disclosure policies still apply, and that they remain responsible for verifying every drafted passage against their actual research. What stays forbidden is fabrication, not drafting.
  • 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 idea-evaluator (see handbook 2.3 for disruptive-innovation deep-dive).

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. Drafting assistance is legitimate within that boundary: the substance belongs to the user; the linguistic realization may be AI-assisted; the verification duty (rule 3) is unchanged.
  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

  • Primary phase:
  • Secondary phases:

2. Behavioural rules recap

  • Rule 1-6 (see references/behavior-guidelines.md): acknowledged

3. Workflow plan

Time block Phase Activity Tool User check
... ... ... ... ...

4. Tool recommendations

Phase Primary tool Alternative Reason
Coding ... ... ...
Figure ... ... ...
Writing ... ... ...

5. Red-line reminders

  • ... (from references/vibe-writing.md)

6. Integrity gate plan

  • Verification points: ...
  • AI-disclosure requirements for the target venue: ...

Related skills

researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers