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Reflect Paper

Community
ShaishavMaisuria
reflect-paper

Runs a two-pass paper reflection loop with a Researcher pass and a Writer pass. Use when the user asks to reflect on a paper, improve a draft, run a research-agent and writer-agent review, critique the paper before submission, find both technical and writing issues, or produce a revision plan. The Researcher pass checks claims, citations, evidence, venue fit, missing related work, and reviewer risk; the Writer pass checks framing, structure, abstract, related work, prose, tables/figures, and response clarity. Outputs a merged action plan with evidence, priority, owner, and next skill to run.

Overview

PublisherShaishavMaisuria
Repositoryresearch-paper-lifecycle-skills
Skill namereflect-paper
Stars
59
Forks
22
Bundled files
Instructions only
LicenseApache-2.0
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 ShaishavMaisuria on GitHub. Read the source before you install it.

Installation

Install the Reflect Paper 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/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/reflect-paper
  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/ShaishavMaisuria/research-paper-lifecycle-skills.git /tmp/research-paper-lifecycle-skills
mkdir -p .claude/skills
cp -r /tmp/research-paper-lifecycle-skills/skills/reflect-paper .claude/skills/reflect-paper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reflect Paper 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 Reflect Paper 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 Reflect Paper 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.

Reflect Paper

Run a structured reflection loop over a research-paper draft. Treat this as two separate perspectives, then merge them into one revision plan:

  • Researcher pass — technical truth, evidence, citations, venue fit, novelty, limitations, reviewer risk.
  • Writer pass — narrative, framing, section structure, abstract, related work placement, prose, figures/tables, reader flow.

This is an orchestration skill: it calls on sibling skills when their narrower checks are needed, but it owns the reflection protocol and final synthesis.

If the runtime supports delegated agents or subagents, split the reflection into two independent workers:

  1. Send a Researcher worker the draft, target venue/context, and the Researcher pass checklist only.
  2. Send a Writer worker the draft, target venue/context, and the Writer pass checklist only.
  3. Merge both outputs yourself; do not let either worker decide the final verdict alone.

If delegation is unavailable, run the same passes sequentially. Keep separate notes for each pass so technical-risk findings do not flatten writing feedback, and writing polish does not hide correctness concerns.

When to use

  • "Reflect on this paper before I submit"
  • "Run a research agent and writer agent on my draft"
  • "Critique this draft from both technical and writing angles"
  • "Give me a revision plan"
  • "What should I fix before sending this to my advisor / reviewers?"
  • "Do a final paper reflection pass"

Inputs

  • The draft: LaTeX source, PDF, markdown, Word-exported text, or pasted sections.
  • Optional: target venue or CFP URL.
  • Optional: bibliography file, related-work notes, reviews, or advisor feedback.
  • Optional: the user's current goal, such as submission readiness, camera-ready polish, rebuttal, or presentation prep.

Process

1. Scope The Reflection

Identify the lifecycle stage and what "good" means for this pass:

StagePrimary concern
Early draftMissing contribution, weak framing, incomplete evidence.
Pre-submissionVenue fit, page budget, anonymization, citation reliability, reviewer risk.
RebuttalReview coverage, evidence anchors, tone, response budget.
Camera-readyRequired final-file steps, de-anonymization, acknowledgments, source consistency.
PresentationTalk story, timing, Q&A risk, slide/poster readability.

If a venue or CFP is involved, use parse-cfp or add-venue-profile before relying on page limits, blind level, required sections, or deadlines.

2. Researcher Pass

Read for correctness and evidence. Check:

  • Core claim: what the paper says it contributes, and whether the evidence actually supports it.
  • Novelty and related work: missing comparisons, closest competitors, thin or overstated deltas.
  • Citations: unresolved entries, suspicious metadata, fabricated-looking references, uncited claims.
  • Methods and evaluation: assumptions, baselines, ablations, datasets, reproducibility, statistics, limitations.
  • Venue fit: track expectations, page budget, anonymity, required sections, LLM/AI-use policy, artifact expectations.
  • Reviewer risk: likely objections and what evidence would defuse them.

Delegate as needed:

NeedUse
Find missing related workfind-papers, literature-review
Fetch legal OA copiesfetch-paper
Check bibliographyverify-citations
Study venue exemplarsstudy-exemplars
Simulate reviewer risksimulate-reviewers
Check CFP/venue factsparse-cfp, select-venue, tailor-to-venue

Record findings as: finding, evidence, risk, suggested fix, and confidence.

3. Writer Pass

Read for communication and persuasion. Check:

  • One-sentence paper promise: can a reader say what the paper does and why it matters?
  • Abstract: motivation, gap, approach, result, impact, and venue-specific length/keyword rules.
  • Introduction: problem setup, contribution bullets, reader contract.
  • Related work: clustered themes and explicit deltas, not a laundry list.
  • Method and evaluation: section order, figure/table references, claim titles, readable transitions.
  • Prose: overclaiming, hedging, AI-sounding filler, terminology drift, passive contribution statements.
  • Figures and tables: captions, crossrefs, color accessibility, layout, and whether each visual answers a reader question.

Delegate as needed:

NeedUse
Abstract rewrite or lintwrite-abstract
Related work draftingdraft-related-work
Full review synthesisliterature-review
Prose polishpolish-prose
Tables and figurespolish-tables-figures
Talk/poster translationmake-slides, write-talk-script, make-poster

Record findings as: reader problem, location, revision, expected benefit, and next skill.

4. Reflection Merge

Merge the two passes into a prioritized plan. Do not average away conflicts: when the Researcher pass wants more caveats and the Writer pass wants a sharper claim, state the tradeoff and propose a wording that is both accurate and readable.

Use this priority scale:

PriorityMeaning
P0Blocks submission or creates serious correctness/integrity risk.
P1Likely affects review outcome or reader understanding.
P2Improves clarity, polish, or confidence.
P3Nice-to-have cleanup.

5. Output

Return a concise reflection report:

  1. Verdict — ready / close / needs major revision / not ready.
  2. Researcher pass — top technical, evidence, citation, and venue risks.
  3. Writer pass — top framing, structure, prose, and visual risks.
  4. Merged action plan — prioritized table with owner/pass, location, action, next skill, and estimated effort.
  5. Open questions — facts that require user input or live verification.

Guardrails

  • Never invent papers, results, deadlines, page limits, or venue rules.
  • Do not modify technical claims to sound stronger unless the evidence supports them.
  • Keep confidential reviews and unpublished paper content local.
  • For legal/copyright safety, fetch papers only from legal open-access sources and do not reproduce abstracts or paper text in the report.

Frequently asked questions

What does the Reflect Paper AI skill do?

Runs a two-pass paper reflection loop with a Researcher pass and a Writer pass. Use when the user asks to reflect on a paper, improve a draft, run a research-agent and writer-agent review, critique the paper before submission, find both technical and writing issues, or produce a revision plan. The Researcher pass checks claims, citations, evidence, venue fit, missing related work, and reviewer risk; the Writer pass checks framing, structure, abstract, related work, prose, tables/figures, and response clarity. Outputs a merged action plan with evidence, priority, owner, and next skill to run.

Why use Reflect Paper on TypingMind?

Because you install it once and use it with any model. Reflect Paper 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 Reflect Paper in TypingMind?

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

Which AI models can use Reflect Paper?

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 Reflect Paper?

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

Is the Reflect Paper AI skill free?

Yes. It is published on GitHub by ShaishavMaisuria under the Apache-2.0 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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