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Plan Submission

Community
ShaishavMaisuria
plan-submission

Builds a backwards submission timeline from a conference deadline - days remaining (AoE-aware), abstract-registration offsets, author-list freeze, OpenReview/CMT/EasyChair/HotCRP/PCS account lead times and per-system steps, supplementary-material and code size limits, reciprocal-reviewing duties, and a dual-submission audit. Use when the user wants to plan a submission, asks how long until a deadline, mentions abstract registration, submission checklist, deadline countdown, "what do I need before the NeurIPS/ICML/CHI deadline", or asks what happens after submitting (rebuttal window, notification, camera-ready). Phase-aware - produces pre-submission, under-review, and camera-ready plans from venues/ profiles and flags overdue steps.

Overview

PublisherShaishavMaisuria
Repositoryresearch-paper-lifecycle-skills
Skill nameplan-submission
Stars
59
Forks
22
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Plan Submission 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/plan-submission
  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/plan-submission .claude/skills/plan-submission
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan Submission 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 Plan Submission 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 Plan Submission 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.

Plan Submission

Turn "the deadline is in N weeks" into a dated, phase-aware milestone plan. Works backwards from the paper deadline (or forwards through rebuttal and camera-ready if the deadline already passed), anchored to a venues/conferences/<venue>-<year>.yml profile. Companion skills: parse-cfp (creates the profile), preflight-check (the T-3d desk-reject lint), write-rebuttal, prepare-camera-ready.

When to use

  • "Plan my SIGSPATIAL submission" / "what do I need to do before the deadline?"
  • "How many days until the NeurIPS deadline?" (AoE confusion included)
  • "When do I have to register the abstract?" / "when does the rebuttal start?"
  • The user just picked a venue (via select-venue) and asks "what now?"

Inputs

  • A venue + year (and ideally a track). The profile must exist at venues/conferences/<venue>-<year>.yml; if it does not, run parse-cfp first — never plan from remembered dates.
  • Today's date, and any user-specific constraints (vacations, co-author availability, experiments still running).
  • CONTACT_EMAIL env var if the live re-verification fetch is used.

Process

  1. Resolve the venue profile. Locate venues/conferences/<venue>-<year>.yml. Missing or from a previous cycle → route through parse-cfp to create/refresh it before planning. Check the profile's verified: block — if confidence is not verified-live or verified.date is old, treat every date as suspect.

  2. Re-verify the critical dates against the live CFP. Mandatory before the user relies on the plan. Run:

    python3 scripts/fetch_page.py "<cfp_url from the profile>"

    Confirm the abstract deadline, paper deadline, and timezone on the live page (deadlines slip mid-cycle — extensions are common, but so are silent corrections). On mismatch, update the profile first. If the page is JS-rendered or down, say so and mark the plan "dates unverified".

  3. Build the timeline. Run:

    python3 scripts/build_timeline.py venues/conferences/<venue>-<year>.yml \
        --track "<track>" --today YYYY-MM-DD

    The script is offline and deterministic: it detects the current phase (pre-submission / under-review / camera-ready), computes days remaining for every profile deadline, and emits dated milestones with standard backwards offsets (T-21d dual-submission audit and reciprocal-reviewing check, T-14d system accounts, T-10d author-list freeze, abstract registration at its real date, T-5d supplementary, T-3d preflight, T-2d draft upload, T-1d final upload). --camera-ready YYYY-MM-DD supplies a deadline the profile lists as null. --format json for machine use.

  4. Customize the milestones using references/milestone-playbook.md: what each milestone actually requires, why the offset is what it is, supplementary/code size limits, reciprocal-reviewing mechanics, and the AoE math. Adjust offsets for the user's constraints (e.g. co-author on leave at T-10d → freeze the author list earlier). Never move a HARD deadline; only prep milestones are adjustable.

  5. Add per-system steps from references/submission-systems.md for the profile's review.submission_system — account lead times (OpenReview profile moderation can take ~2 weeks), what the submission form demands, conflict-of-interest entry, and each system's signature gotcha (HotCRP's "ready for review" checkbox, PCS metadata lock, EasyChair co-author email typos, CMT COI lists).

  6. Run the dual-submission audit interactively. Quote the profile's review.dual_submission policy verbatim, then ask the user to inventory every overlapping manuscript by ANY co-author that is under review or planned elsewhere (including workshop versions and arXiv plans). Flag conflicts against the quoted policy; when the policy is ambiguous, say so and point at the CFP — do not adjudicate.

  7. Present the plan: phase, key-dates table with days remaining, milestone table (OVERDUE items first, called out explicitly), per-system steps, policy gates, and the re-verify warning. If the paper deadline has already passed, do NOT pretend otherwise — plan the next phase and say plainly which deadlines are gone.

Output

A dated submission plan in Markdown: current phase, key dates with days-remaining (timezone noted), backwards milestone schedule with status (in Nd / TODAY / OVERDUE / PASSED), submission-system checklist, policy gates (dual submission, LLM policy, reciprocal reviewing), and the closing notice: "Dates from <profile> (verified <date>). Re-verified against <cfp_url> on <today>. Deadlines change mid-cycle — check the CFP again before each milestone."

Guardrails

  • Never invent a date. A null in the profile stays unknown in the plan, with an instruction on where to find it — a guessed deadline is worse than none.
  • Always state the timezone next to every deadline. AoE is common, not universal (SIGSPATIAL uses Pacific Time); a wrong assumption costs a day.
  • Never submit, register, create accounts, or click anything in any submission system on the user's behalf. The plan tells the user what to do; the user does it.
  • Past deadlines are reported as passed, never silently dropped or rescheduled.
  • One page per fetch-script invocation; cache stays under .cache/ (gitignored); never commit fetched CFP text.

Bundled files

  • references/milestone-playbook.md — what every milestone requires, default offsets and their rationale, supplementary/code limits, reciprocal reviewing, dual-submission audit, AoE math.
  • references/submission-systems.md — per-system walkthroughs: OpenReview, CMT, EasyChair, HotCRP, PCS.
  • scripts/build_timeline.py — deterministic phase-aware timeline builder (run it; do not compute date arithmetic by hand).
  • scripts/fetch_page.py — polite single-page fetcher for live CFP re-verification (vendored from parse-cfp so this skill is self-contained).
  • scripts/venue_profile.py — shared profile loader (YAML-subset parser + family merge), vendored for self-containment.

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 Plan Submission AI skill do?

Builds a backwards submission timeline from a conference deadline - days remaining (AoE-aware), abstract-registration offsets, author-list freeze, OpenReview/CMT/EasyChair/HotCRP/PCS account lead times and per-system steps, supplementary-material and code size limits, reciprocal-reviewing duties, and a dual-submission audit. Use when the user wants to plan a submission, asks how long until a deadline, mentions abstract registration, submission checklist, deadline countdown, "what do I need before the NeurIPS/ICML/CHI deadline", or asks what happens after submitting (rebuttal window, notific...

Why use Plan Submission on TypingMind?

Because you install it once and use it with any model. Plan Submission 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 Plan Submission in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/plan-submission. 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 Plan Submission?

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 Plan Submission?

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

Is the Plan Submission 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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