An Agent Skill that watches upcoming AI/ML conferences and reports, for each one: submission deadlines (every stage), dates, location, special sessions / call for papers, official web links, and a 5-year acceptance-rate trend. It is written to be reusable across common coding agents that can search the web — Claude Code, Codex, and similar.

Every run yields two artifacts:

1. conferences.<date>.json — the structured source of truth. One object per conference (keyed by acronym), each holding the upcoming edition (deadlines, location, special sessions, links) and a 5-year acceptance-rate trend. Schema: references/json-schema.md.
2. conference-report.<date>.md — a human-readable report with a short summary, a nearest-deadlines table, a per-conference breakdown, and a clearly separated "approximate / inferred" section. Template: assets/report.template.md.

The agent also answers directly in chat with the most time-sensitive items.

This skill is plain Markdown + JSON, so it can be used by Codex, Claude Code, or any agent that can read SKILL.md, search/fetch the web, and write files.

Clone directly into Codex's personal skills directory:

git clone https://github.com/Zsun79/ConferenceWatch.git ~/.codex/skills/conference-watch

If you already cloned the project somewhere else, run this from the root of this cloned skill repository to link the current checkout into Codex:

mkdir -p ~/.codex/skills
ln -s "$(pwd)" ~/.codex/skills/conference-watch

Then ask Codex naturally for AI/ML conference deadlines, venue comparisons, or submission planning. Codex will use SKILL.md as the workflow when the request matches the skill description.

Clone directly into Claude's personal skills directory:

git clone https://github.com/Zsun79/ConferenceWatch.git ~/.claude/skills/conference-watch

If you already cloned the project somewhere else, run this from the root of this cloned skill repository to link the current checkout into Claude:

mkdir -p ~/.claude/skills
ln -s "$(pwd)" ~/.claude/skills/conference-watch

If this checkout is the project where you want Claude to load the skill, run this from the repository root to link the current directory into .claude/skills/conference-watch:

mkdir -p .claude/skills
ln -s "$(pwd)" .claude/skills/conference-watch

The skill activates from its description when your request matches.

For agents without a dedicated skill folder, use this repository as a local instruction bundle:

• Point the agent at SKILL.md as the task instructions.
• Keep references/conference-catalog.md, references/json-schema.md, and the files in assets/ available as supporting context.
• Ask the agent to follow the workflow and write the JSON/report artifacts into your project folder.

Requirement: the host agent must have a web search / fetch capability (and, to save files, filesystem access). No API keys or build step required.

> What are the deadlines for the top NLP conferences in the next 6 months?
> Track NeurIPS, ICML, and ICLR 2026 for me.
> Compare the top-tier CV venues by acceptance rate and next deadline.
> List the top 5 AI conferences with deadlines before end of 2026.
> I work in computer vision -- track the A* venues and show acceptance-rate trends.
> Just give me the top conferences, I don't want to specify an area.

The full logic lives in SKILL.md. In short:

flowchart TD
    A[Anchor on today's date] --> B[Analyze request and narrow scope]
    B --> C{Named venues or clear filters?}
    C -- No --> D[Ask 1-3 follow-up questions<br/>or use top-venue defaults]
    C -- Yes --> E[Build initial conference JSON]
    D --> E
    E --> F[Confirm candidate conference list]
    F --> G[Research upcoming edition first<br/>official CFP and important dates]
    G --> H[Collect every deadline stage,<br/>location, sessions, and links]
    H --> I[Gather last 5 editions<br/>acceptance-rate trend]
    I --> J{Upcoming dates confirmed?}
    J -- Yes --> K[Mark confirmed with sources]
    J -- No --> L[Infer approximate window<br/>from historical pattern]
    K --> M[Validate JSON]
    L --> M
    M --> N[Write JSON dataset]
    M --> O[Write Markdown report]
    N --> P[Answer in chat<br/>nearest deadlines first]
    O --> P

1. Anchor on today's date — all "future vs past" decisions are relative to the current date, not the model's training cutoff.
2. Analyze the request & narrow scope — the agent asks 1–3 follow-up questions to filter by area (NLP/CV/RL/…), reputation/tier (CORE A*/A/B), difficulty (acceptance rate), and horizon. If you decline, it defaults to the top AI venues (see references/conference-catalog.md).
3. Build the initial JSON — the candidate conference list is written out first, then confirmed with you before the long search phase.
4. Research the upcoming edition first — searches official CFP/"important dates" pages, records every deadline stage, location, special sessions, and links; marks them confirmed with sources.
5. Historical anchor (last 5 editions) — builds the acceptance-rate trend and, when a future edition isn't announced yet, infers an approximate deadline window from the historical pattern — always flagged as approximate.
6. Output — validates the JSON, writes the Markdown report with a short synthesized summary, and answers in chat.

Core rule: never fabricate dates. Anything not on an authoritative source is either null (with a note) or clearly marked approximate/inferred.

ConferenceWatch/
├── SKILL.md                          # main skill: workflow + rules (entry point)
├── README.md                         # this file
├── references/
│   ├── conference-catalog.md         # curated venues by area/tier + source sites
│   └── json-schema.md                # JSON schema + field rules
└── assets/
    ├── conference-data.template.json # starting JSON structure
    └── report.template.md            # Markdown report template

• Deadlines and rankings change; the skill confirms on official sites and cites sources, but always re-check the CFP before submitting.
• Acceptance-rate numbers can differ by source (main track vs. all tracks); the skill records the source alongside each figure.
• Inferred future dates are estimates only — labeled approximate everywhere.
• ⚠️ Generated reports are marked PRIVATE classification by default; handle the output accordingly.