DeepTutor CLI
HKUDS/DeepTutor
Teaches the agent to set up and run DeepTutor from the command line: chat and capabilities, knowledge bases, partners, memory, sessions, notebooks and the server or Web app.
A skill your agent uses when interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles, the A/ARR/RR/RRX/X recommendation scale, desk-reject and rubric-based assisted desk-reject…
Install Chi Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-review-process --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/CHI-Skills/skills/chi-review-process .claude/skills/chi-review-process && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "chi-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-process into .claude/skills/chi-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-review-process", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-processType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-review-process --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/CHI-Skills/skills/chi-review-process .agents/skills/chi-review-process && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "chi-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-process into .agents/skills/chi-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-review-process", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-review-process --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/CHI-Skills/skills/chi-review-process .cursor/skills/chi-review-process && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "chi-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-process into .cursor/skills/chi-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-review-process", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path CHI-Skills/skills/chi-review-process--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-review-process --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/CHI-Skills/skills/chi-review-process .gemini/skills/chi-review-process && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "chi-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-process into .gemini/skills/chi-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-review-process", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-review-processInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/CHI-Skills/skills/chi-review-process .github/skills/chi-review-process && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "chi-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-process into .github/skills/chi-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-review-process", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills chi-review-process --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/CHI-Skills/skills/chi-review-process .opencode/skills/chi-review-process && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "chi-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CHI-Skills/skills/chi-review-process into .opencode/skills/chi-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chi-review-process", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
chi-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 649 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles, the A/ARR/RR/RRX/X recommendation scale, desk-reject and rubric-based assisted desk-reject…
- Works in 2 steps: Desk reject (DR): format and compliance… → Assisted desk reject (ADR): a…
- Interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles
- SKILL.md covers Who touches your paper, Two kinds of early rejection, Round 1: the recommendation… and Round 2: revision, then the PC…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Chi Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles, the A/ARR/RR/RRX/X recommendation scale, desk-reject and rubric-based assisted desk-reject screening, the revise-and-resubmit threshold, round-2 PC decisions — and what each stage means for authors.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Education, covering Quizzes and assessments. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles
- The A/ARR/RR/RRX/X recommendation scale
- Desk-reject and rubric-based assisted desk-reject screening
- The revise-and-resubmit threshold
Example prompts
- “/chi-review-process”
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- Desk reject (DR): format and compliance — wrong template, anonymization
- Assisted desk reject (ADR): a rubric-based judgment call made jointly by SCs
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Chi Review Process loads about 1.4k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 649 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 649 words, ~1,396 tokens.
Download SKILL.mdSave it as
.claude/skills/chi-review-process/SKILL.md (or your agent's skills folder).- name
- chi-review-process
- description
- Use when interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles, the A/ARR/RR/RRX/X recommendation scale, desk-reject and rubric-based assisted desk-reject screening, the revise-and-resubmit threshold, round-2 PC decisions — and what each stage means for authors.
CHI Review Process
CHI's Papers track runs a two-round, committee-anchored process that resembles a journal's revise-and-resubmit more than an ML conference's rebuttal. Mechanics below follow the CHI 2027 and CHI 2026 Papers Review Process pages and the CHI 2026 outcome reports (read 2026-07-08 via search renderings; the conference sites block direct automated fetching).
Who touches your paper
- 1AC — the Associate Chair who manages your submission: screens it, recruits two external reviewers, writes the meta-review, and represents the paper at the PC meeting. The 1AC is your most important reader at CHI.
- 2AC — a second committee member who writes a full review like the externals.
- Two external reviewers — recruited by the 1AC for topical expertise.
- Subcommittee chairs (SCs) — run the subcommittee you designated, arbitrate desk rejects with the ACs, and chair the discussions.
- Papers Chairs / PC — own the overall process and the final accept list.
Two kinds of early rejection
CHI screens before full review, and since the 2026 cycle this screening has teeth:
- Desk reject (DR): format and compliance — wrong template, anonymization failure, unjustified length (the >12,000-word rule), policy violations.
- Assisted desk reject (ADR): a rubric-based judgment call made jointly by SCs and ACs, introduced for CHI 2026 and codified by the CHI Steering Committee. The four posted rubric grounds:
| Rubric code | Meaning |
|---|---|
| ADR-Context | Grossly insufficient literature review to contextualize the contribution |
| ADR-Contribution | Disproportionately small HCI contribution for the paper's length |
| ADR-Data | Grossly insufficient data to support the claims |
| ADR-Method | Grossly insufficient methodological detail, conceptual clarity, or research transparency |
At CHI 2026, subcommittees desk-rejected between 1.4% and 17.9% of their submissions
and assisted-desk-rejected between 1.1% and 16.6%, with ADR-Context the most common
rubric ground — weak related work is now a pre-review rejection risk, not just a
score penalty (chi-related-work).
Round 1: the recommendation scale
Reviewers and ACs recommend on a five-point scale:
- A — Accept
- ARR — Accept with Required Revisions
- RR — Revise and Resubmit
- RRX — Revise and Resubmit / Reject (leaning reject)
- X — Reject
No paper is accepted in round 1. The posted threshold: a paper earning at least one recommendation of RR or better (A, ARR, or RR — not RRX or X) from the 1AC or the 2AC is invited to revise and resubmit. External reviewers' scores inform but do not trigger the invitation; the AC pair holds the keys.
Show full SKILL.md (268 more words)Show less
Round 2: revision, then the PC meeting
Invited authors get about five weeks to submit a tracked-changes revision plus a
response to the reviews (mechanics in chi-author-response). The same review team
re-reads, and the PC meeting settles accepts and rejects. The process page warns that
as many as 50% of revised papers may still be rejected; the realized CHI 2026 numbers:
CHI 2026 Papers funnel (posted outcome reports, chi2026.acm.org):
6,730 completed submissions
→ 2,603 invited to revise and resubmit (38.7% of submissions survived round 1)
→ 2,576 actually resubmitted (27 withdrawn/lapsed)
→ 1,705 conditionally accepted (65.5% of resubmissions; 25.3% overall)Read your round-1 packet against this funnel: an RR invitation means you are in the top ~39%, with roughly two-in-three odds that depend heavily on revision quality.
Reading the reviews you receive
- Weigh the 1AC meta-review above everything; it frames the PC-meeting discussion and tells you which criticisms the committee actually owns.
- An RRX from an external with RR from the ACs is survivable; the inverse pattern (ACs at RRX/X) means the invitation, if it comes at all, is fragile.
- Contradictory externals are common at CHI because subcommittees mix methodological cultures; resolve contradictions by aligning with the meta-review, and flag the contradiction politely in the response rather than picking a side silently.
- "The contribution is unclear" from multiple reviewers is a framing defect you can fix in five weeks; "the study cannot support these claims" usually is not — decide early whether to revise seriously or withdraw and redo the work for next cycle.
Confidentiality and conduct
Submissions are confidential to the review team; contacting reviewers or ACs about a paper outside PCS channels violates the process. Reviewer anonymity is permanent. Public complaints during the cycle can only hurt — escalation happens through the subcommittee chairs and Papers Chairs, in writing, via PCS or the posted contacts.
Output format
[Stage] screening / round-1 / RnR window / round-2 / decided
[Screening exposure] DR: <format risks> · ADR: <weakest rubric ground>
[Round-1 read] 1AC: <score> 2AC: <score> ext: <scores> → invitation likely? yes/no
[Meta-review center of gravity] <the criticism the PC will discuss>
[Realistic odds] <calibrated vs the ~65% resubmission acceptance base rate>
[Action] <revise seriously / fix framing / withdraw and re-route>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in CHI-Skills/skills/chi-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Chi Review Process next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Chi Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| DeepTutor CLIHKUDS/DeepTutor | 41k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch | 66k | — | ~2k | Automated safety check: Pass | MIT | |
| Codebase to Coursezarazhangrui/codebase-to-course | 5.7k | — | ~4.4k | Automated safety check: Pass | None | |
| AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch | 66k | — | ~2.1k | Automated safety check: Pass | MIT | |
| Scholar EvaluationK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~2.9k | Automated safety check: Notes | MIT |
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Categories
Questions about Chi Review Process
What does Chi Review Process do?
A skill your agent uses when interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles, the A/ARR/RR/RRX/X recommendation scale, desk-reject and rubric-based assisted desk-reject…. Chi Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles, the A/ARR/RR/RRX/X recommendation scale, desk-reject and rubric-based assisted desk-reject screening, the revise-and-resubmit threshold, round-2 PC decisions — and what each stage means for authors.
When should I use Chi Review Process?
Chi Review Process fits situations like: interpreting the ACM CHI Papers review pipeline — 1AC and 2AC roles; the A/ARR/RR/RRX/X recommendation scale; desk-reject and rubric-based assisted desk-reject screening; the revise-and-resubmit threshold.
How do I install Chi Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a claude-code`. Or copy the skill folder (CHI-Skills/skills/chi-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/chi-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Chi Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a codex`. Or copy the skill folder (CHI-Skills/skills/chi-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/chi-review-process in your project. Codex loads it when a task matches its description.
Can I use Chi Review Process in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill chi-review-process -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chi-review-process, .gemini/skills/chi-review-process, .github/skills/chi-review-process and .opencode/skills/chi-review-process in your project.
What does Chi Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Chi Review Process is instructions for the agent only.
Does Chi Review Process access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Chi Review Process safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Chi Review Process use?
Chi Review Process is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Chi Review Process use?
About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Chi Review Process?
Skills that share tags, products or a category with Chi Review Process: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 66k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 66k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Chi Review Process?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.