A skill your agent uses when reasoning about how an ICSE research-track submission is evaluated, covering double-anonymous PC review, the four posted criteria, the Accept / Major Revision / Reject…

MITAuto-check passedData & Analytics

Install Icse Review Process

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-review-process -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-review-process --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ICSE-Skills/skills/icse-review-process .claude/skills/icse-review-process && rm -rf skills-src

Use ~/.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/

Facts

Skill name
icse-review-process
GitHub stars
1.2k
Token cost
~1.5k tokens
SKILL.md length
751 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when reasoning about how an ICSE research-track submission is evaluated, covering double-anonymous PC review, the four posted criteria, the Accept / Major Revision / Reject…

  • Works in 4 steps: Novelty — originality of solutions,… → Rigor — soundness, clarity, and depth of… → Relevance — significance and potential… → …
  • Reasoning about how an ICSE research-track submission is evaluated
  • SKILL.md covers The four scored criteria, Decision model and 2027 timeline, What the recent numbers say and Who reads you, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Icse Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how an ICSE research-track submission is evaluated, covering double-anonymous PC review, the four posted criteria, the Accept / Major Revision / Reject decision model, the revision-and-final-decision timeline, and what recent ICSE acceptance statistics imply for strategy.

Its SKILL.md is about 1.5k 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 Data & Analytics, covering Statistics. 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

  • Reasoning about how an ICSE research-track submission is evaluated
  • Covering double-anonymous PC review
  • The four posted criteria
  • The Accept / Major Revision / Reject decision model

Example prompts

  • “/icse-review-process”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Novelty — originality of solutions, problem formulations, methodologies,
  2. Rigor — soundness, clarity, and depth of the technical or theoretical
  3. Relevance — significance and potential impact on software engineering.
  4. Verifiability and Transparency — whether the paper contains enough

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

Icse Review Process loads about 1.5k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 751 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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). 751 words, ~1,535 tokens.

Download SKILL.mdSave it as .claude/skills/icse-review-process/SKILL.md (or your agent's skills folder).
name
icse-review-process
description
Use when reasoning about how an ICSE research-track submission is evaluated, covering double-anonymous PC review, the four posted criteria, the Accept / Major Revision / Reject decision model, the revision-and-final-decision timeline, and what recent ICSE acceptance statistics imply for strategy.

ICSE Review Process

Model the pipeline before you interpret any single review. ICSE's process differs from most CS conferences in one structural way: Major Revision is a real decision category, not a euphemism for rejection, and a large share of accepted papers arrive through it.

The four scored criteria

The ICSE 2027 call (read 2026-07-08) evaluates each paper on:

  1. Novelty — originality of solutions, problem formulations, methodologies, theories, or evaluations relative to the state of the art.
  2. Rigor — soundness, clarity, and depth of the technical or theoretical contribution, and thoroughness/completeness of the evaluation.
  3. Relevance — significance and potential impact on software engineering.
  4. Verifiability and Transparency — whether the paper contains enough information to understand how the innovation works, how data was obtained and analyzed, and whether independent verification or replication is supported.

Read every review comment as an instance of one of these. "The benchmark seems small" is a Rigor objection; "practitioners would not use this" is Relevance; "the prompt templates are not shown" is Verifiability. Diagnosing the criterion tells you whether the cure is new evidence, new framing, or new packaging.

Decision model and 2027 timeline

Stage (2027 cycle)DateWhat happens
Submission closesJun 30, 2026 AoEPC bidding and assignment follow
Author responseSep 2026Authors see reviews and reply (format 待核实)
First decisionsOct 20, 2026Accept / Major Revision / Reject
Revision dueNov 17, 2026Revised paper + response for MR papers
Final MR decisionsDec 18, 2026Accept or Reject, no second revision
ConferenceApr 25 – May 1, 2027Dublin; core days Apr 28–30

Two consequences. First, a Major Revision gives you roughly four weeks to execute — reviewers expect the revision plan to be feasible in that window, so promising a new user study is self-defeating. Second, the December decision is terminal for the cycle: there is no minor-revision escape hatch after it.

What the recent numbers say

ICSE 2026 (the last two-cycle year) reported, per its research-track pages: Cycle 1 — 660 submissions, 60 direct accepts, 101 accepted after Major Revision; Cycle 2 — 809 submissions, 72 direct accepts, 88 accepted after Major Revision; 321 of 1,469 total (~22%). The strategic reading: direct acceptance is rare (~9%) — the modal successful path runs through Major Revision. Write the initial submission so that its weaknesses are revisable (missing analysis, unclear framing) rather than structural (wrong population, no baseline), because the process is built to reward repairable papers.

Who reads you

Reviews come from a large research-track Program Committee working double-anonymously; ICSE also runs a Shadow PC in some years (2027 posts one) where early-career researchers review in parallel for training — their reviews do not decide outcomes but signal how a non-expert reads your paper. Expect three reviews with SE-empiricist instincts: they will look for the threats-to- validity section, check whether claims outrun evidence, and often open the replication package.

Show full SKILL.md (280 more words)Show less

Where author leverage exists

text
[Before submission]   topic tags -> reviewer pool     (largest lever)
[Author response]     factual corrections, criterion-targeted evidence
[Major Revision]      the strongest lever in the system: a tracked-change
                      revision + response letter reviewed by the same PC members
[After final reject]  no appeal lane; reroute (FSE/ASE/ISSTA or journals)

Author response moves borderline papers when it corrects a factual misreading or supplies a number a reviewer said was missing. It does not move papers when it argues taste. Major Revision moves papers when every requested change is either made or explicitly declined with a reason — silent omissions are what turn December into a rejection.

Reading a review packet

Weight reviews before answering them. Look for: specificity — a review citing your section numbers and exact tables was read closely and will be read closely again in December; criterion coverage — a review that only discusses novelty has left rigor and verifiability to the others, so answer those reviewers on those axes; the question list — reviewers often end with explicit questions, and the September response is scored heavily on whether each got a direct answer. A short, vague, positive review is worth less protection than a long, critical, specific one: the latter's author is your likely advocate if the response holds up.

Misreadings to avoid

  • Treating Major Revision as a soft accept. ICSE 2026's numbers show most MR papers do get in, but the December reject is real; budget the four weeks like a deadline, not a formality.
  • Treating the response as a debate stage. The PC discussion after the response is where decisions form; your text is evidence for an advocate, not a closing argument to a jury.
  • Assuming reviewer unanimity is required. Discussion consensus, guided by the criteria, decides — one enthusiastic champion with answers to the other reviews can carry a paper.
  • Projecting the two-cycle rhythm forward. 2025 and 2026 ran two cycles; 2027 posts one. Never infer next year's calendar from last year's.

Output format

text
[Process stage] pre-submission / awaiting reviews / response / major revision / final
[Criterion map] each review point -> novelty | rigor | relevance | verifiability
[Decision forecast] direct accept / MR-likely / reject-risk, with reasons
[Leverage plan] what to do at the next stage that can actually change the outcome

© 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 ICSE-Skills/skills/icse-review-process of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Icse 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.

Icse Review Process compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Icse Review Process this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.5kAutomated safety check: PassMIT
Sandbox Benchvercel/next.js143k—~4.1kAutomated safety check: PassMIT
Statistical Analysisspacering-net/codeg3.9k3 repos~5kAutomated safety check: PassMIT
StatsmodelszLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
AI Daily DigestvigorX777/ai-daily-digest1.6k—~1.3kAutomated safety check: PassNone
Statistical Powerspacering-net/codeg3.9k1 repos~3.6kAutomated safety check: NotesMIT

Similar skills

  • Sandbox Bench

    vercel/next.js

    Official

    Benchmark React or Next.js changes on Vercel Sandbox VMs with paired A/B statistics: react PR/commit vs base, or Next.js PR/commit vs base, measured end-to-end through the bench/render-pipeline app…

    143k GitHub stars~4.1k tokensUpdated today
    Data & AnalyticsAuto-check passed
  • Statistical Analysis

    spacering-net/codeg

    Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting.

    3.9k GitHub starsUsed in 3 repos~5k tokens
    Data & AnalyticsAuto-check passed
  • Statsmodels

    zLanqing/codex-claude-academic-skills

    Statistical models library for Python. An agent skill from zLanqing/codex-claude-academic-skills.

    4.7k GitHub starsUsed in 15 repos~4.9k tokens
    Data & AnalyticsAuto-check passed
  • AI Daily Digest

    vigorX777/ai-daily-digest

    Fetches RSS feeds from 90 top Hacker News blogs (curated by Karpathy), uses AI to score and filter articles, and generates a daily digest in Markdown with Chinese-translated titles, category…

    1.6k GitHub stars~1.3k tokensUpdated 7 mo ago
    Data & AnalyticsAuto-check passed
  • Statistical Power

    spacering-net/codeg

    Sample-size and statistical power calculations for planning studies.

    3.9k GitHub starsUsed in 1 repo~3.6k tokens
    Data & AnalyticsAuto-check: notes
  • Agent Session Monitor

    higress-group/higress

    Real-time agent conversation monitoring - monitors Higress access logs, aggregates conversations by session, tracks token usage.

    9.5k GitHub stars~3.3k tokensUpdated 2 days ago
    Data & AnalyticsAuto-check passed

More from brycewang-stanford/Awesome-Journal-Skills

All 2,387 skills in this repo
  • Aaag Data Analysis

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed
  • Aaag Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed
  • Aaag Rebuttal

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…

    1.2k GitHub stars~1.4k tokensUpdated 13 days ago
    Auto-check passed
  • Aaag Research Design

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…

    1.2k GitHub stars~1.4k tokensUpdated 13 days ago
    Auto-check passed
  • Aaag Review Process

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed
  • Aaag Submission

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…

    1.2k GitHub stars~1.6k tokensUpdated 13 days ago
    Auto-check passed

Questions about Icse Review Process

What does Icse Review Process do?

A skill your agent uses when reasoning about how an ICSE research-track submission is evaluated, covering double-anonymous PC review, the four posted criteria, the Accept / Major Revision / Reject…. Icse Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how an ICSE research-track submission is evaluated, covering double-anonymous PC review, the four posted criteria, the Accept / Major Revision / Reject decision model, the revision-and-final-decision timeline, and what recent ICSE acceptance statistics imply for strategy.

When should I use Icse Review Process?

Icse Review Process fits situations like: reasoning about how an ICSE research-track submission is evaluated; covering double-anonymous PC review; the four posted criteria; the Accept / Major Revision / Reject decision model.

How do I install Icse Review Process in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-review-process -a claude-code`. Or copy the skill folder (ICSE-Skills/skills/icse-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icse-review-process in your project. Claude Code loads it when a task matches its description.

How do I install Icse Review Process in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-review-process -a codex`. Or copy the skill folder (ICSE-Skills/skills/icse-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icse-review-process in your project. Codex loads it when a task matches its description.

Can I use Icse 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 icse-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/icse-review-process, .gemini/skills/icse-review-process, .github/skills/icse-review-process and .opencode/skills/icse-review-process in your project.

What does Icse Review Process need to run?

SKILL.md names no scripts, command-line tools or credentials: Icse Review Process is instructions for the agent only.

Does Icse 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 Icse 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 Icse Review Process use?

Icse 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 Icse Review Process use?

About 1.5k tokens (SKILL.md is roughly 6.1k 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 Icse Review Process?

Skills that share tags, products or a category with Icse Review Process: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars) and AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Icse Review Process?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.