Arize Evaluator
github/awesome-copilot
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and…
A skill your agent uses when reasoning about how a USENIX FAST submission is evaluated, covering double-blind program-committee review with outside referees, the author-response (rebuttal) period…
MITAuto-check passed
Install Fast Review Process
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-review-process -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-review-process .claude/skills/fast-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 "fast-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-review-process into .claude/skills/fast-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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/FAST-Skills/skills/fast-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 fast-review-process -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-review-process .agents/skills/fast-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 "fast-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-review-process into .agents/skills/fast-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-review-process -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-review-process .cursor/skills/fast-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 "fast-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-review-process into .cursor/skills/fast-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 FAST-Skills/skills/fast-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 fast-review-process -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fast-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/FAST-Skills/skills/fast-review-process .gemini/skills/fast-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 "fast-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-review-process into .gemini/skills/fast-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-review-process .github/skills/fast-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 "fast-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-review-process into .github/skills/fast-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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 fast-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 fast-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/FAST-Skills/skills/fast-review-process .opencode/skills/fast-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 "fast-review-process" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAST-Skills/skills/fast-review-process into .opencode/skills/fast-review-process/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fast-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
fast-review-process- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 706 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 a USENIX FAST submission is evaluated, covering double-blind program-committee review with outside referees, the author-response (rebuttal) period…
- Reasoning about how a USENIX FAST submission is evaluated
- SKILL.md covers Process model, Reading a decision against the…, What a one-shot revision… and How FAST differs from its…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering double-blind program-committee review with outside referees
What it does
Fast Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how a USENIX FAST submission is evaluated, covering double-blind program-committee review with outside referees, the author-response (rebuttal) period, the Accept / Accept-with-shepherding / One-shot-Revision / Reject decision set, how a one-shot revision differs from a journal R&R and from OSDI/ATC handling, and where author leverage exists.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
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 a USENIX FAST submission is evaluated
- Covering double-blind program-committee review with outside referees
- The author-response (rebuttal) period
- The Accept / Accept-with-shepherding / One-shot-Revision / Reject decision set
Example prompts
- “/fast-review-process”
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
Fast Review Process loads about 1.6k tokens when it runs. Until then it costs about 97 tokens; SKILL.md has 706 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). 706 words, ~1,620 tokens.
Download SKILL.mdSave it as
.claude/skills/fast-review-process/SKILL.md (or your agent's skills folder).- name
- fast-review-process
- description
- Use when reasoning about how a USENIX FAST submission is evaluated, covering double-blind program-committee review with outside referees, the author-response (rebuttal) period, the Accept / Accept-with-shepherding / One-shot-Revision / Reject decision set, how a one-shot revision differs from a journal R&R and from OSDI/ATC handling, and where author leverage exists.
FAST Review Process
Model the pipeline before interpreting any single review. FAST — the USENIX Conference on File and Storage Technologies — uses the USENIX systems-review machinery: double-blind program-committee review, an author-response period, PC shepherding for accepted papers, and a one-shot revision track. The most consequential mental shift for authors is understanding that FAST has four outcomes, not two, and that a one-shot revision is a real, bounded second chance — not a soft accept and not an open-ended journal R&R.
Process model
- Submission and review run on HotCRP (a separate instance per deadline) with double-blind anonymity: identities are hidden from reviewers; authors anonymize the PDF and references and refer to their own prior work in the third person.
- The program committee reviews, assisted by outside referees when needed. Storage papers are matched to reviewers from their subarea (file systems, SSD/NVM, KV stores, caching, reliability), so device details and evaluation state are scrutinized, not skimmed.
- There is an author-response (rebuttal) period before notification: a short window to answer reviewer questions and correct misreadings.
- First decisions fall into Accept, Accept-with-shepherding, One-shot Revision, or Reject. Accepted papers are shepherded by a PC member to closure. Proceedings are open access via USENIX.
Reading a decision against the categories
| Decision | What it means | Author move |
|---|---|---|
| Accept | Contribution and evidence hold; polish only | Camera-ready + artifact; do not reopen scope |
| Accept-with-shepherding | Accepted subject to specific, bounded fixes overseen by a PC shepherd | Make exactly the requested changes; keep the shepherd informed |
| One-shot Revision | Likely-acceptable if specific changes (possibly new experiments) are made | Treat as a second deadline: do every required item, resubmit next cutoff |
| Reject | Structural: wrong metric, no real-device evidence, thin storage contribution | Reframe or reroute (OSDI/ATC/EuroSys/HotStorage/TOS); do not lightly resubmit unchanged |
The strategic reading: write the initial submission so that whatever is weakest is fixable in a one-shot revision (a measurement you can add, a baseline you can tune, a crash test you can run) rather than structural (a study design or device set you cannot redo). The process rewards papers whose gaps are experiments, not premises.
What a one-shot revision actually is
- Bounded: the decision comes with a summary of merits and a concrete list of required changes; detailed resubmission instructions follow within days.
- Can require new experiments: unlike light shepherding, the instructions may say "compare against system X" or "measure at steady state on device Y" — real work, not just editing.
- One shot: the revised paper, resubmitted at the subsequent deadline, can only be accepted or rejected — there is no second revision. The revision must fully satisfy the list.
- Exclusive: during the revision the paper is still under review at FAST and may not be submitted elsewhere unless withdrawn.
Show full SKILL.md (262 more words)Show less
How FAST differs from its siblings
- vs. OSDI/ATC/SOSP: those are general-systems venues; FAST is storage-specialized, so the review weights storage-specific evidence (device state, endurance, crash consistency) that a general-systems PC might not probe as hard. Never assume a shared calendar or template.
- vs. a journal R&R: a one-shot revision is bounded and terminal (accept/reject after one round), unlike an open-ended journal revise-and-resubmit that can iterate.
- vs. a single-deadline conference: FAST's two deadlines mean a revision or a near-miss has a scheduled next on-ramp within the same year — plan around it.
Who reads you
Expect storage-subarea reviewers. They look for the device table and firmware, ask whether SSDs were preconditioned and whether latency is reported at the tail, check that baselines are tuned, and often want the crash-consistency test for any durability claim. Vague measurement descriptions get caught, not skimmed.
Where author leverage actually exists
[Before submission] topic tags + a crisp storage framing -> reviewer pool (largest lever)
[Author response] factual corrections, pointers to existing evidence, a concrete feasible plan
[Shepherding] make exactly the bounded fixes; the shepherd is an ally, not an adversary
[One-shot revision] the strongest lever: do every required item (incl. new experiments), resubmit
[After reject] no lengthy appeal; reroute to a sibling storage/systems venue or ACM TOSA response moves borderline papers when it corrects a misreading or supplies a measurement a reviewer said was missing; it does not move papers when it argues taste. In a one-shot revision, a required item left undone is what turns the terminal second read into a rejection.
Misreadings to avoid
- Treating a one-shot revision as a guaranteed accept — it is terminal and can be rejected; budget its experiments like a deadline.
- Treating shepherding as license to expand scope — do the bounded fixes, nothing more.
- Treating the response as a debate — the PC discussion decides; your text is evidence for an advocate.
- Projecting last year's cadence — deadline count, response windows, and revision timing are decided per edition.
Output format
[Process stage] pre-submission / awaiting reviews / response / shepherding / one-shot revision / accepted
[Decision category] accept / accept-with-shepherding / one-shot revision / reject, with the driving criterion
[Criterion map] each review point -> storage-contribution | device evidence | baseline | tail latency | consistency | clarity
[Leverage plan] the next-stage action that can actually change the outcome
[Forbidden moves] identity leak / parallel submission during revision / unsupported new claims© 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 FAST-Skills/skills/fast-review-process of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Fast 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 |
|---|---|---|---|---|---|---|
| Fast Review Process this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| LLM Evaluationdavila7/claude-code-templates | 33k | 12 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Agent Evaluationsickn33/agentic-awesome-skills | 47k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Deepseek Reasonruvnet/ruflo | 74k | — | ~626 | Automated safety check: Notes | MIT | |
| EvaluatorsArize-ai/phoenix | 12k | — | ~1.7k | Automated safety check: Pass | Custom licence |
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Agent Evaluation
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Evaluate agent behavior with versioned cases and explicit verifiers.
Deepseek Reason
ruvnet/ruflo
Reasoning-mode completion against DeepSeek's deepseek-reasoner model (R1) via /v1/chat/completions.
Evaluators
Arize-ai/phoenix
Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output.
Agent Evaluation Reporting
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Questions about Fast Review Process
What does Fast Review Process do?
A skill your agent uses when reasoning about how a USENIX FAST submission is evaluated, covering double-blind program-committee review with outside referees, the author-response (rebuttal) period…. Fast Review Process is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when reasoning about how a USENIX FAST submission is evaluated, covering double-blind program-committee review with outside referees, the author-response (rebuttal) period, the Accept / Accept-with-shepherding / One-shot-Revision / Reject decision set, how a one-shot revision differs from a journal R&R and from OSDI/ATC handling, and where author leverage exists.
When should I use Fast Review Process?
Fast Review Process fits situations like: reasoning about how a USENIX FAST submission is evaluated; covering double-blind program-committee review with outside referees; the author-response (rebuttal) period; the Accept / Accept-with-shepherding / One-shot-Revision / Reject decision set.
How do I install Fast Review Process in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-review-process -a claude-code`. Or copy the skill folder (FAST-Skills/skills/fast-review-process in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fast-review-process in your project. Claude Code loads it when a task matches its description.
How do I install Fast Review Process in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fast-review-process -a codex`. Or copy the skill folder (FAST-Skills/skills/fast-review-process in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fast-review-process in your project. Codex loads it when a task matches its description.
Can I use Fast 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 fast-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/fast-review-process, .gemini/skills/fast-review-process, .github/skills/fast-review-process and .opencode/skills/fast-review-process in your project.
What does Fast Review Process need to run?
SKILL.md names no scripts, command-line tools or credentials: Fast Review Process is instructions for the agent only.
Does Fast 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 Fast 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 Fast Review Process use?
Fast 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 Fast Review Process use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Fast Review Process?
Skills that share tags, products or a category with Fast Review Process: Arize Evaluator (github/awesome-copilot, 40k stars), LLM Evaluation (davila7/claude-code-templates, 33k stars), Agent Evaluation (sickn33/agentic-awesome-skills, 47k stars) and Deepseek Reason (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Fast 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.