Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when hardening a PLDI paper's measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline, variance and confidence reporting…
Install Pldi Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pldi-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pldi-reproducibility --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/PLDI-Skills/skills/pldi-reproducibility .claude/skills/pldi-reproducibility && 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 "pldi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PLDI-Skills/skills/pldi-reproducibility into .claude/skills/pldi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pldi-reproducibility", 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/PLDI-Skills/skills/pldi-reproducibilityType 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 pldi-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pldi-reproducibility --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/PLDI-Skills/skills/pldi-reproducibility .agents/skills/pldi-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pldi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PLDI-Skills/skills/pldi-reproducibility into .agents/skills/pldi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pldi-reproducibility", 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 pldi-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pldi-reproducibility --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/PLDI-Skills/skills/pldi-reproducibility .cursor/skills/pldi-reproducibility && 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 "pldi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PLDI-Skills/skills/pldi-reproducibility into .cursor/skills/pldi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pldi-reproducibility", 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 PLDI-Skills/skills/pldi-reproducibility--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 pldi-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pldi-reproducibility --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/PLDI-Skills/skills/pldi-reproducibility .gemini/skills/pldi-reproducibility && 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 "pldi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PLDI-Skills/skills/pldi-reproducibility into .gemini/skills/pldi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pldi-reproducibility", 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 pldi-reproducibilityInstalls 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 pldi-reproducibility -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/PLDI-Skills/skills/pldi-reproducibility .github/skills/pldi-reproducibility && 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 "pldi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PLDI-Skills/skills/pldi-reproducibility into .github/skills/pldi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pldi-reproducibility", 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 pldi-reproducibility -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 pldi-reproducibility --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/PLDI-Skills/skills/pldi-reproducibility .opencode/skills/pldi-reproducibility && 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 "pldi-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PLDI-Skills/skills/pldi-reproducibility into .opencode/skills/pldi-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pldi-reproducibility", 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
pldi-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1k tokens
- SKILL.md length
- 396 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening a PLDI paper's measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline, variance and confidence reporting…
- Hardening a PLDI papers measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline
- SKILL.md covers Checklist, translated to PL…, The measurement sins PLDI…, A protocol worth writing down and Compile-time and memory are…, plus 2 more sections
- Calls python3
- Variance and confidence reporting
What it does
Pldi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening a PLDI paper's measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline, variance and confidence reporting, principled benchmark choice, pinned toolchains, cross-platform validity, and a measurement log that survives artifact evaluation.
Its SKILL.md is about 1k 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 Research & Science, covering Reproducible research. 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
- Hardening a PLDI papers measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline
- Variance and confidence reporting
- Principled benchmark choice
- Pinned toolchains
Example prompts
- “/pldi-reproducibility”
Requirements
- Python 3
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
Shell commands in SKILL.md call:
python3
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
Pldi Reproducibility loads about 1k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 396 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). 396 words, ~1,048 tokens.
Download SKILL.mdSave it as
.claude/skills/pldi-reproducibility/SKILL.md (or your agent's skills folder).- name
- pldi-reproducibility
- description
- Use when hardening a PLDI paper's measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline, variance and confidence reporting, principled benchmark choice, pinned toolchains, cross-platform validity, and a measurement log that survives artifact evaluation.
PLDI Reproducibility
PLDI's methodological yardstick is written down: the SIGPLAN Empirical Evaluation
Guidelines and their one-page checklist (Blackburn, Hauswirth, Berger, Hicks,
Krishnamurthi, 2018; sigplan.org/Resources/EmpiricalEvaluation/, read 2026-07-08).
Reviewers and artifact evaluators both reach for it. This skill turns the
checklist into compiler-bench practice; pldi-experiments covers what to measure,
this covers whether anyone can trust and repeat the measurement.
Checklist, translated to PL systems
| Guideline item | What it means for a compiler/runtime paper |
|---|---|
| Clearly stated claims | "1.17x geomean on suite S vs baseline B at -O2" — never "significant speedups" |
| Suitable comparison | The strongest sensible baseline configuration, tuned as its authors intend |
| Principled benchmark choice | The suite is justified; exclusions are listed with reasons, not silently dropped |
| Adequate data analysis | Repetitions, variance, and an aggregation rule (geomean for ratios) stated in the paper |
The measurement sins PLDI reviewers hunt
- No warmup discipline. JIT-compiled and cache-sensitive workloads need documented warmup iterations before timed runs; AOT binaries still need file-cache and frequency-scaling control. Say which regime you measured — steady-state and cold-start are different claims.
- Single-run numbers. Report repetitions (dozens, not three), dispersion (confidence intervals or at least min/max), and never present a 2% delta inside the noise band as an improvement.
- One machine, universal claim. A locality optimization can invert between microarchitectures. Two platforms with differing cache hierarchies is the floor for a general performance claim; otherwise scope the claim to the measured machine.
- Unpinned toolchains. "GCC" is not a baseline; "GCC 14.2, -O2, glibc 2.39, Ubuntu 24.04, governor=performance" is.
- Benchmark survivorship. Excluding the programs your technique fails on, without saying so, is the most damaging silent choice in a PL evaluation.
Show full SKILL.md (129 more words)Show less
A protocol worth writing down
Keep the protocol in the repository, executed by machine, so paper and artifact cannot diverge:
# protocol.sh — executed, not described
set -euo pipefail
lscpu > results/env/cpu.txt; uname -a > results/env/os.txt
cc --version > results/env/toolchain.txt
for b in $(cat benchmarks/suite.list); do
for i in $(seq 1 5); do ./run.sh "$b" >/dev/null; done # warmup
for i in $(seq 1 30); do ./run.sh "$b" >> "results/raw/$b.csv"; done
done
python3 scripts/aggregate.py --stat geomean --ci 95 results/raw/Log the environment beside the numbers: CPU model, frequency-scaling governor, ASLR setting, load conditions. When a reviewer's rerun differs from yours, the environment log is what turns a dispute into a diagnosis.
Compile-time and memory are claims too
If the paper claims low compile-time overhead or memory neutrality, those numbers need the same repetitions-and-variance treatment as speedups. A "under 3% overhead" sentence backed by one timed build is the soft spot response-phase reviewers press hardest.
Tie-in to badges
Everything above lands in the artifact (pldi-artifact-evaluation): the executed
protocol becomes reproduce_all.sh, the environment log becomes results/env/,
and the suite-choice justification becomes benchmarks/README. Reproducibility
retrofitted after acceptance always shows.
Output format
[Guidelines pass] claims / comparison / benchmark choice / analysis — each ok?
[Warmup regime] documented? steady-state vs cold-start stated?
[Variance] runs per data point, CI method, noise floor vs claimed delta
[Platforms] n machines; claim scoped accordingly?
[Pinning + log] toolchain versions, flags, environment captured in repo?© 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 PLDI-Skills/skills/pldi-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Pldi Reproducibility 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 |
|---|---|---|---|---|---|---|
| Pldi Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Related
Categories
Questions about Pldi Reproducibility
What does Pldi Reproducibility do?
A skill your agent uses when hardening a PLDI paper's measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline, variance and confidence reporting…. Pldi Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening a PLDI paper's measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline, variance and confidence reporting, principled benchmark choice, pinned toolchains, cross-platform validity, and a measurement log that survives artifact evaluation.
When should I use Pldi Reproducibility?
Pldi Reproducibility fits situations like: hardening a PLDI papers measurements against the SIGPLAN Empirical Evaluation Guidelines — warmup and steady-state discipline; variance and confidence reporting; principled benchmark choice; pinned toolchains.
How do I install Pldi Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pldi-reproducibility -a claude-code`. Or copy the skill folder (PLDI-Skills/skills/pldi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/pldi-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Pldi Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pldi-reproducibility -a codex`. Or copy the skill folder (PLDI-Skills/skills/pldi-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/pldi-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Pldi Reproducibility 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 pldi-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pldi-reproducibility, .gemini/skills/pldi-reproducibility, .github/skills/pldi-reproducibility and .opencode/skills/pldi-reproducibility in your project.
What does Pldi Reproducibility need to run?
Going by SKILL.md and its folder, Pldi Reproducibility needs the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Pldi Reproducibility 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 Pldi Reproducibility 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 Pldi Reproducibility use?
Pldi Reproducibility 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 Pldi Reproducibility use?
About 1k tokens (SKILL.md is roughly 4.2k 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 Pldi Reproducibility?
Skills that share tags, products or a category with Pldi Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pldi Reproducibility?
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.