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 an ASPLOS paper's results for independent repetition — pinning simulator versions and configs, recording kernel/firmware/BIOS state, packaging FPGA bitstreams…
Install Asplos Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill asplos-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-reproducibility .claude/skills/asplos-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 "asplos-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-reproducibility into .claude/skills/asplos-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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/ASPLOS-Skills/skills/asplos-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 asplos-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-reproducibility .agents/skills/asplos-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 "asplos-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-reproducibility into .agents/skills/asplos-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 asplos-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-reproducibility .cursor/skills/asplos-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 "asplos-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-reproducibility into .cursor/skills/asplos-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 ASPLOS-Skills/skills/asplos-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 asplos-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills asplos-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/ASPLOS-Skills/skills/asplos-reproducibility .gemini/skills/asplos-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 "asplos-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-reproducibility into .gemini/skills/asplos-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 asplos-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 asplos-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/ASPLOS-Skills/skills/asplos-reproducibility .github/skills/asplos-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 "asplos-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-reproducibility into .github/skills/asplos-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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 asplos-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 asplos-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/ASPLOS-Skills/skills/asplos-reproducibility .opencode/skills/asplos-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 "asplos-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ASPLOS-Skills/skills/asplos-reproducibility into .opencode/skills/asplos-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "asplos-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
asplos-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 753 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening an ASPLOS paper's results for independent repetition — pinning simulator versions and configs, recording kernel/firmware/BIOS state, packaging FPGA bitstreams…
- Works in 3 steps: Repeatable anywhere: simulator… → Repeatable with named hardware: the… → Not independently repeatable: results on…
- Hardening an ASPLOS papers results for independent repetition — pinning simulator versions and configs
- SKILL.md covers The state ledger, Scripted capture beats…, The hardware-access problem,… and Claim-preservation, not…, plus 5 more sections
- Calls git
What it does
Asplos Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening an ASPLOS paper's results for independent repetition — pinning simulator versions and configs, recording kernel/firmware/BIOS state, packaging FPGA bitstreams and RTL, documenting hardware dependencies an evaluator may lack, and writing availability statements that match what the ACM badges will later require.
Its SKILL.md is about 1.7k 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 an ASPLOS papers results for independent repetition — pinning simulator versions and configs
- Recording kernel/firmware/BIOS state
- Packaging FPGA bitstreams and RTL
- Documenting hardware dependencies an evaluator may lack
Example prompts
- “/asplos-reproducibility”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- Repeatable anywhere: simulator experiments and analysis scripts — full
- Repeatable with named hardware: the exact platform requirements (board,
- Not independently repeatable: results on lab-only or pre-production
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:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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
Asplos Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 753 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). 753 words, ~1,725 tokens.
Download SKILL.mdSave it as
.claude/skills/asplos-reproducibility/SKILL.md (or your agent's skills folder).- name
- asplos-reproducibility
- description
- Use when hardening an ASPLOS paper's results for independent repetition — pinning simulator versions and configs, recording kernel/firmware/BIOS state, packaging FPGA bitstreams and RTL, documenting hardware dependencies an evaluator may lack, and writing availability statements that match what the ACM badges will later require.
ASPLOS Reproducibility
Systems results decay fast: a kernel update, a microcode revision, or a silently
changed simulator default can move numbers by more than the paper's claimed margin.
Reproducibility work at ASPLOS is therefore state capture — recording the full
machine, model, and toolchain state behind every figure — done while the experiments
run, not reconstructed at camera-ready time. It also front-loads artifact
evaluation: the badge criteria (asplos-artifact-evaluation) are exactly a demand
that this state capture exists and works.
The state ledger
Maintain one ledger row per experimental platform, committed alongside results:
| Layer | Capture | Why it moves numbers |
|---|---|---|
| Silicon | CPU model + stepping, memory config/topology, device (e.g. CXL expander) firmware | Steppings differ in errata and prefetch behavior |
| Firmware/BIOS | Microcode revision; SMT, turbo, prefetcher, C-state, NUMA settings | Any one knob can swamp a 10% effect |
| OS | Kernel version + full config, relevant sysctls, mitigations state | Speculation mitigations alone shift syscall-heavy results |
| Toolchain | Compiler + flags, libraries, runtime versions | -O level and allocator choice are classic silent variables |
| Simulator | Exact commit, all config files, region/checkpoint method, warm-up length | Defaults change across releases without notice |
| FPGA | Board, toolchain version, constraints, bitstream hash, achieved clock | Re-synthesis at a different clock is a different experiment |
| Workloads | Suite versions, input sets, trace provenance and preprocessing | "SPEC" without input class is unrepeatable |
| Randomness | Seeds for any stochastic component + run counts | Needed for the dispersion numbers to mean anything |
Scripted capture beats remembered capture
Run at the start of every measurement session; store output next to the data:
#!/bin/sh
# state-capture.sh — commit this file and its output with each result set
uname -a; cat /proc/cmdline
grep -m1 'model name' /proc/cpuinfo; grep microcode /proc/cpuinfo | sort -u
cat /sys/devices/system/cpu/vulnerabilities/* 2>/dev/null | sort -u
cat /sys/devices/system/cpu/smt/control 2>/dev/null
numactl --hardware 2>/dev/null | head -5
cc --version | head -1
git -C "$SIM_DIR" rev-parse HEAD 2>/dev/null # simulator commit
sha256sum "$BITSTREAM" 2>/dev/null # FPGA bitstream identityThe hardware-access problem, named honestly
ASPLOS artifacts often need hardware an independent evaluator will not have. The honest pattern is a three-tier availability statement drafted at submission time:
- Repeatable anywhere: simulator experiments and analysis scripts — full configs and one command per figure.
- Repeatable with named hardware: the exact platform requirements (board, expander, CPU family), plus what to expect if the evaluator's part differs.
- Not independently repeatable: results on lab-only or pre-production hardware — say so, and provide either supervised access, raw logs with the analysis pipeline, or a scaled-down proxy. Silence here reads as concealment; a stated limitation reads as engineering.
Claim-preservation, not number-worship
State which conclusions should survive environmental drift and which are environment-specific: "the ordering of policies is stable across kernels 6.6-6.9; absolute runtimes are not." This single sentence pattern prevents the most common failed-reproduction dispute — an evaluator matching your ordering but not your absolute numbers and calling it a failure.
Timing across the ASPLOS cycle
- Before September 9: ledger current; capture script in the repo; availability tiers drafted (they inform the paper's own text).
- Response window: the ledger is your defense when a reviewer doubts a number — you can state the exact conditions instead of hand-waving.
- Major Revision: re-run under the captured original state where possible; where the environment has drifted, disclose the drift in the change note.
- After acceptance: the ledger becomes the Artifact Appendix's dependency section nearly verbatim; AE calendars for 2027 were 待核实 at pack-check time, so confirm dates when notified.
Show full SKILL.md (259 more words)Show less
One command per figure
The internal gold standard that makes everything downstream cheap: every figure and table in the paper regenerates from a single committed command that reads raw results and emits the exact plot. It catches stale-figure bugs before submission, turns response-window questions into lookups, and becomes the Reproducible-badge run script with a rename. Institute it at the first result, when it costs minutes — retrofitting it at camera-ready costs days.
Trace and dataset provenance
Workload inputs decay independently of code. For each trace or dataset, record origin (public suite version, generated-by script + seed, or production source), preprocessing steps as scripts rather than prose, and a checksum of the exact bytes used. Production traces that cannot be released need a characterization (rate, skew, working-set curves) plus a matched synthetic generator committed to the repo — this is also the anonymity-safe form for submission, since a raw trace can identify its owner.
When numbers drift between submission and revision
The Major Revision window arrives months after the original runs, and environments drift. Protocol:
- Re-run a sentinel subset (three representative experiments) under the captured original state before starting revision work; if the sentinels reproduce, extend confidently.
- If they do not, bisect the ledger — kernel, microcode, simulator commit — until the moved variable is found; the ledger exists for exactly this moment.
- Disclose in the change note which results were re-collected and under what changed conditions, and re-state the claim-preservation sentence for the new environment. Silent regeneration of all numbers invites a reviewer to ask which version was real.
Output format
[Ledger coverage] platforms with complete rows: N/N · gaps listed
[Capture automation] script committed + outputs stored with data: Y/N
[Simulator pinning] commit + configs + region method + warm-up recorded: Y/N
[Availability tiers] anywhere / named-hardware / not-repeatable — each populated
[Claim preservation] drift-stable vs environment-specific conclusions stated: Y/N
[Badge readiness] which badges the current package could already earn© 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 ASPLOS-Skills/skills/asplos-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Asplos 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 |
|---|---|---|---|---|---|---|
| Asplos Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | 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 |
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xjtulyc/MedgeClaw
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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
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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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Questions about Asplos Reproducibility
What does Asplos Reproducibility do?
A skill your agent uses when hardening an ASPLOS paper's results for independent repetition — pinning simulator versions and configs, recording kernel/firmware/BIOS state, packaging FPGA bitstreams…. Asplos Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening an ASPLOS paper's results for independent repetition — pinning simulator versions and configs, recording kernel/firmware/BIOS state, packaging FPGA bitstreams and RTL, documenting hardware dependencies an evaluator may lack, and writing availability statements that match what the ACM badges will later require.
When should I use Asplos Reproducibility?
Asplos Reproducibility fits situations like: hardening an ASPLOS papers results for independent repetition — pinning simulator versions and configs; recording kernel/firmware/BIOS state; packaging FPGA bitstreams and RTL; documenting hardware dependencies an evaluator may lack.
How do I install Asplos Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill asplos-reproducibility -a claude-code`. Or copy the skill folder (ASPLOS-Skills/skills/asplos-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/asplos-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Asplos Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill asplos-reproducibility -a codex`. Or copy the skill folder (ASPLOS-Skills/skills/asplos-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/asplos-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Asplos 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 asplos-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/asplos-reproducibility, .gemini/skills/asplos-reproducibility, .github/skills/asplos-reproducibility and .opencode/skills/asplos-reproducibility in your project.
What does Asplos Reproducibility need to run?
Going by SKILL.md and its folder, Asplos Reproducibility needs the command-line tools its instructions call (git).
Does Asplos Reproducibility access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Asplos 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 Asplos Reproducibility use?
Asplos 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 Asplos Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.9k 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 Asplos Reproducibility?
Skills that share tags, products or a category with Asplos 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 Asplos 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.