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 making an ISCA paper's results regenerable — pinning simulator versions and local patches, archiving per-figure configuration manifests, recording workload provenance…
Install Isca Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isca-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-reproducibility .claude/skills/isca-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 "isca-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-reproducibility into .claude/skills/isca-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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/ISCA-Skills/skills/isca-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 isca-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-reproducibility .agents/skills/isca-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 "isca-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-reproducibility into .agents/skills/isca-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 isca-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-reproducibility .cursor/skills/isca-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 "isca-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-reproducibility into .cursor/skills/isca-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 ISCA-Skills/skills/isca-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 isca-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills isca-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/ISCA-Skills/skills/isca-reproducibility .gemini/skills/isca-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 "isca-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-reproducibility into .gemini/skills/isca-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 isca-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 isca-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/ISCA-Skills/skills/isca-reproducibility .github/skills/isca-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 "isca-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-reproducibility into .github/skills/isca-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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 isca-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 isca-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/ISCA-Skills/skills/isca-reproducibility .opencode/skills/isca-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 "isca-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ISCA-Skills/skills/isca-reproducibility into .opencode/skills/isca-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "isca-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
isca-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 789 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when making an ISCA paper's results regenerable — pinning simulator versions and local patches, archiving per-figure configuration manifests, recording workload provenance…
- Works in 3 steps: At submission: container or environment… → Window-open minus one week (early… → Keep one team member's environment…
- Making an ISCA papers results regenerable — pinning simulator versions and local patches
- SKILL.md covers The result chain, and what to…, One manifest per published…, Nondeterminism gets measured,… and Paper-side reporting, plus 4 more sections
- Calls git
What it does
Isca Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making an ISCA paper's results regenerable — pinning simulator versions and local patches, archiving per-figure configuration manifests, recording workload provenance and sampling seeds, quantifying run-to-run variation on real hardware, and keeping the environment resurrectable through the February window.
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
- Making an ISCA papers results regenerable — pinning simulator versions and local patches
- Archiving per-figure configuration manifests
- Recording workload provenance and sampling seeds
- Quantifying run-to-run variation on real hardware
Example prompts
- “/isca-reproducibility”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- At submission: container or environment image built and stored; regen.sh
- Window-open minus one week (early February): resurrection drill — boot the
- Keep one team member's environment untouched between November and March; do
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
Isca Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 789 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). 789 words, ~1,741 tokens.
Download SKILL.mdSave it as
.claude/skills/isca-reproducibility/SKILL.md (or your agent's skills folder).- name
- isca-reproducibility
- description
- Use when making an ISCA paper's results regenerable — pinning simulator versions and local patches, archiving per-figure configuration manifests, recording workload provenance and sampling seeds, quantifying run-to-run variation on real hardware, and keeping the environment resurrectable through the February window.
ISCA Reproducibility
In architecture, "reproducible" means someone else — or you, three months later,
mid-rebuttal — can regenerate every reported number from recorded state. Because
most ISCA numbers come out of simulators, reproducibility here is largely
configuration archaeology: the result is a function of tool commit, local
patches, model parameters, workload build, region selection, and warm-up policy,
and losing any one of those breaks the chain. The venue reinforces this culture
with post-acceptance artifact evaluation under ACM badging
(isca-artifact-evaluation); this skill covers the discipline that must exist
before any AE form is filled.
The result chain, and what to pin at each link
| Link | What drifts silently | Pin it by |
|---|---|---|
| Simulator | Version-to-version behavior changes; forgotten local edits | Exact commit hash + git diff of local patches archived with results |
| Machine model | Config files edited during exploration | One immutable config per experiment family; configs referenced by hash |
| Workloads | Compiler/flags/inputs change binaries | Archive binaries or lockfile the build; record input sets by checksum |
| Regions & warm-up | Re-generated sampling points differ | Store the region/checkpoint files themselves, plus the generator seed |
| Post-processing | "Quick" notebook edits change aggregation | Scripted stats path from raw output to figure, in the repo |
| Real-hardware runs | Frequency scaling, thermal state, background load | Record governor, SMT/turbo state, kernel; report dispersion over trials |
One manifest per published number
Adopt the rule that every figure and table in the paper has a manifest and a
regeneration command. This is the same manifest format isca-experiments
specifies for methodology writing — one artifact serves both purposes.
results/
f07-headline/
manifest.ini # instrument, model, measurement, workloads
regen.sh # rebuild -> run -> aggregate -> plot, no hands
raw/ # simulator stats as emitted (never edited)
derived/f07.csv # scripted aggregation output
f07.pdf # exactly the file included in the paper
# The submission-freeze ritual:
git tag isca27-submitted && \
sha256sum results/*/f*.pdf paper/fig/*.pdf | sort | uniq -c -w64 | \
awk '$1!=2 {print "FIGURE MISMATCH:", $0}' # every paper figure must
# hash-match a regenerated oneThe freeze ritual catches the classic disaster: a figure in the PDF produced by a config that no longer exists because exploration continued after the plot was made.
Nondeterminism gets measured, not ignored
- Deterministic simulators: verify determinism once (same commit + config + workload → bit-identical stats) and record that check; if a threading mode breaks it, either use the deterministic mode for reported numbers or report dispersion.
- Real hardware: never a single trial. Report median and spread across ≥5 runs, with the machine-state record (governor, turbo, SMT, kernel, isolation measures). Reviewers increasingly ask; artifact evaluators always do.
- Sampled simulation: the sampling procedure and seed are part of the result. Different SimPoint runs are different experiments — archive the chosen regions, don't regenerate them.
Paper-side reporting
The paper must let a skeptical reader reconstruct the setup without the
artifact: a full configuration table (structures, sizes, latencies, DRAM
timing), the workload list with inputs and build flags summarized, the region/
warm-up policy, and a variability statement wherever hardware was measured. Under
double-blind rules the repository link, if given, must be fully anonymized
(verified 2026 rule — see isca-submission); the common pattern is an
anonymized-mirror link at submission, replaced by the real archival link in the
camera-ready.
Show full SKILL.md (343 more words)Show less
Resurrectability: the February requirement
The 2026 cycle's rebuttal/revision window (Feb 16 - Mar 6) arrived three months after submission. Teams whose environment had rotted — simulator tree no longer building, cluster images recycled, workload binaries lost — entered the window unable to run the experiments that would have saved the paper. Protocol:
- At submission: container or environment image built and stored;
regen.shfor at least the headline figure verified from the image, not from a dev machine. - Window-open minus one week (early February): resurrection drill — boot the image, regenerate one figure end to end, confirm hash match.
- Keep one team member's environment untouched between November and March; do not upgrade the shared toolchain mid-wait.
Habits that make all of this cheap
- Results directories are append-only; a changed config is a new experiment ID, never an edit in place.
- The plotting path takes experiment IDs, not file paths typed by hand.
- A
METHODS.mdin the repo grows in real time — every methodological choice (why these regions, why this warm-up, why this DRAM model) written down when made, because November-you will not remember July-you's reasoning. - Weekly:
regen.shfor the current headline figure runs green in CI or by hand. Regeneration that only works on deadline eve doesn't work.
Pre-submission reproducibility gate
- Every paper figure hash-matches a scripted regeneration
- Simulator commit + local patch diff archived alongside results
- Workload binaries/inputs archived or deterministically rebuildable
- Region/checkpoint files stored; sampling seeds recorded
- Hardware numbers carry trial counts and dispersion
- Environment image built, stored, and drill-tested
- Anonymized artifact link (if any) resolves and contains no identity
Where each practice pays off later
| Practice | Pays off at... |
|---|---|
Per-figure manifests + regen.sh | Methodology section writing, rebuttal experiments, AE claims table |
| Submission-tag freeze ritual | Camera-ready number verification, artifact snapshot selection |
| Environment image + drill | The February window's first 48 hours |
| Hardware-state records | Reviewer variance questions, Functional-badge documentation |
METHODS.md running log | Every "why did we choose X" question from reviewers and evaluators |
Venue facts (AE program, badging, double-blind link rule) verified 2026-07-08 in
../../resources/official-source-map.md; the engineering protocol above is
community best practice, applicable regardless of cycle.
© 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 ISCA-Skills/skills/isca-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Isca 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 |
|---|---|---|---|---|---|---|
| Isca 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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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
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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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Categories
Questions about Isca Reproducibility
What does Isca Reproducibility do?
A skill your agent uses when making an ISCA paper's results regenerable — pinning simulator versions and local patches, archiving per-figure configuration manifests, recording workload provenance…. Isca Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when making an ISCA paper's results regenerable — pinning simulator versions and local patches, archiving per-figure configuration manifests, recording workload provenance and sampling seeds, quantifying run-to-run variation on real hardware, and keeping the environment resurrectable through the February window.
When should I use Isca Reproducibility?
Isca Reproducibility fits situations like: making an ISCA papers results regenerable — pinning simulator versions and local patches; archiving per-figure configuration manifests; recording workload provenance and sampling seeds; quantifying run-to-run variation on real hardware.
How do I install Isca Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isca-reproducibility -a claude-code`. Or copy the skill folder (ISCA-Skills/skills/isca-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/isca-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Isca Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill isca-reproducibility -a codex`. Or copy the skill folder (ISCA-Skills/skills/isca-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/isca-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Isca 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 isca-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/isca-reproducibility, .gemini/skills/isca-reproducibility, .github/skills/isca-reproducibility and .opencode/skills/isca-reproducibility in your project.
What does Isca Reproducibility need to run?
Going by SKILL.md and its folder, Isca Reproducibility needs the command-line tools its instructions call (git).
Does Isca 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 Isca 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 Isca Reproducibility use?
Isca 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 Isca Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 7k 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 Isca Reproducibility?
Skills that share tags, products or a category with Isca 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 Isca 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.