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 MICRO paper's results for re-derivation — pinning simulator commits and configs, recording workload trace provenance and SimPoint recipes, versioning…
Install Micro Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill micro-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-reproducibility .claude/skills/micro-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 "micro-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-reproducibility into .claude/skills/micro-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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/MICRO-Skills/skills/micro-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 micro-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-reproducibility .agents/skills/micro-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 "micro-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-reproducibility into .agents/skills/micro-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 micro-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-reproducibility .cursor/skills/micro-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 "micro-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-reproducibility into .cursor/skills/micro-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 MICRO-Skills/skills/micro-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 micro-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-reproducibility .gemini/skills/micro-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 "micro-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-reproducibility into .gemini/skills/micro-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 micro-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 micro-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/MICRO-Skills/skills/micro-reproducibility .github/skills/micro-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 "micro-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-reproducibility into .github/skills/micro-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 micro-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 micro-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/MICRO-Skills/skills/micro-reproducibility .opencode/skills/micro-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 "micro-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-reproducibility into .opencode/skills/micro-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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
micro-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 717 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 MICRO paper's results for re-derivation — pinning simulator commits and configs, recording workload trace provenance and SimPoint recipes, versioning…
- Hardening a MICRO papers results for re-derivation — pinning simulator commits and configs
- SKILL.md covers What must be pinned, by…, The run manifest, Disclosure lines the paper… and Determinism and drift checks, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Recording workload trace provenance and SimPoint recipes
What it does
Micro Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening a MICRO paper's results for re-derivation — pinning simulator commits and configs, recording workload trace provenance and SimPoint recipes, versioning power/area models, capturing RTL toolchain state, and writing the run manifests that later survive MICRO's post-acceptance artifact evaluation.
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 a MICRO papers results for re-derivation — pinning simulator commits and configs
- Recording workload trace provenance and SimPoint recipes
- Versioning power/area models
- Capturing RTL toolchain state
Example prompts
- “/micro-reproducibility”
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 (its code samples are yaml).
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
Micro Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 717 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). 717 words, ~1,661 tokens.
Download SKILL.mdSave it as
.claude/skills/micro-reproducibility/SKILL.md (or your agent's skills folder).- name
- micro-reproducibility
- description
- Use when hardening a MICRO paper's results for re-derivation — pinning simulator commits and configs, recording workload trace provenance and SimPoint recipes, versioning power/area models, capturing RTL toolchain state, and writing the run manifests that later survive MICRO's post-acceptance artifact evaluation.
MICRO Reproducibility
In microarchitecture research the "experiment" is usually a software artifact pretending to be hardware. That makes results perfectly reproducible in principle — and silently unreproducible in practice, because a simulator commit, a config diff, or a trace-generation flag changed between the headline run and the camera-ready. This skill installs the record-keeping that prevents that.
What must be pinned, by evidence type
| Evidence | Pin these or the number is unrecoverable |
|---|---|
| Cycle-level simulation | Simulator name + exact commit hash; full config files (not "8-wide OoO" prose); patch set applied; host compiler for the simulator build |
| Workload traces | Source binary + compiler flags; trace tool + version; SimPoint interval size, K, and seed; warmup instructions per region |
| Power / area estimates | McPAT/CACTI/Accelergy version; technology node; the XML/cfg input actually fed in; any manual scaling factors |
| RTL results | HDL commit; synthesis tool + version; target library/PDK; constraints file; which corners were run |
| FPGA / silicon | Board/chip stepping; bitstream hash or firmware; frequency; measurement method for power (rail vs counter model) |
| DRAM behavior | Ramulator/DRAMsim3 version + timing config (device datasheet parameters) |
The rule of thumb: a result is reproducible when a stranger can regenerate the exact bar in Fig. 3 from the manifest alone, without asking you anything.
The run manifest
Emit one manifest per experiment batch, generated by the launch script — never written by hand after the fact:
# results/2026-04-02_headline/manifest.yaml
simulator: {name: champsim, commit: 3fa9c21, patches: [prefetch-fix.diff]}
config: {file: configs/8w_ooo_baseline.json, sha256: 9be1...}
mechanism: {file: configs/ours_32kb.json, knobs: {table_entries: 2048, tag_bits: 12}}
workloads: {suite: spec2017_rate1, traces: traces/v3/, simpoints: k30_int200M_seed7,
warmup_M: 200, detailed_M: 1000}
power: {tool: mcpat, version: "1.3", node_nm: 22, input: mcpat/ours.xml}
host: {machine: lab-node-14, compiler: gcc-13.2}
outputs: {raw: stats/, figures: fig3_headline.pdf, script: plot_headline.py}Commit the manifest with the raw stats files. Figures are regenerated from stats by a script named in the manifest — a figure that cannot be regenerated does not go in the paper.
Disclosure lines the paper itself owes
The methodology section (see micro-writing-style, ~1 page) must state, in text:
- Simulator and version, with your modifications summarized honestly ("we added a cycle-accurate model of X; the interconnect remains the default model").
- The full baseline configuration in a table (pipeline width, ROB, caches with latencies, predictor, prefetchers, DRAM timing).
- Sampling recipe: how regions were chosen, warmup, and measured instruction counts.
- Power/area model, node, and the sentence "these are model estimates" — the community's tools (CACTI, McPAT — both MICRO papers themselves) are respected but known to be approximations, and pretending otherwise reads as naivety.
Determinism and drift checks
- Two identical launches must produce bit-identical stats; if the simulator has any nondeterminism (thread interleaving in parallel sims), pin it or report run count and spread.
- Re-run the baseline after every simulator patch — a patch that changes baseline IPC by 0.5% invalidates every relative number already collected.
- Keep a
results-ledger.md: date, manifest path, headline geomean, what changed. Rebuttal season (June) will ask "what config produced Fig. 7?" — from three months earlier; the ledger answers in minutes, memory does not.
Show full SKILL.md (286 more words)Show less
Validating the instrument itself
Reproducibility of a wrong number is worthless; record the evidence that the simulator models what the paper says it models:
- Cross-check the baseline against published behavior (see
micro-experiments): store the comparison table — suite, metric, published range, your value — in the repo, not just in someone's memory. - Unit-test the mechanism model. Hand-construct access sequences with known correct outcomes (a burst that must train the table, a pattern that must not) and assert the simulator's counters match. These micro-tests are the first thing an AE evaluator can run and the best evidence the model is not a stats bug.
- Record known fidelity gaps in a
LIMITS.md: default interconnect model, simplified TLB, unmodeled prefetcher interactions. The paper's claims must stay inside these limits; writing them down keeps deadline-week edits honest.
Anonymity interaction (submission phase)
MICRO 2026 requires artifact links to be fully anonymized or removed. Before the
April upload, sweep the repo you intend to link: git author fields, hostnames in
manifests (lab-node-14 is fine; patt-lab-utexas-14 is not), cluster paths,
funding strings in READMEs. The manifest discipline above makes this sweep
mechanical rather than archaeological.
Storage economics of keeping everything
Raw cycle-level stats for a full paper (dozens of configs × dozens of workloads × sweeps) usually fit in single-digit gigabytes — trivially archivable, and the cheapest insurance in research. Policy: keep all raw stats files, forever, compressed; regenerate figures on demand. Delete only simulator checkpoints and intermediate trace files that the recipes can rebuild. When a reviewer asks in June about a bar from a January run, or AE asks in August, or a follow-up paper asks in two years, the manifest + stats pair answers; the alternative is re-simulating weeks of compute under deadline pressure.
Output format
[Pinning audit] per evidence type: pinned / partially pinned / loose (list)
[Manifest coverage] experiments with manifests: N/M
[Figure regen] all paper figures scripted from raw stats: yes / no (list unscripted)
[Methodology text] simulator+mods / baseline table / sampling / model-estimate
disclosure: present-absent each
[Determinism] identical-rerun check passed: yes / no / untested
[Ledger] results-ledger current through: <date>
[Anonymity sweep] repo clean for anonymized linking: yes / issues listed© 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 MICRO-Skills/skills/micro-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Micro 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 |
|---|---|---|---|---|---|---|
| Micro 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 |
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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Categories
Questions about Micro Reproducibility
What does Micro Reproducibility do?
A skill your agent uses when hardening a MICRO paper's results for re-derivation — pinning simulator commits and configs, recording workload trace provenance and SimPoint recipes, versioning…. Micro Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening a MICRO paper's results for re-derivation — pinning simulator commits and configs, recording workload trace provenance and SimPoint recipes, versioning power/area models, capturing RTL toolchain state, and writing the run manifests that later survive MICRO's post-acceptance artifact evaluation.
When should I use Micro Reproducibility?
Micro Reproducibility fits situations like: hardening a MICRO papers results for re-derivation — pinning simulator commits and configs; recording workload trace provenance and SimPoint recipes; versioning power/area models; capturing RTL toolchain state.
How do I install Micro Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill micro-reproducibility -a claude-code`. Or copy the skill folder (MICRO-Skills/skills/micro-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/micro-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Micro Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill micro-reproducibility -a codex`. Or copy the skill folder (MICRO-Skills/skills/micro-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/micro-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Micro 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 micro-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/micro-reproducibility, .gemini/skills/micro-reproducibility, .github/skills/micro-reproducibility and .opencode/skills/micro-reproducibility in your project.
What does Micro Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Micro Reproducibility is instructions for the agent only.
Does Micro 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 Micro 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 Micro Reproducibility use?
Micro 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 Micro Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 Micro Reproducibility?
Skills that share tags, products or a category with Micro 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 Micro Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.