Literature Review
K-Dense-AI/scientific-agent-skills
Runs systematic, scoping or narrative literature reviews across PubMed, arXiv, bioRxiv and Semantic Scholar, with citation checks and Markdown or PDF output.
A skill your agent uses when strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map, self-contained proofs in the at-submission appendix (no external appendices)…
Install Pods Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pods-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/PODS-Skills/skills/pods-reproducibility .claude/skills/pods-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 "pods-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PODS-Skills/skills/pods-reproducibility into .claude/skills/pods-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pods-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/PODS-Skills/skills/pods-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 pods-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pods-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/PODS-Skills/skills/pods-reproducibility .agents/skills/pods-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 "pods-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PODS-Skills/skills/pods-reproducibility into .agents/skills/pods-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pods-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 pods-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pods-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/PODS-Skills/skills/pods-reproducibility .cursor/skills/pods-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 "pods-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PODS-Skills/skills/pods-reproducibility into .cursor/skills/pods-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pods-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 PODS-Skills/skills/pods-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 pods-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills pods-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/PODS-Skills/skills/pods-reproducibility .gemini/skills/pods-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 "pods-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PODS-Skills/skills/pods-reproducibility into .gemini/skills/pods-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pods-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 pods-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 pods-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/PODS-Skills/skills/pods-reproducibility .github/skills/pods-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 "pods-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PODS-Skills/skills/pods-reproducibility into .github/skills/pods-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pods-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 pods-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 pods-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/PODS-Skills/skills/pods-reproducibility .opencode/skills/pods-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 "pods-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PODS-Skills/skills/pods-reproducibility into .opencode/skills/pods-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pods-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
pods-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 656 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map, self-contained proofs in the at-submission appendix (no external appendices)…
- Strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map
- SKILL.md covers Claim-to-proof map, Verifiability audit, Assumptions and scope pinning and Degrees of verifiability…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Self-contained proofs in the at-submission appendix (no external appendices)
What it does
Pods Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map, self-contained proofs in the at-submission appendix (no external appendices), correctly stated assumptions, honest scope and open cases, the full-version-on-arXiv norm, and consistency between what the paper claims and what the proofs actually establish.
Its SKILL.md is about 1.4k 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 and Academic paper search. It works with arXiv. 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
- Strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map
- Self-contained proofs in the at-submission appendix (no external appendices)
- Correctly stated assumptions
- Honest scope and open cases
Example prompts
- “/pods-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.
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
Pods Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 656 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). 656 words, ~1,442 tokens.
Download SKILL.mdSave it as
.claude/skills/pods-reproducibility/SKILL.md (or your agent's skills folder).- name
- pods-reproducibility
- description
- Use when strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map, self-contained proofs in the at-submission appendix (no external appendices), correctly stated assumptions, honest scope and open cases, the full-version-on-arXiv norm, and consistency between what the paper claims and what the proofs actually establish.
PODS Reproducibility
Use this before submission and again before camera-ready. For a theory venue, "reproducibility" means verifiability: a competent reader can follow every proof and confirm every stated theorem. PODS makes this concrete by requiring the proof appendix to be incorporated at submission — there are no online/external appendices — so the reviewers can check the mathematics now, not on trust.
Claim-to-proof map
- Map each theorem, lemma, corollary, and reported bound to a complete proof location — the body or the appendix. No stated result may lack a full proof somewhere in the submitted PDF.
- For each proof, state its assumptions, cite the exact prior lemma or theorem it uses, and do not defer a key step to "the full version" or "a routine argument" when the argument is not routine.
- For constructions and algorithms, give enough that a reader could re-derive them: the invariant, the data structure, the parameter settings, and the complexity accounting.
- Keep the assumptions ledger honest: every conditional result (ETH, OMv,
#P-hardness) is labeled where it is used, and the model (data vs. combined complexity, cost model) is fixed. - Keep the paper and its proofs consistent: an abstract that claims more than the theorems prove, or a corollary that does not follow from the stated theorem, is the contradiction reviewers read as carelessness.
Verifiability audit
| Claim in the paper | Weak (reject-prone) form | PODS-ready form |
|---|---|---|
| "Theorem: the problem is hard" | Hardness "sketched"; no reduction given | A complete reduction in the appendix, with the source problem cited |
| "Our algorithm is optimal" | Upper bound only | Upper bound + matching lower bound, both proved |
| "This holds for all such queries" | Proof for the examples shown | A general proof covering every case, or a scoped, honest claim |
| "It is easy to see that..." | The hard step waved away | The argument written out, or a precise appendix pointer |
| "Full proofs in the full version" | Nothing in the appendix | Complete proofs in the at-submission appendix; arXiv full version optional-but-consistent |
"Proof omitted" with no appendix proof is treated at PODS the way "available on request" is treated at a systems venue — as not provided. The appendix exists so nothing decision-critical is off-paper.
Assumptions and scope pinning
[Model] fix data model, query class, and complexity/cost measure; use them consistently
[Assumptions] label every conditional bound with its conjecture at the point of use
[Scope] state exactly which cases the result covers and which remain open — do not overclaim
[Dependencies] each proof cites the exact prior result it invokes; no circular lemma chains
[Constants] expose hidden dependence on query size / schema / fixed parametersShow full SKILL.md (298 more words)Show less
Degrees of verifiability (state the one you achieved)
- Complete: every theorem has a full, self-contained proof in the submitted PDF (body + appendix). This is the PODS default and expectation.
- Scoped: the main theorems are fully proved; a clearly labeled extension or a corollary is stated with a proof idea and deferred to the journal full version — acceptable only if nothing decision-critical is deferred.
- Conjectural: a statement is offered as a conjecture with partial evidence — allowed as such, never dressed as a theorem.
The full-version-on-arXiv norm
PODS is an extended-abstract venue: the community norm is to post a full version on arXiv (with all proofs and any extended development) and, at camera-ready, DOI-link it to the PACMMOD paper. At submission the arXiv version must not break double-anonymity; keep it anonymized or withhold the link until acceptance. The at-submission appendix, not the arXiv link, is what the reviewers must be able to verify.
Vignette: a dichotomy paper
Consider a paper proving a PTIME/hard dichotomy for a query class. Its verifiability spine: the exact model and query class in preliminaries; the tractable direction as a proved algorithm with complexity; the hard direction as a complete reduction from a cited problem, under a named assumption if conditional; a lemma establishing that the two directions partition the class (completeness); and a scope sentence naming the one extension (e.g. self-joins) left open — every proof in the body or the at-submission appendix, with a consistent arXiv full version prepared for later.
Consistency and camera-ready pass
- Before submission: every stated result traces to a complete proof in the PDF; assumptions are labeled; the appendix is incorporated and anonymized.
- Before camera-ready: post the (de-anonymized) full version on arXiv and add the DOI link; ensure the arXiv version's theorems and the PACMMOD version's theorems agree exactly.
Output format
[Claim inventory] <theorem/lemma -> complete proof location (body/appendix)>
[Verifiability] complete / scoped / conjectural, stated honestly
[Assumption gaps] <any conditional bound unlabeled? any circular dependency?>
[Scope honesty] <open cases stated? overclaim in the abstract? yes/no>
[Paper fixes] <proofs to complete or pointers to add before submission>
[Full-version plan] <arXiv version prepared, anonymized now, DOI-linked at camera-ready>© 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 PODS-Skills/skills/pods-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Pods 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 |
|---|---|---|---|---|---|---|
| Pods Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Literature ReviewK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Notes | MIT | |
| Literature Search Methodologyaiming-lab/AutoResearchClaw | 15k | — | ~709 | Automated safety check: Pass | MIT | |
| Paper LensYSQ-boop/paper-lens | 101 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Read arXiv Paperkarpathy/nanochat | 59k | 1 repos | ~494 | Automated safety check: Pass | MIT | |
| Literature Reviewneflibata-feng/MyArxiv-Agent | 126 | 20 repos | ~5.9k | Automated safety check: Notes | MIT |
Similar skills
Literature Search Methodology
aiming-lab/AutoResearchClaw
Lays out a systematic literature review method: PICO-based search strategy, inclusion criteria, PRISMA screening, quality assessment tools and synthesis approaches.
Paper Lens
YSQ-boop/paper-lens
Read and critically analyze one academic paper from an arXiv URL/ID or a local PDF, producing a source-grounded Markdown report that can grow from a quick read into a reviewer-level deep review.
Read arXiv Paper
karpathy/nanochat
Fetches the TeX source of an arXiv paper from its URL, reads it and writes a markdown summary tied to the nanochat project.
Literature Review
neflibata-feng/MyArxiv-Agent
Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.).
Openalex Database
neflibata-feng/MyArxiv-Agent
Query and analyze scholarly literature using the OpenAlex database.
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Related
Works with
Categories
Questions about Pods Reproducibility
What does Pods Reproducibility do?
A skill your agent uses when strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map, self-contained proofs in the at-submission appendix (no external appendices)…. Pods Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map, self-contained proofs in the at-submission appendix (no external appendices), correctly stated assumptions, honest scope and open cases, the full-version-on-arXiv norm, and consistency between what the paper claims and what the proofs actually establish.
When should I use Pods Reproducibility?
Pods Reproducibility fits situations like: strengthening the verifiability of an ACM PODS paper — a complete claim-to-proof map; self-contained proofs in the at-submission appendix (no external appendices); correctly stated assumptions; honest scope and open cases.
How do I install Pods Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-reproducibility -a claude-code`. Or copy the skill folder (PODS-Skills/skills/pods-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/pods-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Pods Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-reproducibility -a codex`. Or copy the skill folder (PODS-Skills/skills/pods-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/pods-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Pods 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 pods-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/pods-reproducibility, .gemini/skills/pods-reproducibility, .github/skills/pods-reproducibility and .opencode/skills/pods-reproducibility in your project.
What does Pods Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Pods Reproducibility is instructions for the agent only.
Does Pods 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 Pods 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 Pods Reproducibility use?
Pods 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 Pods Reproducibility use?
About 1.4k tokens (SKILL.md is roughly 5.8k 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 Pods Reproducibility?
Skills that share tags, products or a category with Pods Reproducibility: Literature Review (K-Dense-AI/scientific-agent-skills, 48k stars), Literature Search Methodology (aiming-lab/AutoResearchClaw, 15k stars), Paper Lens (YSQ-boop/paper-lens, 101 stars) and Read arXiv Paper (karpathy/nanochat, 59k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pods 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.