Agent skill

pods-reproducibility

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.

brycew6m4,252★ · +31/wk · 3 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-reproducibility --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: PODS-Skills/skills/pods-reproducibility/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 984 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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

What's inside
Steps it walks through
  1. Claim-to-proof map
  2. Verifiability audit
  3. Assumptions and scope pinning
  4. Degrees of verifiability (state the one you achieved)
  5. The full-version-on-arXiv norm
  6. Vignette: a dichotomy paper
  7. Consistency and camera-ready pass
  8. Output format
More from Awesome-Journal-Skills
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About this skill
What does the pods-reproducibility skill do?

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.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 984 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going