pods-experiments
Use when designing or auditing the analytical "evidence" of an ACM PODS paper — worst-case and average-case analyses, matching upper and lower bounds, dichotomy completeness, correct complexity assumptions, and the occasional empirical validation when a theory paper claims practicality — matching the rigor to the shape of each theoretical claim.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PODS Experiments Use this before submission when the technical story is not yet locked. For PODS the "experiments" are mostly **analyses and proofs**, because the reviewers are theoreticians and the evidence is a theorem, not a benchmark. The organizing principle is **rigor proportional to the claim** — the analysis must establish exactly what the paper asserts, in the model it names, with no hidden gap. ## Analysis audit - **Match the analysis to the claim shape.** A claim of optimality needs a *matching lower bound*; a claim of a classification needs *completeness* (every case decided); a claim of efficiency needs an exact complexity in a stated measure (data vs. combined), not an asymptotic hand-wave. - **Close the upper/lower-bound gap, or state it.** An upper bound without a lower bound is not optimality; if the gap is open, say so precisely rather than implying tightness. - **State every complexity assumption.** Conditional hardness (ETH, SETH, OMv, the exponential-time hypothesis, `P ≠ NP`, `#P`-hardness) must be named where the bound is stated; never dress a conditional lower bound as unconditional. - **Check the model does not smuggle the result.** A cost model, a data m
- Analysis audit
- Claim-to-rigor design table
- Assumption and model discipline
- Optional empirical validation (when a paper claims practicality)
- Vignette: proving an algorithm optimal
- Rigor reporting floor
- Output format
What does the pods-experiments skill do?
Use when designing or auditing the analytical "evidence" of an ACM PODS paper — worst-case and average-case analyses, matching upper and lower bounds, dichotomy completeness, correct complexity assumptions, and the occasional empirical validation when a theory paper claims practicality — matching the rigor to the shape of each theoretical claim.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill pods-experiments --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.