podc-experiments
Use when building the evidence for an ACM PODC paper — where "evidence" is a proof, not a benchmark. Covers matching upper and lower bounds, tightness arguments, model and assumption stress-tests, adversary-strength calibration, and the honest, clearly-optional role of any simulation in a distributed-computing-theory paper.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill podc-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.
# PODC Experiments At PODC the "experiment" is a **proof**, and the strongest result is a **matching bound**. This skill is the theory analogue of an empirical-evaluation section: it is about making the *evidence for correctness and optimality* as strong as the venue expects. There is no benchmark leaderboard, no artifact track, and a simulation never establishes the result — it can only illustrate it. ## Match the evidence to the claim shape | Claim | What counts as evidence at PODC | Common failure caught | |---|---|---| | "Algorithm A solves problem P in model M" | A proof of the required properties (e.g., agreement, validity, termination) in exactly M | Property proved in a stronger model than claimed | | "A costs O(f(n))" | A proof of the upper bound over all executions/adversary choices | Bound holds only in the best case, not worst/expected as stated | | "A is optimal" | A **matching Ω(f(n)) lower bound** in the same model | "Optimal" asserted with no lower bound | | "P is impossible in M" | An impossibility proof (valency, indistinguishability, covering) | Impossibility shown for a weaker model than claimed | | "A self-stabilizes" | A proof of convergence from every state +
- Match the evidence to the claim shape
- The matching lower bound is the headline evidence
- Stress-test the model and assumptions
- Calibrate the adversary and the regime
- Simulations: optional, illustrative, honest
- Pre-submission evidence checklist
- Output format
What does the podc-experiments skill do?
Use when building the evidence for an ACM PODC paper — where "evidence" is a proof, not a benchmark. Covers matching upper and lower bounds, tightness arguments, model and assumption stress-tests, adversary-strength calibration, and the honest, clearly-optional role of any simulation in a distributed-computing-theory paper.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill podc-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 909 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.