ecai-experiments
Use when designing or auditing the evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAI's breadth (theory/KR, planning/search, ML, multi-agent, applied), fair baselines, seeds and spread, honest ablations, and provenance, all supporting a claim inside a 7-page body.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-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.
# ECAI Experiments The ECAI question is always the same: **is the evidence proportional to the claim?** But ECAI spans symbolic and applied AI, so "evidence" ranges from a **proof** to a **fair empirical comparison**. Choosing the right *kind* of evidence for your claim shape is the first and most important decision. ## Choose the evidence type by claim shape | Claim | Primary evidence | Common ECAI failure | |---|---|---| | "Complete / sound / optimal / (1+ε)-bounded" | A **proof**, all assumptions explicit | Asserting it empirically only | | "More efficient / fewer expansions / faster" | A **controlled comparison** on standard instances, with spread | One lucky run; unfair baseline tuning | | "Learns/generalizes/calibrates better" | Fair comparison + a *reason why*, seeds, significance | A single benchmark delta with no mechanism | | "Handles a broader class / new setting" | A construction/encoding + worked cases | Toy examples only | | "Works in the real world" | A credible deployment demonstration (**PAIS**) | Benchmark abstraction standing in for deployment | A provable claim needs a proof; an empirical claim needs a fair, seeded comparison; a claim about *understanding* needs
- Choose the evidence type by claim shape
- For theory / KR / planning / argumentation
- For ML / learning-based contributions
- For multi-agent contributions
- For applied AI (PAIS)
- Ablations and honesty
- Fit the 7-page body
- Output format
What does the ecai-experiments skill do?
Use when designing or auditing the evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAI's breadth (theory/KR, planning/search, ML, multi-agent, applied), fair baselines, seeds and spread, honest ablations, and provenance, all supporting a claim inside a 7-page body.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-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.