cikm-experiments
Use when designing or auditing the empirical program of a CIKM paper — matching evidence to the claim's lanes across retrieval, mining, and knowledge-management evaluation cultures, choosing datasets and baselines that survive a blended panel, isolating the boundary mechanism, and meeting applied-track deployment-evidence bars.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-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.
# CIKM Experiments The empirical program of a CIKM paper answers to three evaluation cultures. IR culture wants ranked-metric discipline on recognized collections with significance testing; mining culture wants mechanism isolation, scaling behavior, and honest baselines; KM/database culture wants evidence that the method survives real data — heterogeneous, noisy, incomplete. Design the experiment section as the union of what the claimed lanes require, not the maximum of one. ## Claim-lane-evidence contract For every claim sentence in the introduction, write down which lane it invokes and what that lane's standard evidence is: | Claim invokes... | Required evidence pattern | Frequent hole | |---|---|---| | Better retrieval/ranking | Standard collections, tuned baselines, metric@k with paired significance tests | Untuned baselines; "significant" without a test | | A better mechanism | Ablation removing exactly that mechanism; sensitivity to its key parameter | Ablations that vary three things at once | | Scalability | Time/memory vs. input-size curves on stated hardware | A single wall-clock number, no curve | | Robust to real data | Noise/incompleteness injection or a genuinely mess
- Claim-lane-evidence contract
- Dataset strategy for a blended panel
- Mechanism isolation at the boundary
- Reporting floor
- Variance and significance protocol
- LLM-era pitfalls the panel now checks
- Design vignette
- Budgeting the program against the May gate
- Output format
What does the cikm-experiments skill do?
Use when designing or auditing the empirical program of a CIKM paper — matching evidence to the claim's lanes across retrieval, mining, and knowledge-management evaluation cultures, choosing datasets and baselines that survive a blended panel, isolating the boundary mechanism, and meeting applied-track deployment-evidence bars.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-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.