Monitor CI
nrwl/nx
Monitor Nx Cloud CI pipeline and handle self-healing fixes. An agent skill from nrwl/nx.
A skill your agent uses 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…
Install Cikm Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-experiments --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/CIKM-Skills/skills/cikm-experiments .claude/skills/cikm-experiments && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "cikm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experiments into .claude/skills/cikm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-experiments", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experimentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/CIKM-Skills/skills/cikm-experiments .agents/skills/cikm-experiments && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "cikm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experiments into .agents/skills/cikm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-experiments", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/CIKM-Skills/skills/cikm-experiments .cursor/skills/cikm-experiments && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "cikm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experiments into .cursor/skills/cikm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-experiments", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path CIKM-Skills/skills/cikm-experiments--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/CIKM-Skills/skills/cikm-experiments .gemini/skills/cikm-experiments && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "cikm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experiments into .gemini/skills/cikm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-experiments", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-experimentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/CIKM-Skills/skills/cikm-experiments .github/skills/cikm-experiments && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "cikm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experiments into .github/skills/cikm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-experiments", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cikm-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/CIKM-Skills/skills/cikm-experiments .opencode/skills/cikm-experiments && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "cikm-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CIKM-Skills/skills/cikm-experiments into .opencode/skills/cikm-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cikm-experiments", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
cikm-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 834 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses 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…
- Auditing the empirical program of a CIKM paper — matching evidence to the claims lanes across retrieval
- SKILL.md covers Claim-lane-evidence contract, Dataset strategy for a blended…, Mechanism isolation at the… and Reporting floor, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Knowledge-management evaluation cultures
What it does
Cikm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Auditing the empirical program of a CIKM paper — matching evidence to the claims lanes across retrieval
- Knowledge-management evaluation cultures
- Choosing datasets and baselines that survive a blended panel
- Isolating the boundary mechanism
Example prompts
- “/cikm-experiments”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Cikm Experiments loads about 1.7k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 834 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 834 words, ~1,725 tokens.
Download SKILL.mdSave it as
.claude/skills/cikm-experiments/SKILL.md (or your agent's skills folder).- name
- cikm-experiments
- description
- 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.
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 messy dataset | Only clean benchmark data |
| KG/side-information helps | The identical model minus the KG, plus a KG-quality sweep | Confounding KG addition with capacity increase |
| Deployed impact (Applied track) | Launch evidence: online metrics, A/B or pre/post windows, traffic scale | Offline proxy metrics presented as deployment proof |
The Applied Research row is a track requirement, not a style preference: CIKM 2026's call demands substantiation by a system launch, data release, or equivalent practical evidence.
Dataset strategy for a blended panel
- Include at least one dataset each lane recognizes as legitimate for the task — a public IR/recommendation benchmark, a graph/KG corpus, and (when claiming practice) an industrial or organizational dataset, released or described honestly.
- Report per-dataset licenses, sizes, and collection windows; the resource-minded part of the pool reads dataset tables the way IR readers read metric tables.
- For LLM-era work, state the contamination position: whether test queries, documents, or KG facts could sit in a pretrained model's training data, and what was done about it. The blended pool increasingly asks this across all lanes.
Mechanism isolation at the boundary
CIKM contributions typically fuse components (a ranker + a KG; a miner + an index). The decisive experiment is the one that holds the fusion fixed and removes only the claimed novelty — otherwise the panel cannot tell the contribution from the engineering. Budget for it first; it is the experiment reviewers ask for when it is missing, and with no confirmed rebuttal channel at CIKM (待核实), a missing ablation cannot be repaired mid-review.
Reporting floor
For every results table/figure:
- datasets named with split protocol and seed policy
- baseline tuning budget stated (same search space as the proposed method?)
- variance across runs, and the test behind any "significant"
- hardware + software versions for anything timed
- pointer to the artifact path that regenerates itThe floor exists because the appendix cannot absorb it: 2026 budgets count appendices inside the page limit, so this information lives in captions, in the protocol paragraph, or in the cited artifact — never nowhere.
Show full SKILL.md (396 more words)Show less
Variance and significance protocol
The IR lane brings the family's strictest statistics culture, and it grades the whole paper by these habits:
- Multiple runs with distinct seeds for anything stochastic; report the spread (std or CI), not just the best run — "best of five" is a red flag, "mean ± std of five" is the norm.
- Paired tests for system comparisons on shared queries/instances; say which test and at what level, and correct for multiple comparisons when sweeping many configurations.
- Effect sizes over asterisks alone: a 0.2-point gain can be "significant" on a large query set and still not matter; state both the margin and the test.
- Per-slice reporting when the claim is slice-shaped (the gains "concentrate on relation-dependent queries" claim needs the slice table that shows it).
LLM-era pitfalls the panel now checks
- Contamination: pretrained components may have seen the test collections; state the exposure analysis or choose post-cutoff data.
- LLM-as-judge: if an LLM scores outputs, validate it against human labels on a sample and report agreement; unvalidated judge scores are decoration.
- Prompt variance: prompted baselines need the same tuning-budget parity as trained ones — report the prompt-search effort on both sides.
- API drift: hosted-model results must pin model version and date, or the
experiment is unrepeatable by construction (
cikm-reproducibility).
Design vignette
A paper claims: entity linking over enterprise wikis improves internal search. The minimum honest program: (1) linking quality vs. TAGME-line baselines on a public corpus, with significance; (2) end-to-end search quality with linking on/off — same index, same ranker; (3) an ablation degrading link precision synthetically to show the dependence; (4) one messy-data run (stale pages, duplicate entities) with the failure modes described. Four experiments, each answering a different lane.
Budgeting the program against the May gate
Experiment programs at this venue fail by sequencing more than by design. Order of execution when time is short: (1) the decisive ablation — it defines whether there is a paper; (2) the headline comparison with proper baselines and variance — it defines how strong; (3) one lane-coverage run for whichever community the claim still leaves unserved; (4) robustness/messy-data sweeps; (5) everything else. Cut from the bottom, never from the top. And freeze the protocol (splits, metrics, tuning budgets) in writing before results exist — protocol decisions made after seeing numbers are the reproducibility crisis's origin story, and the blended panel includes people who ask when the protocol was fixed.
Output format
[Contract table] <claim → lane → evidence → status>
[Decisive ablation] <what is removed, what stays fixed>
[Panel coverage] <which lane still has no dataset/evidence speaking to it>
[Reporting floor] <captions/protocol/artifact items missing>
[Next run] <the single experiment to schedule first>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in CIKM-Skills/skills/cikm-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cikm Experiments next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Cikm Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Monitor CInrwl/nx | 29k | 6 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Terraform and OpenTofu Guideagentscope-ai/QwenPaw | 35k | 6 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 9 repos | ~4.3k | Automated safety check: Pass | None | |
| Openclaw Live Updateropenclaw/openclaw | 392k | — | ~3.7k | Automated safety check: Pass | MIT | |
| Analyze GitHub Action Logswithastro/astro | 63k | 1 repos | ~1.3k | Automated safety check: Pass | Custom licence |
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Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
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Maintain the canonical live OpenClaw main checkout, macOS LaunchAgent-managed Gateway, local macOS app, exact-head main CI, and recurring full release validation.
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Analyze recent GitHub Actions workflow runs to identify patterns, mistakes, and improvements.
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Categories
Questions about Cikm Experiments
What does Cikm Experiments do?
A skill your agent uses 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…. Cikm Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Cikm Experiments?
Cikm Experiments fits situations like: auditing the empirical program of a CIKM paper — matching evidence to the claims lanes across retrieval; knowledge-management evaluation cultures; choosing datasets and baselines that survive a blended panel; isolating the boundary mechanism.
How do I install Cikm Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a claude-code`. Or copy the skill folder (CIKM-Skills/skills/cikm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cikm-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Cikm Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a codex`. Or copy the skill folder (CIKM-Skills/skills/cikm-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cikm-experiments in your project. Codex loads it when a task matches its description.
Can I use Cikm Experiments in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cikm-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cikm-experiments, .gemini/skills/cikm-experiments, .github/skills/cikm-experiments and .opencode/skills/cikm-experiments in your project.
What does Cikm Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Cikm Experiments is instructions for the agent only.
Does Cikm Experiments access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Cikm Experiments safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Cikm Experiments use?
Cikm Experiments is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Cikm Experiments use?
About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Cikm Experiments?
Skills that share tags, products or a category with Cikm Experiments: Monitor CI (nrwl/nx, 29k stars), Terraform and OpenTofu Guide (agentscope-ai/QwenPaw, 35k stars), Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars) and Openclaw Live Updater (openclaw/openclaw, 392k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cikm Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.