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Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…
A skill your agent uses 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…
Install Ecai Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-experiments .claude/skills/ecai-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 "ecai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-experiments into .claude/skills/ecai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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/ECAI-Skills/skills/ecai-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 ecai-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-experiments .agents/skills/ecai-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 "ecai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-experiments into .agents/skills/ecai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ecai-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-experiments .cursor/skills/ecai-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 "ecai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-experiments into .cursor/skills/ecai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ECAI-Skills/skills/ecai-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 ecai-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills ecai-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/ECAI-Skills/skills/ecai-experiments .gemini/skills/ecai-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 "ecai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-experiments into .gemini/skills/ecai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ecai-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 ecai-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/ECAI-Skills/skills/ecai-experiments .github/skills/ecai-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 "ecai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-experiments into .github/skills/ecai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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 ecai-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 ecai-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/ECAI-Skills/skills/ecai-experiments .opencode/skills/ecai-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 "ecai-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECAI-Skills/skills/ecai-experiments into .opencode/skills/ecai-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ecai-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
ecai-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 514 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 evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAI's breadth (theory/KR, planning/search, ML, multi-agent…
- Auditing the evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAIs breadth (theory/KR
- SKILL.md covers Choose the evidence type by…, For theory / KR / planning /…, For ML / learning-based… and For multi-agent contributions, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Planning/search
What it does
Ecai Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.2k 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 Agent Workflows, covering Multi-agent orchestration. 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 evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAIs breadth (theory/KR
- Planning/search
- Seeds and spread
- Honest ablations
Example prompts
- “/ecai-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
Ecai Experiments loads about 1.2k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 514 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). 514 words, ~1,184 tokens.
Download SKILL.mdSave it as
.claude/skills/ecai-experiments/SKILL.md (or your agent's skills folder).- name
- ecai-experiments
- description
- 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.
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 an explanation, not just a number.
For theory / KR / planning / argumentation
- Prove it, completely. The body sketches; the supplement carries full proofs
(
ecai-reproducibility). State every assumption (finiteness, admissibility, language fragment). - Standard instances for empirical planning/search. Use recognized domains/benchmarks (e.g. the community's standard planning domains) so node/quality numbers are comparable; report per-domain results, not just an aggregate.
- Complexity claims get the reduction or the algorithm, not a hand-wave.
For ML / learning-based contributions
- Fair baselines, fairly tuned. Give the baseline the same tuning budget as your method; a hobbled baseline is the fastest way to lose a reviewer.
- Seeds and spread. Report mean and variance/CI across multiple seeds; a single run is not evidence. State the number of runs.
- Explain the win. ECAI rewards why a method works (an ablation isolating the responsible component, a theoretical reason) over a leaderboard delta.
- Contamination and leakage. For LLM/pretrained components, check train/test overlap and document model identifiers with dates; cache outputs so results reproduce.
Show full SKILL.md (195 more words)Show less
For multi-agent contributions
- Specify the environment, agents, episodes, and metrics exactly; multi-agent results are notoriously protocol-sensitive.
- Compare against the right baselines for the setting (cooperative/competitive), and report across seeds and environment variations, not one map.
- If the contribution is fundamentally about agent interaction, sanity-check whether AAMAS is
the better-matched pool (
ecai-topic-selection).
For applied AI (PAIS)
- Lead with the real-world claim and constraints (data availability, latency, cost, safety), not a benchmark score.
- Show the method survives real conditions; a deployment story that only reports offline accuracy under-delivers on the PAIS bar.
Ablations and honesty
- Ablate the mechanism you credit. If you attribute the gain to component C, remove C and show the drop.
- Report negative and null results where they bound the claim — in a single-round process,
self-reported limits cost less than reviewer-discovered ones (
ecai-review-process). - No cherry-picking domains, seeds, or metrics; report the protocol that generated every number.
Fit the 7-page body
Evidence a reviewer needs to judge the claim (the proof idea, the key comparison, the main table)
stays in the body; full proofs, extra domains, and ablation grids go to the supplement
(ecai-supplementary). Do not exile the decision-critical comparison to save space.
Output format
[Claim -> evidence] each claim mapped to proof / controlled comparison / deployment demo
[Proof completeness] provable claims proved with explicit assumptions? yes/no
[Baseline fairness] baselines tuned comparably? seeds + spread reported?
[Why it works] mechanism explained (ablation/theory), not just a number? yes/no
[Provenance] datasets/models/seeds pinned; outputs cached? gaps: <list>
[Body/supplement] decision-critical evidence inside 7 pages? yes/no© 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 ECAI-Skills/skills/ecai-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Ecai 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 |
|---|---|---|---|---|---|---|
| Ecai Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Orca CLIstablyai/orca | 88k | 2 repos | ~593 | Automated safety check: Pass | MIT | |
| Paseo Advisor Second Opiniongetpaseo/paseo | 20k | 1 repos | ~756 | Automated safety check: Pass | Custom licence | |
| O2 Review Loopopenobserve/openobserve | 22k | — | ~3.7k | Automated safety check: Pass | AGPL-3.0 | |
| Paseo Committeegetpaseo/paseo | 20k | 1 repos | ~496 | Automated safety check: Pass | Custom licence | |
| Mission Control Agent APIbuilderz-labs/mission-control | 6.3k | — | ~2.1k | Automated safety check: Pass | MIT |
Similar skills
Paseo Advisor Second Opinion
getpaseo/paseo
Launches one separate agent through Paseo to give a second opinion on the current task, with a self-contained briefing and no permission to edit files.
O2 Review Loop
openobserve/openobserve
Splits a change into planner, coder and independent reviewer roles: you confirm a spec, a subagent implements it, and a separate reviewer checks each round's local WIP commit.
Paseo Committee
getpaseo/paseo
Forms a two-agent committee with contrasting profiles to analyze a stuck problem in parallel, reconcile their views and return a consensus plan without editing files.
Mission Control Agent API
builderz-labs/mission-control
Teaches an agent to use the Mission Control dashboard API: register, send heartbeats, fetch assigned tasks, report progress and disconnect, with API key auth.
Paseo Agent Handoff
getpaseo/paseo
Hands off the current task, including context, decisions and failed attempts, to a fresh agent through Paseo by writing a self-contained briefing prompt and launching that agent.
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Categories
Questions about Ecai Experiments
What does Ecai Experiments do?
A skill your agent uses 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…. Ecai Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Ecai Experiments?
Ecai Experiments fits situations like: auditing the evidence in an ECAI paper — choosing proof versus experiment by claim shape across ECAIs breadth (theory/KR; planning/search; seeds and spread; honest ablations.
How do I install Ecai Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-experiments -a claude-code`. Or copy the skill folder (ECAI-Skills/skills/ecai-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/ecai-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Ecai Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-experiments -a codex`. Or copy the skill folder (ECAI-Skills/skills/ecai-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/ecai-experiments in your project. Codex loads it when a task matches its description.
Can I use Ecai 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 ecai-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/ecai-experiments, .gemini/skills/ecai-experiments, .github/skills/ecai-experiments and .opencode/skills/ecai-experiments in your project.
What does Ecai Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Ecai Experiments is instructions for the agent only.
Does Ecai 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 Ecai 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 Ecai Experiments use?
Ecai 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 Ecai Experiments use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 Ecai Experiments?
Skills that share tags, products or a category with Ecai Experiments: Orca CLI (stablyai/orca, 88k stars), Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Paseo Committee (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ecai Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.