Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper or lower bound, deciding when supporting computation…
Install Icalp Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icalp-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icalp-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/ICALP-Skills/skills/icalp-experiments .claude/skills/icalp-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 "icalp-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICALP-Skills/skills/icalp-experiments into .claude/skills/icalp-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icalp-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/ICALP-Skills/skills/icalp-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 icalp-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icalp-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/ICALP-Skills/skills/icalp-experiments .agents/skills/icalp-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 "icalp-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICALP-Skills/skills/icalp-experiments into .agents/skills/icalp-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icalp-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 icalp-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icalp-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/ICALP-Skills/skills/icalp-experiments .cursor/skills/icalp-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 "icalp-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICALP-Skills/skills/icalp-experiments into .cursor/skills/icalp-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icalp-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 ICALP-Skills/skills/icalp-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 icalp-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icalp-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/ICALP-Skills/skills/icalp-experiments .gemini/skills/icalp-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 "icalp-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICALP-Skills/skills/icalp-experiments into .gemini/skills/icalp-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icalp-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 icalp-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 icalp-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/ICALP-Skills/skills/icalp-experiments .github/skills/icalp-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 "icalp-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICALP-Skills/skills/icalp-experiments into .github/skills/icalp-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icalp-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 icalp-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 icalp-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/ICALP-Skills/skills/icalp-experiments .opencode/skills/icalp-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 "icalp-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICALP-Skills/skills/icalp-experiments into .opencode/skills/icalp-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icalp-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
icalp-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 592 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper or lower bound, deciding when supporting computation…
- Matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper
- SKILL.md covers Match the argument to the claim, When computation legitimately…, Keep it a proof paper, not an… and Reproducibility of the…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Deciding when supporting computation (SAT/SMT-verified base cases
What it does
Icalp Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper or lower bound, deciding when supporting computation (SAT/SMT-verified base cases, computer-assisted case analysis, exhaustive small-case checks) legitimately backs a theorem, and keeping any such computation reproducible without turning a proof paper into an experimental one.
Its SKILL.md is about 1.3k 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 Research & Science. 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
- Matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper
- Deciding when supporting computation (SAT/SMT-verified base cases
- Computer-assisted case analysis
- Exhaustive small-case checks) legitimately backs a theorem
Example prompts
- “/icalp-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
Icalp Experiments loads about 1.3k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 592 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). 592 words, ~1,273 tokens.
Download SKILL.mdSave it as
.claude/skills/icalp-experiments/SKILL.md (or your agent's skills folder).- name
- icalp-experiments
- description
- Use when matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper or lower bound, deciding when supporting computation (SAT/SMT-verified base cases, computer-assisted case analysis, exhaustive small-case checks) legitimately backs a theorem, and keeping any such computation reproducible without turning a proof paper into an experimental one.
ICALP Experiments (proof strategy, and computation in service of proofs)
At ICALP there is usually no experiment section — the evidence for the claim is the proof. This
skill is therefore about matching the argument to the claim shape, and about the narrow, real
cases where computation supports a theorem (a computer-assisted proof, an SMT-checked base case, an
exhaustive small-case verification). It is deliberately not an empirical-evaluation guide: a paper
whose contribution is a benchmark result is mis-routed (icalp-topic-selection).
Match the argument to the claim
| Claim shape | The argument that fits | Common failure caught by referees |
|---|---|---|
| Upper bound / faster algorithm | Algorithm + correctness proof + complexity analysis | Correctness hand-waved; complexity ignores a hidden cost |
| Approximation ratio | An analysis bounding cost vs optimum, with a tight example | Ratio proved only on the easy case; no tight instance |
| Lower bound (unconditional) | A reduction, adversary, or information-theoretic argument | Model too weak to be interesting, or gap left open |
| Conditional lower bound | A fine-grained reduction from SETH/3SUM/APSP | Wrong assumption invoked; reduction loses a factor |
| Decidability / complexity (Track B) | A decision procedure + matching hardness | Procedure sketched; hardness for a different fragment |
| Dichotomy / characterization | Exhaustive case analysis with each case proved | A case silently dropped; "similarly" hiding a hard case |
When computation legitimately supports a theorem
Some ICALP results genuinely rely on computation. It must be rigorous and checkable, not suggestive:
- Exhaustive small-case verification — checking a property for all objects up to size k as a base case of an induction. State the exact range, the encoding, and make the search reproducible.
- SAT/SMT-certified steps — using a solver to verify a finite gadget or unsatisfiability. Ship the encoding and, where possible, an independently checkable certificate (UNSAT proof, Farkas witness), not just "the solver said so."
- Computer-assisted case analysis — a program enumerating cases in a proof. The program is part of the proof; its logic must be described and its output verifiable.
The bar: a referee (or a reader of the full version) must be able to re-run or independently check the computation. A number a solver produced with no reproducible input is not a proof step.
Show full SKILL.md (246 more words)Show less
Keep it a proof paper, not an experiment paper
- Supporting computation certifies a step; it does not replace the theorem. If the only evidence for the main claim is "it worked on our instances," the paper is experimental and belongs elsewhere.
- Do not add a benchmark table to a theory paper to look more complete — ICALP referees read it as either irrelevant or as a signal the theorem is weak.
- Running time measured is not running time proved. The contribution is the provable bound.
Reproducibility of the computational part
If computation backs a proof, treat it like the full version (see icalp-reproducibility):
- Provide the code and inputs (or precise pseudocode) in the appendix / full version or a public repository referenced at camera-ready.
- Provide certificates an independent checker can verify where the technique allows.
- Pin versions (solver, seed if randomized search) so the check is deterministic.
Worked vignette: a dichotomy with a computer-checked base
A Track B paper proves a dichotomy over a family of constraint languages: tractable vs NP-hard. The inductive step is by hand; the base cases (finitely many small languages) are verified by an exhaustive program. To meet the bar: state the finite base set precisely, describe the enumeration, ship the code and its output in the full version, and — for the hardness base cases — include reductions a referee can check by hand rather than leaving them to the program alone. State clearly which cases are machine-verified and which are proved analytically.
Output format
[Claim shape] upper / approximation / lower (uncond) / lower (conditional) / decidability / dichotomy
[Argument fit] the proof strategy matches the claim? gaps: <where>
[Computation role] none / base-case check / solver-certified step / computer-assisted cases
[Checkability] certificate or reproducible input provided? independent check possible? yes/no
[Not-an-experiment guard] is the theorem the evidence (not benchmark performance)? yes/no
[Fix queue] <ordered: proof gaps, missing certificates, mis-routed empirical framing>© 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 ICALP-Skills/skills/icalp-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Icalp 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 |
|---|---|---|---|---|---|---|
| Icalp Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Peer Reviewspacering-net/codeg | 3.9k | 17 repos | ~5.9k | Automated safety check: Notes | MIT |
Similar skills
GitHub Deep Research
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Nature Paper Card
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
Content Research Writer
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
Peer Review
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Structured manuscript/grant review with checklist-based evaluation.
Last30days
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Research what people actually say about any topic in the last 30 days.
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Related
Categories
Questions about Icalp Experiments
What does Icalp Experiments do?
A skill your agent uses when matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper or lower bound, deciding when supporting computation…. Icalp Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper or lower bound, deciding when supporting computation (SAT/SMT-verified base cases, computer-assisted case analysis, exhaustive small-case checks) legitimately backs a theorem, and keeping any such computation reproducible without turning a proof paper into an experimental one.
When should I use Icalp Experiments?
Icalp Experiments fits situations like: matching the argument of an ICALP (EATCS) theory paper to its claim — choosing the proof strategy for an upper; deciding when supporting computation (SAT/SMT-verified base cases; computer-assisted case analysis; exhaustive small-case checks) legitimately backs a theorem.
How do I install Icalp Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icalp-experiments -a claude-code`. Or copy the skill folder (ICALP-Skills/skills/icalp-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icalp-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Icalp Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icalp-experiments -a codex`. Or copy the skill folder (ICALP-Skills/skills/icalp-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icalp-experiments in your project. Codex loads it when a task matches its description.
Can I use Icalp 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 icalp-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/icalp-experiments, .gemini/skills/icalp-experiments, .github/skills/icalp-experiments and .opencode/skills/icalp-experiments in your project.
What does Icalp Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Icalp Experiments is instructions for the agent only.
Does Icalp 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 Icalp 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 Icalp Experiments use?
Icalp 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 Icalp Experiments use?
About 1.3k tokens (SKILL.md is roughly 5.1k 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 Icalp Experiments?
Skills that share tags, products or a category with Icalp Experiments: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Icalp 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.