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Agent skill

Itcs Experiments

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare…

MITAuto-check passedResearch & Science

Install Itcs Experiments

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill itcs-experiments -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills itcs-experiments --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ITCS-Skills/skills/itcs-experiments .claude/skills/itcs-experiments && rm -rf skills-src

Use ~/.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/

Facts

Skill name
itcs-experiments
GitHub stars
1.2k
Token cost
~1.3k tokens
SKILL.md length
588 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare…

  • Deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence
  • SKILL.md covers Proofs are the evidence, Worked examples and…, The rare, well-scoped… and What NOT to import from…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Worked examples and separations that make a model concrete

What it does

Itcs Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare, well-scoped illustrative computation or simulation — and how to keep any computational content checkable and subordinate to the mathematics.

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

  • Deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence
  • Worked examples and separations that make a model concrete
  • Well-scoped illustrative computation
  • Simulation — and how to keep any computational content checkable and subordinate to the mathematics

Example prompts

  • “/itcs-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

Itcs Experiments loads about 1.3k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 588 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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). 588 words, ~1,341 tokens.

Download SKILL.mdSave it as .claude/skills/itcs-experiments/SKILL.md (or your agent's skills folder).
name
itcs-experiments
description
Use when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare, well-scoped illustrative computation or simulation — and how to keep any computational content checkable and subordinate to the mathematics.

ITCS Experiments

ITCS is a pure-theory venue: the primary — usually the only — evidence for a claim is a proof. There is no experiments requirement, no benchmark, no leaderboard, and no reviewer expectation of an empirical section. Bringing an ML-conference or SE reflex here (a table of numbers "showing" the method works) misreads the venue: at ITCS a theorem is proved, not measured. This skill is about matching evidence to a theory claim and about the rare case where a small computation genuinely helps.

Proofs are the evidence

  • Every central claim is settled by a complete proof, not by examples. A pattern that "holds in all cases we tried" is a conjecture, not a theorem — label it as such and prove or drop it.
  • Match the claim shape to the argument shape: an upper bound needs a construction + analysis; a lower bound needs an adversary/reduction; a separation needs a witness object; an impossibility needs a contradiction from the assumption. Reviewers check that the kind of argument fits the kind of claim.
  • A new model needs an anchoring result (see itcs-writing-style) — a separation or a surprising possibility that proves the model is neither empty nor everything. That anchoring result is the "experiment" an ITCS PC wants: evidence the model is alive.

Worked examples and separations as evidence

Non-proof evidence that is welcome, because it makes the mathematics concrete:

  • Worked examples that instantiate a definition on a small case and show the intended behavior — invaluable for a new model, and cheap insurance against the "is this trivial?" objection.
  • Explicit separating objects — a small graph, code, distribution, or gadget that witnesses a gap between two settings. If finite, include the object so a reviewer verifies the separation directly.
  • Tight-example constructions showing an analysis cannot be improved — the theory analogue of an ablation, demonstrating the bound is not loose by accident.
Show full SKILL.md (282 more words)Show less

The rare, well-scoped computation

Some ITCS papers include a small computational component: a computer search that found a gadget, a SAT/SMT solve certifying a finite separation, a numerically evaluated construction. When one genuinely helps, scope it tightly:

  • It supports a proved claim; it is never the claim. "A search over all graphs on <= 12 vertices found the gadget of Lemma 4, whose properties we then prove" is legitimate. "Our method achieves 92% on a benchmark" is a category error at ITCS.
  • Make it checkable without rerunning. State the exact search space, the tool and version, and — crucially — include the finite object the search produced (the graph, certificate, code) so verification is a static check, not a re-computation. A reviewer should be able to confirm the object has the claimed property by hand or with a one-line check.
  • Report it honestly. If a construction is only verified numerically (not proved), say so and mark exactly which claims rest on computation versus proof.
  • Keep it off the anonymity leak surface. A linked repository under a personal GitHub is a lightweight-double-blind slip; fold the object into an appendix or host it neutrally (see itcs-submission).

What NOT to import from empirical venues

Empirical-venue habitWhy it misfires at ITCS
A benchmark table as the main resultITCS proves; it does not measure. A table cannot establish a theorem
"Outperforms baselines by X%" framingThere are no baselines to beat; the contribution is an idea/proof
Runtime plots to argue efficiencyState and prove the asymptotic bound instead
An artifact/reproducibility package of codeNo artifact track exists; the "artifact" is the proof (see itcs-artifact-evaluation)
Statistical significance / error barsIrrelevant to a deterministic mathematical claim

Decision procedure

text
[Claim] is it a theorem (prove it) or a pattern (label as conjecture, or prove/drop)?
[Argument fit] upper=construction+analysis / lower=adversary / separation=witness / impossibility=contradiction
[Alive] new model? -> anchoring separation or surprising-possibility result present?
[Compute?] does a small search/solve genuinely help a proved claim? if not, omit it
[Checkable] if compute used: search space + tool/version stated, finite object included?
[Honesty] each claim tagged proved vs. numerically-verified; anonymity leak surface clean?

Output format

text
[ITCS evidence status] proof-complete / gaps / mis-imported-empirics
[Central claims] each has a complete proof of matching shape? yes/no + list gaps
[Model alive] anchoring result present for any new model? yes/no
[Computation] present? if so: supports-a-proof only? checkable object included?
[Anonymity] no personal-repo leak from any computational content? yes/no
[Fix queue] <ordered edits>

© 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 ITCS-Skills/skills/itcs-experiments of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Nature Paper CardYuan1z0825/nature-skills46k2 repos~2.1kAutomated safety check: PassApache-2.0
Read arXiv Paperkarpathy/nanochat58k2 repos~494Automated safety check: PassMIT
Content Research Writerweapp-tailwindcss/weapp-tailwindcss1.9k25 repos~3.5kAutomated safety check: PassMIT

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Categories

Questions about Itcs Experiments

What does Itcs Experiments do?

A skill your agent uses when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare…. Itcs Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence, worked examples and separations that make a model concrete, and the rare, well-scoped illustrative computation or simulation — and how to keep any computational content checkable and subordinate to the mathematics.

When should I use Itcs Experiments?

Itcs Experiments fits situations like: deciding what counts as evidence for an ITCS theory claim — proofs as the primary evidence; worked examples and separations that make a model concrete; well-scoped illustrative computation; simulation — and how to keep any computational content checkable and subordinate to the mathematics.

How do I install Itcs Experiments in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill itcs-experiments -a claude-code`. Or copy the skill folder (ITCS-Skills/skills/itcs-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/itcs-experiments in your project. Claude Code loads it when a task matches its description.

How do I install Itcs Experiments in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill itcs-experiments -a codex`. Or copy the skill folder (ITCS-Skills/skills/itcs-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/itcs-experiments in your project. Codex loads it when a task matches its description.

Can I use Itcs 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 itcs-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/itcs-experiments, .gemini/skills/itcs-experiments, .github/skills/itcs-experiments and .opencode/skills/itcs-experiments in your project.

What does Itcs Experiments need to run?

SKILL.md names no scripts, command-line tools or credentials: Itcs Experiments is instructions for the agent only.

Does Itcs 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 Itcs 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 Itcs Experiments use?

Itcs 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 Itcs Experiments use?

About 1.3k tokens (SKILL.md is roughly 5.4k 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 Itcs Experiments?

Skills that share tags, products or a category with Itcs Experiments: Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Read arXiv Paper (karpathy/nanochat, 58k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Itcs 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.