Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
A skill your agent uses when designing the experimental program for an ICCV paper against the early-March deadline, covering benchmark-drift audits across the two-year gap, baseline fairness in the…
Install Iccv Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-experiments .claude/skills/iccv-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 "iccv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-experiments into .claude/skills/iccv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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/ICCV-Skills/skills/iccv-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 iccv-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-experiments .agents/skills/iccv-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 "iccv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-experiments into .agents/skills/iccv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-experiments .cursor/skills/iccv-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 "iccv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-experiments into .cursor/skills/iccv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 ICCV-Skills/skills/iccv-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 iccv-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-experiments .gemini/skills/iccv-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 "iccv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-experiments into .gemini/skills/iccv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-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 iccv-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/ICCV-Skills/skills/iccv-experiments .github/skills/iccv-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 "iccv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-experiments into .github/skills/iccv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-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 iccv-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/ICCV-Skills/skills/iccv-experiments .opencode/skills/iccv-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 "iccv-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-experiments into .opencode/skills/iccv-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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
iccv-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 705 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing the experimental program for an ICCV paper against the early-March deadline, covering benchmark-drift audits across the two-year gap, baseline fairness in the…
- Works in 5 steps: Falsifiers first (autumn): the core… → Headline table (winter): full benchmark… → Breadth pass (January): second domain,… → …
- Designing the experimental program for an ICCV paper against the early-March deadline
- SKILL.md covers Start with the benchmark-drift…, The fairness ledger, Ablations that isolate, not… and Qualitative evidence with…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Iccv Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing the experimental program for an ICCV paper against the early-March deadline, covering benchmark-drift audits across the two-year gap, baseline fairness in the foundation-model era, ablations that isolate mechanisms, qualitative and failure evidence, and sequencing runs so the decisive result lands before the deadline, not after.
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 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
- Designing the experimental program for an ICCV paper against the early-March deadline
- Covering benchmark-drift audits across the two-year gap
- Baseline fairness in the foundation-model era
- Ablations that isolate mechanisms
Example prompts
- “/iccv-experiments”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Falsifiers first (autumn): the core ablation and the strongest baseline
- Headline table (winter): full benchmark suite, matched settings, seeds on
- Breadth pass (January): second domain, transfer, robustness suite —
- Freeze margin (mid-February): the main table freezes ~2–3 weeks out; the
- Rebuttal reserve: hold 10–20% of compute for May; the most common
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 (its code samples are python).
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
Iccv Experiments loads about 1.7k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 705 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). 705 words, ~1,660 tokens.
Download SKILL.mdSave it as
.claude/skills/iccv-experiments/SKILL.md (or your agent's skills folder).- name
- iccv-experiments
- description
- Use when designing the experimental program for an ICCV paper against the early-March deadline, covering benchmark-drift audits across the two-year gap, baseline fairness in the foundation-model era, ablations that isolate mechanisms, qualitative and failure evidence, and sequencing runs so the decisive result lands before the deadline, not after.
ICCV Experiments
An ICCV experimental program is built under a fixed, unrepeatable date: the early-March deadline of an odd year (March 7 in 2025, with supplement due the same day). The program design problem is therefore sequencing under a deadline, on top of the usual question of what evidence convinces a vision reviewer. Both are handled here.
Start with the benchmark-drift audit
Because the venue is biennial, a project conceived after one ICCV and submitted to the next spans two years of field motion. Before designing experiments, audit what moved:
drift audit (fill once, in the autumn before the deadline)
benchmarks: which datasets did the last two years of CVPR/ECCV/ICCV papers
in this area actually evaluate on? any new canonical benchmark?
baselines: leaderboard top-3 today vs when the project started
→ any baseline in your draft older than ~18 months is a red flag
backbones: what does current SOTA initialize from? (matching this defines
"fair" for your comparisons)
metrics: any metric revision or new evaluation server since last cycle?
protocols: resolution/prompt/eval-harness conventions that changedPapers rejected for "outdated comparisons" are usually not lazy — they froze their experiment matrix at project start and never re-based. Re-run the audit in January; two months before an ICCV deadline is exactly when the previous November's CVPR-cycle preprints flood arXiv.
The fairness ledger
Vision reviewers' most reliable objection is compute-and-pretraining confounds dressed as method wins. Make fairness auditable with a ledger column per comparison:
| Axis | Your method | Each baseline | Mismatch handling |
|---|---|---|---|
| Backbone + init checkpoint | Match, or add a matched row | ||
| Pretraining data exposure | Disclose; beware test-adjacent leakage in web-scale corpora | ||
| Input resolution / tokens | Match or tabulate both | ||
| Training schedule + budget | Report epochs and GPU-hours side by side | ||
| Number quoted vs re-run | Mark re-runs; footnote protocol deltas |
The foundation-model twist: when everything builds on the same giant checkpoint, data exposure replaces architecture as the confound reviewers hunt. If your improvement could plausibly come from the pretrain having seen the test domain, run the decontamination or cross-domain check before a reviewer asks for it in a window when you have one rebuttal page to respond.
Ablations that isolate, not decorate
Structure the ablation grid so every row flips one switch, and include the two rows that distinguish a mechanism from a lucky configuration: the transplant (your module inserted into a baseline — does the gain travel?) and the sensitivity sweep (is the headline number a plateau or a spike?). Rows argued from in the text belong in the body; the full grid goes to the same-day supplement. If the core ablation shows the mechanism is not doing the work, that is an October discovery you want in November — which is why it runs first (see sequencing below).
Qualitative evidence with stated rules
At a venue that reviews with its eyes, image and video evidence carries real
weight and attracts real skepticism. Three requirements: a declared selection
rule on every grid ("first N val images", "random seed 0" — curation without a
rule is what reviewers assume by default); side-by-sides against the two
strongest baselines on identical inputs; and a failure-mode section with a
taxonomy, previewed in the body and cataloged in the supplement. For temporal or
3D claims, the supplement video is the primary exhibit — packaging in
iccv-supplementary.
Show full SKILL.md (250 more words)Show less
Sequencing runs toward March
The scarce resource is calendar, not GPUs. Order the program by decision value per week:
- Falsifiers first (autumn): the core ablation and the strongest baseline
comparison. If the idea dies, it dies while retargeting to CVPR-November is
still possible (
iccv-workflow's autumn fork). - Headline table (winter): full benchmark suite, matched settings, seeds on the cheap rows.
- Breadth pass (January): second domain, transfer, robustness suite — whatever supports the claim's outer scope.
- Freeze margin (mid-February): the main table freezes ~2–3 weeks out; the deadline is same-day for the supplement, so late results have no legal landing zone except the rebuttal — and reviewers may not request major new experiments anyway.
- Rebuttal reserve: hold 10–20% of compute for May; the most common
winnable rebuttal item is a small matched re-run reported as a mini-table
(
iccv-author-response).
# deadline_math.py — sanity-check the plan against the calendar
runs = {"core_ablation": 6, "main_table": 21, "breadth": 10} # GPU-days each
gpu = 8; days_left = (deadline - today).days - 18 # freeze margin
assert sum(runs.values()) / gpu <= days_left, "cut scope now, not in February"The four questions any ICCV review silently asks
Does it work (main table, matched)? Why does it work (isolating ablations +
transplant)? Where does it break (failure taxonomy, honest transfer results)?
What does it cost (params, latency on named hardware, training GPU-hours —
volunteered, since no form mandates it; see iccv-reproducibility)? Draft the
experiments section as answers to these four, in this order, and the reviewer's
checklist fills itself.
Reverify each cycle
- The 2027 deadline chain — sequencing above is calendar-shaped and the calendar is 待核实 until posted.
- Whether supplements stay same-day (changes step 4).
- Evaluation-server rules and submission budgets on your benchmarks.
- Any new ethics/human-data documentation the 2027 forms may require.
Output format
[Drift audit] run on <date>; stale baselines found: <list>
[Fairness ledger] axes matched or disclosed per comparison: n/m
[Ablation] one-switch rows: <n>; transplant + sensitivity present: yes/no
[Qualitative] selection rules stated · failure taxonomy drafted
[Sequencing] falsifiers scheduled before <date>; rebuttal reserve: <GPU-days>
[Cut candidates] <lowest decision-value runs if the calendar slips>© 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 ICCV-Skills/skills/iccv-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Iccv 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 |
|---|---|---|---|---|---|---|
| Iccv Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | 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 |
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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
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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.
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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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Categories
Questions about Iccv Experiments
What does Iccv Experiments do?
A skill your agent uses when designing the experimental program for an ICCV paper against the early-March deadline, covering benchmark-drift audits across the two-year gap, baseline fairness in the…. Iccv Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing the experimental program for an ICCV paper against the early-March deadline, covering benchmark-drift audits across the two-year gap, baseline fairness in the foundation-model era, ablations that isolate mechanisms, qualitative and failure evidence, and sequencing runs so the decisive result lands before the deadline, not after.
When should I use Iccv Experiments?
Iccv Experiments fits situations like: designing the experimental program for an ICCV paper against the early-March deadline; covering benchmark-drift audits across the two-year gap; baseline fairness in the foundation-model era; ablations that isolate mechanisms.
How do I install Iccv Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-experiments -a claude-code`. Or copy the skill folder (ICCV-Skills/skills/iccv-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/iccv-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Iccv Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-experiments -a codex`. Or copy the skill folder (ICCV-Skills/skills/iccv-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/iccv-experiments in your project. Codex loads it when a task matches its description.
Can I use Iccv 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 iccv-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/iccv-experiments, .gemini/skills/iccv-experiments, .github/skills/iccv-experiments and .opencode/skills/iccv-experiments in your project.
What does Iccv Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Iccv Experiments is instructions for the agent only. Our summary lists: Python 3.
Does Iccv 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 Iccv 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 Iccv Experiments use?
Iccv 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 Iccv Experiments use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 Iccv Experiments?
Skills that share tags, products or a category with Iccv 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 Iccv 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.