LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
A skill your agent uses when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart…
Install Facct Camera Ready
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-camera-ready -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-camera-ready --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/FAccT-Skills/skills/facct-camera-ready .claude/skills/facct-camera-ready && 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 "facct-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-camera-ready into .claude/skills/facct-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-camera-ready", 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/FAccT-Skills/skills/facct-camera-readyType 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 facct-camera-ready -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-camera-ready --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/FAccT-Skills/skills/facct-camera-ready .agents/skills/facct-camera-ready && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "facct-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-camera-ready into .agents/skills/facct-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-camera-ready", 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 facct-camera-ready -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-camera-ready --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/FAccT-Skills/skills/facct-camera-ready .cursor/skills/facct-camera-ready && 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 "facct-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-camera-ready into .cursor/skills/facct-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-camera-ready", 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 FAccT-Skills/skills/facct-camera-ready--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 facct-camera-ready -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills facct-camera-ready --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/FAccT-Skills/skills/facct-camera-ready .gemini/skills/facct-camera-ready && 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 "facct-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-camera-ready into .gemini/skills/facct-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-camera-ready", 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 facct-camera-readyInstalls 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 facct-camera-ready -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/FAccT-Skills/skills/facct-camera-ready .github/skills/facct-camera-ready && 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 "facct-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-camera-ready into .github/skills/facct-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-camera-ready", 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 facct-camera-ready -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 facct-camera-ready --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/FAccT-Skills/skills/facct-camera-ready .opencode/skills/facct-camera-ready && 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 "facct-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FAccT-Skills/skills/facct-camera-ready into .opencode/skills/facct-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "facct-camera-ready", 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
facct-camera-ready- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 446 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart…
- Preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements
- SKILL.md covers Camera-ready audit, De-anonymization + endmatter…, ACM production checks and Worked example: integrating a…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Switching to the acmart sigconf proceedings format
What it does
Facct Camera Ready is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart sigconf proceedings format, ACM metadata (DOI, ORCID, CCS concepts, rights), the two-round camera-ready calendar for Accept vs Revise papers, ACM Open Access, finalizing datasheets/model cards, and integrating required changes without scope creep.
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 AI & LLM Engineering, covering Model hubs and datasets. 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
- Preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements
- Switching to the acmart sigconf proceedings format
- ACM metadata (DOI
- The two-round camera-ready calendar for Accept vs Revise papers
Example prompts
- “/facct-camera-ready”
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
Facct Camera Ready loads about 1.3k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 446 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). 446 words, ~1,308 tokens.
Download SKILL.mdSave it as
.claude/skills/facct-camera-ready/SKILL.md (or your agent's skills folder).- name
- facct-camera-ready
- description
- Use when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart sigconf proceedings format, ACM metadata (DOI, ORCID, CCS concepts, rights), the two-round camera-ready calendar for Accept vs Revise papers, ACM Open Access, finalizing datasheets/model cards, and integrating required changes without scope creep.
FAccT Camera Ready
Use this after acceptance. FAccT papers are archival ACM proceedings (unless you chose non-archival), so the camera-ready is an ACM production step. Reopen the current camera-ready instructions, the decision email, and the rights/Open-Access pages before advising — and note the two-round calendar: Round-1 accepts and Revise-and-resubmit accepts have different camera-ready deadlines (FAccT 2026: 24 April and 11 May respectively).
Camera-ready audit
- De-anonymize completely and add the withheld endmatter: restore the author block, affiliations, and now add the Positionality, Acknowledgements, Author Contributions, and Competing Interests statements that mutual anonymity required you to hold back. The Positionality statement in particular exists only from this point on.
- Switch to the proceedings format: move from the anonymous
reviewbuild to the acmartsigconfcamera-ready, within the current page budget (Revised/accepted papers may use 15 content pages; confirm). - Keep the required Generative AI Usage Statement accurate for the final manuscript.
- Complete ACM metadata: title/abstract, author order, ORCIDs, ACM CCS concepts and keywords, and the DOI-bearing citation once assigned.
- Integrate reviewer/AC-required changes faithfully — without strengthening claims beyond what was evaluated in the Revise round.
- Finalize accountability documentation: replace anonymized artifact links with permanent, licensed ones, and align the datasheet / model card / impact assessment with the released version and the paper's tables.
- Confirm presentation: at least one author presents in person (archival or non-archival).
Show full SKILL.md (227 more words)Show less
De-anonymization + endmatter sweep
| Held back / anonymized at submission | Restore or add at camera-ready | Watch for |
|---|---|---|
| Author block, affiliations, ORCID | Full, correctly ordered | Wrong author order breaks the DOI citation |
| Positionality statement | Add now (it was withheld) | Leaving it out entirely — it is expected here |
| Acknowledgements, funding, Competing Interests | Restored / added | Grant numbers required by funders |
| System / field-site name | Real name throughout text, figures, artifact | A leftover anonymized placeholder in a caption |
| Self-citations (third person) | Natural first-person where it aids clarity | Over-correcting and double-citing |
| Artifact / data link | Public, licensed, persistent archive | The old anonymized URL surviving in a footnote |
ACM production checks
[Template] acmart sigconf, current revision; no manual margin/font edits
[Metadata] title, abstract, authors+ORCID, CCS concepts, keywords entered and matching the PDF
[Rights] ACM e-rights form completed; correct rights/Open-Access statement on the first page
[Open Access] since 1 Jan 2026 ACM proceedings are 100% OA — confirm ACM Open coverage or the APC
path (fee-waiver policy 待核实) well before the deadline, not at the last minute
[Endmatter] Generative AI Usage kept; Positionality/Acks/Contributions added; ethics/adverse-impacts final
[References] complete, consistent; DOIs where available (references are unlimited)
[Links] every artifact / data-availability link resolves from a logged-out browserWorked example: integrating a Revise-round fix
The Area Chair required adding disaggregation by an intersectional subgroup and correcting a doctrinal framing. Camera-ready move: place the new subgroup table in the results with its uncertainty and the utility trade-off, fix the legal framing and cite the doctrine to its real origin, add the now-permitted Positionality statement reflecting on the authors' standpoint, and point the data link at the public, licensed archive with a finalized datasheet — without expanding the harm claim the reviewers accepted.
Hedged logistics
- The two camera-ready dates, page allowances, metadata fields, rights mechanics, and the ACM Open / APC specifics change each cycle and differ by round; confirm against the decision email and current instructions rather than a prior year. APC/fee-waiver details are 待核实.
Output format
[Camera-ready status] ready / needs fixes / blocked
[Round] round-1 accept / revise-and-resubmit accept (deadline differs)
[De-anonymization] author block / system name / links restored? yes/no
[Endmatter added] Positionality / Acknowledgements / Contributions present now? yes/no
[ACM metadata] ORCID / CCS / keywords / rights + Open-Access path complete? yes/no
[Reviewer-change map] <required change -> final edit, no scope creep>
[Documentation] datasheet/model card finalized and consistent? 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 FAccT-Skills/skills/facct-camera-ready of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Facct Camera Ready 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 |
|---|---|---|---|---|---|---|
| Facct Camera Ready this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Local Model Evalshuggingface/skills | 11k | 2 repos | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Upload Post Imagehuggingface/blog | 3.5k | — | ~1.1k | Automated safety check: Pass | None | |
| Add Archon Modelareal-project/AReaL | 5.8k | — | ~4.9k | Automated safety check: Pass | Apache-2.0 |
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- Official
SageMaker Serving Image Selection
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
- Official
Hugging Face Local Model Evals
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
- Official
Upload Post Image
huggingface/blog
A skill your agent uses when adding or migrating non-thumbnail images for a Hugging Face Blog post.
Add Archon Model
areal-project/AReaL
Guide for adding a new model to the Archon engine. An agent skill from areal-project/AReaL.
Esmfold2
JimLiu/science-skills
Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al.
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Related
Categories
Questions about Facct Camera Ready
What does Facct Camera Ready do?
A skill your agent uses when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart…. Facct Camera Ready is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements, switching to the acmart sigconf proceedings format, ACM metadata (DOI, ORCID, CCS concepts, rights), the two-round camera-ready calendar for Accept vs Revise papers, ACM Open Access, finalizing datasheets/model cards, and integrating required changes without scope creep.
When should I use Facct Camera Ready?
Facct Camera Ready fits situations like: preparing an accepted ACM FAccT paper for camera-ready — de-anonymizing and adding the withheld Positionality/Acknowledgements statements; switching to the acmart sigconf proceedings format; ACM metadata (DOI; the two-round camera-ready calendar for Accept vs Revise papers.
How do I install Facct Camera Ready in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-camera-ready -a claude-code`. Or copy the skill folder (FAccT-Skills/skills/facct-camera-ready in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/facct-camera-ready in your project. Claude Code loads it when a task matches its description.
How do I install Facct Camera Ready in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill facct-camera-ready -a codex`. Or copy the skill folder (FAccT-Skills/skills/facct-camera-ready in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/facct-camera-ready in your project. Codex loads it when a task matches its description.
Can I use Facct Camera Ready 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 facct-camera-ready -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/facct-camera-ready, .gemini/skills/facct-camera-ready, .github/skills/facct-camera-ready and .opencode/skills/facct-camera-ready in your project.
What does Facct Camera Ready need to run?
SKILL.md names no scripts, command-line tools or credentials: Facct Camera Ready is instructions for the agent only.
Does Facct Camera Ready 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 Facct Camera Ready 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 Facct Camera Ready use?
Facct Camera Ready 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 Facct Camera Ready use?
About 1.3k tokens (SKILL.md is roughly 5.2k 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 Facct Camera Ready?
Skills that share tags, products or a category with Facct Camera Ready: LLM Benchmarking with lm-evaluation-harness (Orchestra-Research/AI-Research-SKILLs, 13k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Hugging Face Local Model Evals (huggingface/skills, 11k stars) and Upload Post Image (huggingface/blog, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Facct Camera Ready?
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.