Audit Preparation
sickn33/agentic-awesome-skills
Audit preparation register: required document, period covered, request and receipt dates, preparer and reviewer, auditor queries and adjustments.
A skill your agent uses when preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final, completing ACM rights and metadata, restoring acknowledgments, landing the…
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Install Sensys Camera Ready
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-camera-ready -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-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/SenSys-Skills/skills/sensys-camera-ready .claude/skills/sensys-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 "sensys-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-camera-ready into .claude/skills/sensys-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-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/SenSys-Skills/skills/sensys-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 sensys-camera-ready -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-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/SenSys-Skills/skills/sensys-camera-ready .agents/skills/sensys-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 "sensys-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-camera-ready into .agents/skills/sensys-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-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 sensys-camera-ready -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-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/SenSys-Skills/skills/sensys-camera-ready .cursor/skills/sensys-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 "sensys-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-camera-ready into .cursor/skills/sensys-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-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 SenSys-Skills/skills/sensys-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 sensys-camera-ready -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sensys-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/SenSys-Skills/skills/sensys-camera-ready .gemini/skills/sensys-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 "sensys-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-camera-ready into .gemini/skills/sensys-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-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 sensys-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 sensys-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/SenSys-Skills/skills/sensys-camera-ready .github/skills/sensys-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 "sensys-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-camera-ready into .github/skills/sensys-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-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 sensys-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 sensys-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/SenSys-Skills/skills/sensys-camera-ready .opencode/skills/sensys-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 "sensys-camera-ready" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SenSys-Skills/skills/sensys-camera-ready into .opencode/skills/sensys-camera-ready/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sensys-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
sensys-camera-ready- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 387 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 SenSys camera-ready — de-anonymizing the double-column ACM final, completing ACM rights and metadata, restoring acknowledgments, landing the…
- Preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final
- SKILL.md covers De-anonymize completely, ACM rights and metadata, Coordinate the artifact badges… and Plan the talk and demo, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Completing ACM rights and metadata
What it does
Sensys Camera Ready is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final, completing ACM rights and metadata, restoring acknowledgments, landing the awarded ACM artifact badges onto the paper, releasing traces and firmware within their constraints, and planning the in-person talk and demo for the merged audience.
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.
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 SenSys camera-ready — de-anonymizing the double-column ACM final
- Completing ACM rights and metadata
- Restoring acknowledgments
- Landing the awarded ACM artifact badges onto the paper
Example prompts
- “/sensys-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
Sensys Camera Ready loads about 1.2k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 387 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). 387 words, ~1,177 tokens.
Download SKILL.mdSave it as
.claude/skills/sensys-camera-ready/SKILL.md (or your agent's skills folder).- name
- sensys-camera-ready
- description
- Use when preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final, completing ACM rights and metadata, restoring acknowledgments, landing the awarded ACM artifact badges onto the paper, releasing traces and firmware within their constraints, and planning the in-person talk and demo for the merged audience.
SenSys Camera-Ready
Acceptance flips the paper from anonymous-under-review to a permanent ACM DL record. The camera-ready is a checklist of irreversible steps: de-anonymize completely, get the ACM rights and metadata exactly right, land any artifact badges onto the printed paper, and prepare to present to a room that now spans the merged SenSys/IPSN/IoTDI communities. A metadata or rights slip here is corrected only by a painful post-publication erratum.
De-anonymize completely
Everything double-blind suppressed now goes back — and nothing may be missed:
[ ] Author names, affiliations, and emails restored on the paper.
[ ] Acknowledgments and funding/grant numbers added back.
[ ] Self-citations returned to first person where natural.
[ ] Artifact links de-blinded to their permanent public homes (with DOIs).
[ ] PDF metadata now correctly names authors (was scrubbed for review).Run the inverse of the submission blindness sweep: the risk now is an un-restored acknowledgment or a still-anonymized repo link, not a leak.
ACM rights and metadata
The ACM e-rights process gates the final. Complete it precisely:
| Item | Action | Failure cost |
|---|---|---|
| e-rights form | Choose the license/transfer the process offers; get the exact rights text + DOI | Wrong rights block on the PDF |
| Rights stamp | Paste ACM's rights text and conference string verbatim into the template | Reprint / erratum |
| Author metadata | Names, affiliations, ORCIDs match the form and the PDF | ACM DL record errors |
| Title/abstract | Match the accepted paper exactly | Indexing mismatch |
| Template version | Use the ACM double-column version the CFP names (待核实 per cycle) | Production rejection |
Extra camera-ready pages are sometimes granted for reviewer-requested additions; confirm the exact allowance for the cycle before spending them.
Show full SKILL.md (173 more words)Show less
Coordinate the artifact badges onto the paper
If you pursued badges (sensys-artifact-evaluation), the camera-ready is where awarded badges are
attached to the printed paper and the ACM DL metadata. Confirm:
[ ] Each awarded badge (Available / Functional / Reproduced) appears on the final PDF.
[ ] The permanent artifact DOI in the paper matches the archived deposit.
[ ] Released traces/firmware honor their consent/NDA/location constraints (sensys-reproducibility).
[ ] The public repo README maps claims → the script/trace that reproduces each figure.Do not release sensor data, firmware, or deployment locations you have not cleared — the public release is permanent.
Plan the talk and demo
SenSys is in-person; presenting to the merged audience means a room with sensor-networks, embedded, IoT, and on-device-AI expertise all present:
- Lead with the physical problem, not the architecture — the same first-page arc as the paper
(
sensys-writing-style), compressed to a few minutes. - Show the measurement, not just the claim: a power trace or a latency CDF lands harder than a bullet saying "efficient."
- A live or recorded demo of the node/deployment is unusually persuasive at SenSys; if you demo, rehearse the failure modes (a dead battery on stage is avoidable).
- Anticipate the merged audience's questions: an IPSN-rooted reviewer asks about the protocol; an IoTDI-rooted one about deployment; an embedded-AI one about footprint. Have all three ready.
Final camera-ready sequence
1. De-anonymize fully; inverse blindness sweep passes.
2. ACM e-rights complete; rights text + DOI stamped verbatim in the template.
3. Metadata (authors, ORCIDs, title, abstract) matches the form and PDF.
4. Awarded badges + artifact DOI on the final PDF; public release cleared.
5. Compile in the correct template version; re-read the produced PDF.
6. Talk + demo built from the measured-behavior arc; merged-audience Q&A prepared.Output format
[Deanon] de-anonymization complete? un-restored items listed
[Rights] ACM e-rights done; rights text + DOI stamped — pass/gap
[Metadata] authors/ORCIDs/title/abstract consistent across form and PDF
[Badges] awarded badges + artifact DOI on the final; release cleared
[Talk] talk/demo built from the measured-behavior arc; Q&A ready
[Open] the one irreversible item still unconfirmed before the deadline© 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 SenSys-Skills/skills/sensys-camera-ready of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sensys 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 |
|---|---|---|---|---|---|---|
| Sensys Camera Ready this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Audit Preparationsickn33/agentic-awesome-skills | 47k | 1 repos | ~5.3k | Automated safety check: Pass | MIT | |
| QA Acceptancepaperclipai/paperclip | 100k | — | ~964 | Automated safety check: Pass | MIT | |
| Release PreparationCherryHQ/cherry-studio | 53k | — | ~4.3k | Automated safety check: Pass | AGPL-3.0 | |
| Acceptance Orchestratorsickn33/agentic-awesome-skills | 47k | 2 repos | ~943 | Automated safety check: Pass | MIT | |
| Acceptance Evidence for Deliverieslobehub/lobehub | 83k | — | ~9.7k | Automated safety check: Pass | Apache-2.0 |
Similar skills
QA Acceptance
paperclipai/paperclip
Produce QA acceptance criteria and a manual validation plan for a feature change — golden path, edge cases, error states, performance limits, and explicit pass/fail evidence.
Release Preparation
CherryHQ/cherry-studio
Prepares a Cherry Studio release by resolving the version, collecting commits, writing bilingual release notes, updating version files and creating a release branch.
Acceptance Orchestrator
sickn33/agentic-awesome-skills
A skill your agent uses when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
Acceptance Evidence for Deliveries
lobehub/lobehub
Verifies a delivery end to end by driving the real product on a CLI, web, desktop or iOS Simulator surface, capturing evidence and publishing a round with the lh CLI.
Prepare Paperclip PR
paperclipai/paperclip
Prepare a Paperclip branch for PR with commits, template body, and checks.
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Questions about Sensys Camera Ready
What does Sensys Camera Ready do?
A skill your agent uses when preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final, completing ACM rights and metadata, restoring acknowledgments, landing the…. Sensys Camera Ready is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final, completing ACM rights and metadata, restoring acknowledgments, landing the awarded ACM artifact badges onto the paper, releasing traces and firmware within their constraints, and planning the in-person talk and demo for the merged audience.
When should I use Sensys Camera Ready?
Sensys Camera Ready fits situations like: preparing an accepted SenSys camera-ready — de-anonymizing the double-column ACM final; completing ACM rights and metadata; restoring acknowledgments; landing the awarded ACM artifact badges onto the paper.
How do I install Sensys Camera Ready in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-camera-ready -a claude-code`. Or copy the skill folder (SenSys-Skills/skills/sensys-camera-ready in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sensys-camera-ready in your project. Claude Code loads it when a task matches its description.
How do I install Sensys Camera Ready in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sensys-camera-ready -a codex`. Or copy the skill folder (SenSys-Skills/skills/sensys-camera-ready in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sensys-camera-ready in your project. Codex loads it when a task matches its description.
Can I use Sensys 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 sensys-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/sensys-camera-ready, .gemini/skills/sensys-camera-ready, .github/skills/sensys-camera-ready and .opencode/skills/sensys-camera-ready in your project.
What does Sensys Camera Ready need to run?
SKILL.md names no scripts, command-line tools or credentials: Sensys Camera Ready is instructions for the agent only.
Does Sensys 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 Sensys 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 Sensys Camera Ready use?
Sensys 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 Sensys Camera Ready 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 Sensys Camera Ready?
Skills that share tags, products or a category with Sensys Camera Ready: Audit Preparation (sickn33/agentic-awesome-skills, 47k stars), QA Acceptance (paperclipai/paperclip, 100k stars), Release Preparation (CherryHQ/cherry-studio, 53k stars) and Acceptance Orchestrator (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sensys Camera Ready?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 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.