Car Methods
franklee16/academic-research-skills
A skill your agent uses when the research design and identification are the bottleneck for a Contemporary Accounting Research (CAR) manuscript — choosing and defending an archival, experimental…
A skill your agent uses when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating…
Install Revacc Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill revacc-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills revacc-data-analysis --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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis .claude/skills/revacc-data-analysis && 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 "revacc-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis into .claude/skills/revacc-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "revacc-data-analysis", 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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysisType 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 revacc-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills revacc-data-analysis --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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis .agents/skills/revacc-data-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "revacc-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis into .agents/skills/revacc-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "revacc-data-analysis", 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 revacc-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills revacc-data-analysis --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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis .cursor/skills/revacc-data-analysis && 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 "revacc-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis into .cursor/skills/revacc-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "revacc-data-analysis", 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 Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis--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 revacc-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills revacc-data-analysis --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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis .gemini/skills/revacc-data-analysis && 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 "revacc-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis into .gemini/skills/revacc-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "revacc-data-analysis", 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 revacc-data-analysisInstalls 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 revacc-data-analysis -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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis .github/skills/revacc-data-analysis && 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 "revacc-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis into .github/skills/revacc-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "revacc-data-analysis", 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 revacc-data-analysis -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 revacc-data-analysis --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/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis .opencode/skills/revacc-data-analysis && 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 "revacc-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis into .opencode/skills/revacc-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "revacc-data-analysis", 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
revacc-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 655 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating…
- Running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering
- SKILL.md covers When to trigger, Get the standard errors right…, Execute the identification,… and Measure accounting constructs…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Executing the identification
What it does
Revacc Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating accounting constructs, and the robustness battery referees expect, plus the data provenance trail. Executes and reports; it does not choose the identification strategy (revacc-methods) or frame the contribution (revacc-contribution-framing).
Its SKILL.md is about 1.6k 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 Business, Finance & HR, covering Accounting and bookkeeping, Data analysis and Reproducible research. 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
- Running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering
- Executing the identification
- Validating accounting constructs
- The robustness battery referees expect
Example prompts
- “/revacc-data-analysis”
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
Revacc Data Analysis loads about 1.6k tokens when it runs. Until then it costs about 110 tokens; SKILL.md has 655 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). 655 words, ~1,650 tokens.
Download SKILL.mdSave it as
.claude/skills/revacc-data-analysis/SKILL.md (or your agent's skills folder).- name
- revacc-data-analysis
- description
- Use when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating accounting constructs, and the robustness battery referees expect, plus the data provenance trail. Executes and reports; it does not choose the identification strategy (revacc-methods) or frame the contribution (revacc-contribution-framing).
Data Analysis & Robustness (revacc-data-analysis)
When to trigger
- Data are built and it is time to estimate and report
- You are unsure how to cluster standard errors for your accounting panel
- Referees will probe endogeneity, construct measurement, or the channel
- You must document the Compustat/CRSP/I/B/E/S/audit-data provenance behind the sample
- An analytical paper needs a stylized empirical illustration of its comparative statics
Get the standard errors right (a RAST signature check)
Empirical-accounting referees scrutinize inference. Default to clustering by firm, and consider two-way clustering by firm and year (Petersen) when both cross-sectional and time-series dependence are present. With few clusters (e.g., a state- or country-level policy), use the wild-cluster bootstrap rather than asymptotic cluster-robust SEs. Match the clustering to the source of correlated shocks implied by your design, and report the choice explicitly — an unjustified SE choice is a fast credibility hit at a journal that often decides in one round.
Execute the identification, don't just assert it
- DiD: report pre-trends and use heterogeneity-robust estimators for staggered timing (Callaway–Sant'Anna / Sun–Abraham), not naive two-way FE.
- RD: report the optimal bandwidth, robust bias-corrected estimates, a manipulation (density) test, and covariate balance at the cutoff.
- IV/2SLS: report the first stage and instrument strength (e.g., F-statistic) and defend the exclusion in words.
- Event studies: report abnormal returns with a defensible benchmark and window, and confront confounding events — central for value-relevance and information-content claims.
Measure accounting constructs credibly
Use measures with precedent in prior RAST/JAR/JAE/TAR work (discretionary accruals, accruals quality, earnings persistence/smoothness, disclosure indices, comparability, audit-quality proxies, bid-ask spread / PIN for information asymmetry). Show the proxy behaves sensibly (validation, correlation with established measures) and test sensitivity to alternative proxies — proxy fragility is one of the most common RAST rejection reasons. For analyst/forecasting work, document I/B/E/S handling (actuals definition, stale-forecast screens, splits adjustments).
The robustness battery referees expect
- Alternative specifications: controls in/out, alternative fixed effects, alternative key-construct measures.
- Subsamples and falsification/placebo tests (effect absent where theory says it should be).
- Sensitivity of identification assumptions (alternative instruments, donut RD, bounds).
- Cross-sectional partitions that confirm the predicted channel (the conditional predictions from
revacc-theory-development). - Sample-construction, winsorization, and screen choices documented and varied.
Show full SKILL.md (304 more words)Show less
Provenance is a deliverable, not a courtesy
RAST does not run JAE's mandatory archive or JAR's posted package as the headline, but referees and the editor still expect a credible, reconstructable sample. Keep top-to-bottom runnable scripts that regenerate every table from raw extracts; document screens, vintages, and access dates for each source; respect database terms of use. If the work entered through the RAST Conference path, keep the version history clean for the conference-issue timeline. Confirm current data/code expectations on the official page (待核实; 检索于 2026-06).
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. RAST is empirical accounting; emphasize identification of disclosure / governance effects and the multiple-testing haircut for mined associations.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.
Checklist
- SE clustering matches the design (firm / firm-and-year; wild bootstrap if few clusters), stated explicitly
- Identification executed with diagnostics (pre-trends / bandwidth / first-stage / balance)
- Modern DiD estimators used for staggered treatment timing
- Key construct validated; results robust to alternative proxies
- Robustness, falsification/placebo, and channel partitions reported
- Winsorization/screens documented and varied; I/B/E/S handling documented where relevant
- Provenance trail (sources, vintages, screens) reconstructable; scripts runnable end-to-end
Anti-patterns
- White/robust SEs on panel data ignoring within-firm correlation.
- Naive two-way-FE DiD with staggered adoption.
- One proxy, no validation for a contested accounting construct.
- Significance fishing across windows, bandwidths, or specifications.
- Confounded event windows in value-relevance/information-content tests.
- Black-box sample: screens and vintages undocumented, results unreproducible.
Output format
【Estimator & SEs】model; clustering (firm / firm×year / wild bootstrap) + justification
【Identification executed】diagnostics reported (pre-trends/bandwidth/first-stage/balance)
【Construct measurement】proxy + validation + alt-proxy robustness
【Robustness/falsification】[...]
【Channel partitions】conditional predictions confirmed? [...]
【Provenance】sources/vintages/screens documented; scripts runnable
【Open issues for referees】[...]
【Next skill】revacc-contribution-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 Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Revacc Data Analysis 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 |
|---|---|---|---|---|---|---|
| Revacc Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Car Methodsfranklee16/academic-research-skills | 223 | 1 repos | ~1.2k | Automated safety check: Pass | None | |
| Jar Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~1.3k | Automated safety check: Pass | None | |
| Jar Tables Figuresfranklee16/academic-research-skills | 223 | 1 repos | ~1.1k | Automated safety check: Pass | None | |
| Tar Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~1.2k | Automated safety check: Pass | None | |
| Tar Tables Figuresfranklee16/academic-research-skills | 223 | 1 repos | ~1.1k | Automated safety check: Pass | None |
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Categories
Questions about Revacc Data Analysis
What does Revacc Data Analysis do?
A skill your agent uses when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating…. Revacc Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering, executing the identification, validating accounting constructs, and the robustness battery referees expect, plus the data provenance trail.
When should I use Revacc Data Analysis?
Revacc Data Analysis fits situations like: running and reporting the empirical analysis for a Review of Accounting Studies (RAST) manuscript — standard-error clustering; executing the identification; validating accounting constructs; the robustness battery referees expect.
How do I install Revacc Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill revacc-data-analysis -a claude-code`. Or copy the skill folder (Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/revacc-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Revacc Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill revacc-data-analysis -a codex`. Or copy the skill folder (Review-of-Accounting-Studies-Skills/skills/revacc-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/revacc-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Revacc Data Analysis 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 revacc-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/revacc-data-analysis, .gemini/skills/revacc-data-analysis, .github/skills/revacc-data-analysis and .opencode/skills/revacc-data-analysis in your project.
What does Revacc Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Revacc Data Analysis is instructions for the agent only.
Does Revacc Data Analysis 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 Revacc Data Analysis 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 Revacc Data Analysis use?
Revacc Data Analysis 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 Revacc Data Analysis use?
About 1.6k 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 Revacc Data Analysis?
Skills that share tags, products or a category with Revacc Data Analysis: Car Methods (franklee16/academic-research-skills, 223 stars), Jar Data Analysis (franklee16/academic-research-skills, 223 stars), Jar Tables Figures (franklee16/academic-research-skills, 223 stars) and Tar Data Analysis (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Revacc Data Analysis?
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.