claude-code-my-workflow is a forkable setup for using Claude Code to produce and review academic papers, slides, data analyses, and replication packages. Researchers use its agents, skills, rules, hooks, and quality checks to coordinate these tasks and verify their results. The catalogue entries define the reusable workflow components for Claude Code.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pedrohcgs/claude-code-my-workflow --skill proofreadgit clone --depth 1 https://github.com/pedrohcgs/claude-code-my-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/proofread)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/proofread"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/proofread/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/proofread"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/proofread.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.00487 |
| Opus 5.5 | $0.00031 | $0.00195 |
| Sonnet 5.5 | $0.00015 | $0.00097 |
| Haiku 4.5 | $0.00008 | $0.00049 |
Grade A, and why
proofread scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 22d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- proofread — 92% identical, 7 lines differ
What it actually says
Proofread Lecture Files
Run the proofreading protocol on lecture files. This produces a report of all issues found WITHOUT editing any source files.
Steps
-
Identify files to review:
- If
$ARGUMENTSis a specific filename: review that file only - If
$ARGUMENTSis "all": review all lecture files inSlides/andQuarto/
- If
-
For each file, launch the proofreader agent that checks for:
GRAMMAR: Subject-verb agreement, articles (a/an/the), prepositions, tense consistency TYPOS: Misspellings, search-and-replace artifacts, duplicated words OVERFLOW: Overfull hbox (LaTeX), content exceeding slide boundaries (Quarto) CONSISTENCY: Citation format, notation, terminology ACADEMIC QUALITY: Informal language, missing words, awkward constructions
-
Produce a detailed report for each file listing every finding with:
- Location (line number or slide title)
- Current text (what's wrong)
- Proposed fix (what it should be)
- Category and severity
-
Save each report to
quality_reports/:- For
.texfiles:quality_reports/FILENAME_report.md - For
.qmdfiles:quality_reports/FILENAME_qmd_report.md
- For
-
Leave source files unchanged — fixes are applied after the user reviews the report. Only produce the report. Fixes are applied separately after user review.
-
Present summary to the user:
- Total issues found per file
- Breakdown by category
- Most critical issues highlighted
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 22d ago Changed 40c794ce26b0
- 1mo ago First seen · 44 lines · 77 tokens per session scan A 81dbb9a9b5b0
proofread is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,664 stars, last pushed 22d ago), licensed MIT. It adds 77 tokens to every session and 487 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
fin-paper-convert
Compile LaTeX to PDF and convert to target journal format.
r-econometrics
Run IV, DiD, and RDD analyses in R with proper diagnostics.
paper-workflow
A staged workflow for producing an evidence-based economics or social-science research paper, from choosing a topic through submission. It coordinates existing tools and analysis options, including Python, Stata, and R.
aer-consistency
Use when auditing a finished or near-finished AER, AER:Insights, or AEJ manuscript for internal consistency: headline numbers across abstract, introduction, results, and tables; sample sizes; log-point and percentage-point conversions; cross-references; and in-text-citation/bibliography matching. Apply after the body…
econ-management-paper-polish
A writing and editing guide for papers in economics, management, finance, accounting, marketing, information systems, public administration, and related fields.
did-causal
Use this Skill when the user needs to estimate causal treatment effects using difference-in-differences (DID) designs: two-way fixed effects (TWFE) regression, parallel trends pre-testing, Callaway-Sant'Anna staggered adoption estimator, and Goodman-Bacon decomposition. Covers both Python (linearmodels) and R (did…