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 humanizegit 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/humanize)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/humanize"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/humanize/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/humanize"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/humanize.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 | $0.00163 | $0.02845 |
| Opus 5.5 | $0.00065 | $0.01138 |
| Sonnet 5.5 | $0.00033 | $0.00569 |
| Haiku 4.5 | $0.00016 | $0.00284 |
Grade A, and why
humanize 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.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/humanize — AI-voice audit (detect-and-flag)
Read the target file (or all paper-like files), audit for the canonical AI-voice tells in academic prose, and write a structured report. The skill does not rewrite. The author edits.
Why this skill exists
Referees and editors increasingly recognise AI-generated prose. The tells are not stylistic preferences — they're statistically conspicuous patterns the LLM training distribution produces at higher rates than human academic writers. Five reasons to audit before submission:
- Reviewer suspicion is a tax. Even good substance pays a credibility tax if the prose reads as AI-drafted.
- Journal policy is tightening. A growing number of venues require disclosure or prohibit AI-drafted text.
- AI tells signal weak content. Boilerplate transitions ("Moreover", "It is important to note") almost always cover up logical gaps the author didn't think through.
- You are not the tells. Even authors who use AI tools heavily can preserve their own voice by stripping the model's lexical fingerprint.
- The fix is cheap once you can see it. The cost is detection, not rewriting — once the report flags the tells, removal is mechanical.
What this skill is NOT
- Not a rewriter. No
--rewritemode. An automatic rewriter introduces its own tells and cannot change what a neural detector sees (seewriting-with-ai.md); the author preserves voice by editing manually. - Not a substance reviewer. Use
/review-paperfor argument structure, identification, citations. - Not a grammar checker. Use
/proofreadfor grammar, typos, overflow, citation format. - Not a fact-checker. Use
/verify-claimsfor Chain-of-Verification fact-checking of citations and numeric claims.
/humanize is the voice lens. Run it alongside the others — none of them substitute.
When to use
- Before journal submission.
- Before posting a working paper / preprint / SSRN draft.
- After any AI-assisted prose generation (R&R response drafts, lit-review synthesis, abstract revisions).
- As a self-discipline pass after long writing sessions — your own writing drifts toward LLM patterns when you stare at LLM output all day.
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 12161ebd284e
- 1mo ago First seen · 234 lines · 163 tokens per session scan A ce6aba2aa3f7
humanize is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,664 stars, last pushed 22d ago), licensed MIT. It adds 163 tokens to every session and 2,845 once invoked, about $0.0007 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.
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