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 agentmods add skills/pedrohcgs/claude-code-my-workflow/preregisternpx skills add pedrohcgs/claude-code-my-workflow --skill preregistergit 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/preregister)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/preregister"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/preregister.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00153 | $0.02933 |
| Opus 5 | $0.00077 | $0.01466 |
| Sonnet 5 | $0.00031 | $0.00587 |
| Haiku 4.5 | $0.00015 | $0.00293 |
Grade A, and why
preregister 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 6d 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:
- preregister — 89% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/preregister — Preregistration Document Generator
Produce a registry-ready preregistration document. The user uploads it to a real registry (OSF / AsPredicted / AEA RCT Registry) — this skill writes the prose and structure, it does not submit anywhere.
Why preregister
Preregistration is a written commitment to your hypotheses, design, and analysis plan before you see the data (or, for observational analyses, before you analyse the realised outcome). It separates confirmatory tests from exploratory tests and protects you from p-hacking, HARKing, and forking-paths. Different fields use different registries:
- Political science, psychology, broad social science → OSF (osf.io/registries) is the default. AsPredicted (aspredicted.org) is a popular short form for experiments.
- Economics field experiments → AEA RCT Registry (socialscienceregistry.org) is mandatory for AEA journal submission since 2018.
- Public health / clinical trials → ClinicalTrials.gov or ISRCTN (NOT covered by this skill — use the trial-registry's own template).
When to use
- Before launching an experiment (lab, field, or survey).
- Before collecting observational data on a target population for a specific RQ.
- Before analysing data you have access to but haven't yet examined for the focal hypothesis.
- During R&R when a referee asks for a written preanalysis plan.
When NOT to use
- After you've seen the realised outcomes and want to "preregister retrospectively" — that is not preregistration. The skill will refuse if the input description includes results.
- For exploratory analyses — those don't need preregistration; they need transparent labelling.
- For meta-analyses — use PROSPERO directly, not this template.
Workflow
PHASE 1 — Read inputs
Two input modes:
--input <path>— a research spec produced by/interview-me(saved underquality_reports/specs/) or any structured Markdown file. Read the spec and extract: research question, hypotheses (directional!), data source, design, sample, analysis approach. If the spec already has apaper_type:field (e.g.,survey-experiment), use it to bias the style choice.- No
--input— prompt the user for a 1–3 paragraph description of the study, then proceed. If the description omits a directional hypothesis, ask once. Do not fabricate.
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.
- 6d ago First seen · 173 lines · 153 tokens per session scan A f7a4395f45bd
preregister is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,562 stars, last pushed 12d ago), licensed MIT. It adds 153 tokens to every session and 2,933 once invoked, about $0.0008 per session on Opus 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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