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 differential-auditgit 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/differential-audit)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/differential-audit"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/differential-audit.svg" alt="Measured on agentmods" 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 | $0.00115 | $0.01266 |
| Opus 5 | $0.00057 | $0.00633 |
| Sonnet 5 | $0.00023 | $0.00253 |
| Haiku 4.5 | $0.00012 | $0.00127 |
Grade A, and why
differential-audit 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 8d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Make agreement mean something
Two implementations agreeing proves they satisfy a prespecified contract. It does not prove either is correct, and it never validates the method or its assumptions. Both can be wrong in the same way — especially when one was written by reading the other. Design the comparison so that agreement is informative and disagreement is legible.
Rule: freeze before you compare; test the comparator before you trust it.
1. State the claim and the reference
Write down: what is being compared, which side is the reference, and what agreement would and would not establish. "Matches the R package" is a conformance claim, not a correctness claim. Say so explicitly, so nobody later reads parity as validation.
2. Freeze the inputs before inspecting anything
Record and fix: data versions or hashes, code and package versions, random seeds or realized sample splits, options and defaults, the outputs to be compared, and the acceptance thresholds. Freezing after a first look invites tolerance drift toward whatever the run produced.
Do not compare defaults across systems as if only the language changed. Map the choices explicitly — a "default" is a substantive modeling decision that usually differs between implementations.
3. Declare tolerance classes, and make them binding
Do not carry a single fuzzy epsilon. Classify each output:
- EXACT — names, ordering, sample masks, counts, statuses, return/error codes, warning classes, option defaults. Byte-equal after documented normalization.
- Scalar numeric — deterministic estimates, standard errors, p-values, critical values. State absolute and relative tolerances and the justification.
- Matrix/vector — covariance matrices, influence summaries, weight vectors, plot data.
- Stochastic — must meet a prespecified error-rate criterion with uncertainty reported.
A looser tolerance may be used only through a recorded approved divergence with a reason. Silent widening is the most common way a parity gate stops testing anything.
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.
- 8d ago First seen · 88 lines · 115 tokens per session scan A 72335e59883f
differential-audit is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,567 stars, last pushed 14d ago), licensed MIT. It adds 115 tokens to every session and 1,266 once invoked, about $0.0006 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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