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/challengenpx skills add pedrohcgs/claude-code-my-workflow --skill challengegit 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/challenge)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/challenge"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/challenge.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 | $0.00138 | $0.01757 |
| Opus 5 | $0.00069 | $0.00879 |
| Sonnet 5 | $0.00028 | $0.00351 |
| Haiku 4.5 | $0.00014 | $0.00176 |
Grade A, and why
challenge 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.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Challenge — does the result survive the choices you didn't make?
A single specification is one draw from a distribution you never looked at.
Why this exists, measured rather than asserted. In a controlled study, 150 autonomous agents were given the same data and the same questions. Effect-size interquartile ranges reached ~10.7 %/yr, and the spread concentrated in discrete measure-choice forks — not in estimation noise. Within a measure family, agents agreed to ~0.25 %/yr. Two findings from that study shape this skill:
- AI peer review left the spread essentially unchanged. Review catches errors; it does not reduce analytical-choice variance. A clean referee report is not robustness.
- Exposure to exemplar papers collapsed the spread by 80–99 % — convergence by imitation, not by correctness. Herding is not agreement.
So the spread has to be measured, not reviewed away.
Preconditions
- A working baseline specification that runs and produces the headline estimate.
- The estimate's estimand stated in words — "the ATT for units treated in 2015, over 2013–2019, on the treated population". If you cannot state it, stop: you cannot challenge a claim you have not defined.
- A fork budget (
--forks, default 64). Grid size is the product of your choices; it grows faster than intuition.
Step 1 — Enumerate the forks, before running anything
List every point where a competent, honest analyst could have chosen differently. Do this before seeing any alternative result, and write it down — the list is the pre-registration of the challenge.
| Fork | Typical alternatives |
|---|---|
| Measure definition | level vs rate vs share; dollar vs count; stock vs flow |
| Sample filter | balanced vs unbalanced; trimming rules; inclusion windows |
| Control set | none / baseline / baseline+trends / interacted |
| Clustering level | unit / treatment-assignment / two-way |
| Weighting | unweighted / population / inverse-propensity |
| Functional form | levels / logs / IHS / Poisson |
| Winsorization | none / 1% / 5% |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 139 lines · 138 tokens per session scan A aac4117e29ad
challenge is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,562 stars, last pushed 12d ago), licensed MIT. It adds 138 tokens to every session and 1,757 once invoked, about $0.0007 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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