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 scaffold-exercisesgit 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/scaffold-exercises)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/scaffold-exercises"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/scaffold-exercises/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/scaffold-exercises"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/scaffold-exercises.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 125 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 126 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 127 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 128 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 129 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00101 | $0.01833 |
| Opus 5 | $0.00051 | $0.00916 |
| Sonnet 5 | $0.00020 | $0.00367 |
| Haiku 4.5 | $0.00010 | $0.00183 |
Grade A, and why
scaffold-exercises 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 5d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/scaffold-exercises — Problem Set Scaffolder
Generate a graded problem set as two files: a clean student set (problems only) and a solution key (worked solutions + a one-line explainer per problem). Pattern imported from mattpocock/skills, adapted for economics teaching — the primary lens is graded coursework that mixes derivation, estimation, and code.
Input: $ARGUMENTS — a topic (e.g., "instrumental variables", "consumer theory", "quantile regression") and optional flags. See Flags.
When to use
- You have a lecture or reading and want a matching assignment with an answer key.
- You want a mix of problem types (derive, estimate, code) at a controlled difficulty, with solutions emitted separately so the student file stays clean.
Do not use this to grade submissions, auto-check answers, or build a timed exam — it scaffolds practice/graded material, not assessment infrastructure.
Problem types
| Type | What the student does | Solution artifact |
|---|---|---|
| analytical | Derive / prove / characterize (theory: optimization, identification, comparative statics) | Step-by-step derivation with the key lemma named |
| empirical | Estimate + interpret on a provided or simulated dataset | Expected estimate, sign/magnitude reasoning, common-mistake note |
| coding | Implement an estimator or simulation in R or Stata | Runnable reference snippet + expected output shape |
If no dataset is supplied for an empirical problem, generate a small simulated one with a fixed seed (YYYYMMDD) so the answer key is deterministic and reproducible.
Workflow
Phase 0: Set topic, difficulty, counts, types (Pre-Flight)
Read any source material the user points at (lecture .tex/.qmd, a paper, a dataset header) and produce a Pre-Flight Report before generating problems:
## Pre-Flight Report — Problem Set
**Topic:** [topic]
**Source(s) read:** [lecture/paper/dataset — one-line takeaway each]
**Difficulty:** intro | core | advanced
**Counts by type:** analytical=N, empirical=N, coding=N (total = `--count`)
**Dataset:** [provided path | simulated with seed YYYYMMDD | none]
**Learning objectives:** [2-4 bullets the set should exercise]
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.
- 5d ago First seen · 141 lines · 101 tokens per session scan A fd13d556d9e6
scaffold-exercises is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,567 stars, last pushed 15d ago), licensed MIT. It adds 101 tokens to every session and 1,833 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
econometrics-phd-level
A guide to econometrics, the use of statistics to study relationships in data, based on a 12-part Korean lecture series. It routes questions to explanations of topics such as regression, panel data, instrumental variables, and causal comparisons.
fin-paper-figure
Generate academic-quality figures (>=300 DPI) for economics and finance papers.
fin-paper-writing
A workflow for turning a research outline and study design into a complete economics or finance paper. It coordinates drafting, figure creation, consistency checks, review rounds, LaTeX compilation, and submission checks.
fin-viz-launch
A tool for turning research data and a written description into academic charts. It can choose a suitable chart type, create plotting code with matplotlib or seaborn, and save the result as a high-resolution PDF, SVG, or PNG.
fin-data-acquisition
A data-preparation workflow for financial research. It reads variable definitions from a completed research design and produces data-acquisition work plus executable Python or Stata regression scripts.
fin-lit-review
A systematic literature-review workflow for economics and finance. It searches several academic sources, maps how papers cite one another, and organizes the main findings and research gaps.