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/interview-menpx skills add pedrohcgs/claude-code-my-workflow --skill interview-megit 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/interview-me)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/interview-me"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/interview-me.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 | $0.00103 | $0.01540 |
| Opus 5 | $0.00051 | $0.00770 |
| Sonnet 5 | $0.00021 | $0.00308 |
| Haiku 4.5 | $0.00010 | $0.00154 |
Grade A, and why
interview-me 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.
Research Interview
Conduct a structured interview to help formalize a research idea into a concrete specification.
Input: $ARGUMENTS — a brief topic description or "start fresh" for an open-ended exploration.
How This Works
This is a conversational skill. Instead of producing a report immediately, you conduct an interview by asking questions one at a time, probing deeper based on answers, and building toward a structured research specification.
Do NOT use AskUserQuestion. Ask questions directly in your text responses, one or two at a time. Wait for the user to respond before continuing.
Interview Structure
Phase 1: The Big Picture (1-2 questions)
- "What phenomenon or puzzle are you trying to understand?"
- "Why does this matter? Who should care about the answer?"
- After the user answers, optionally ask: "Do you have a sense of what kind of paper this would be — reduced-form / structural / theory+empirics / descriptive / formal-theory / survey-experiment / unsure?" (See
.claude/agents/methods-referee.mdfor the type definitions and.claude/references/discipline-cards.mdfor field-default frequencies.) Record the answer in the saved spec under the**Paper type:**header field; "unsure" is fine and is recorded as**Paper type:** unsure.
Phase 2: Theoretical Motivation (1-2 questions)
- "What's your intuition for why X happens / what drives Y?"
- "What would standard theory predict? Do you expect something different?"
Phase 3: Data and Setting (1-2 questions)
- "What data do you have access to, or what data would you ideally want?"
- "Is there a specific context, time period, or institutional setting you're focused on?"
Phase 4: Identification (1-2 questions)
- "Is there a natural experiment, policy change, or source of variation you can exploit?"
- "What's the biggest threat to a causal interpretation?"
Phase 5: Expected Results (1-2 questions)
- "What would you expect to find? What would surprise you?"
- "What would the results imply for policy or theory?"
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 · 103 tokens per session scan A 78c572dea1c2
interview-me is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,563 stars, last pushed 12d ago), licensed MIT. It adds 103 tokens to every session and 1,540 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 2026-08-30.
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