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 power-analysisgit 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/power-analysis)<a href="https://agentmods.dev/skills/pedrohcgs/claude-code-my-workflow/power-analysis"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/power-analysis/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/power-analysis"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-code-my-workflow/power-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 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 119 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 120 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 122 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 122 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 | $0.00150 | $0.02920 |
| Opus 5.5 | $0.00060 | $0.01168 |
| Sonnet 5.5 | $0.00030 | $0.00584 |
| Haiku 4.5 | $0.00015 | $0.00292 |
Grade A, and why
power-analysis 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 1mo 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/power-analysis — Power / MDE for study design
Compute the three interlocking quantities of an ex-ante design calculation — power, required N, and minimum detectable effect (MDE) — and emit a power section the user can paste straight into a preregistration. Analytical for standard designs; simulation-based (reusing the /simulation-study harness pattern) for non-standard ones.
Core principle: a power calculation is a design-time commitment made before the data exist. Fix any two of {effect size, N, power} and solve for the third; never back out a "power" number from a realised estimate (that is post-hoc power, and it is uninformative — see "What this skill does NOT do").
When to use
- Before launching an RCT / field / survey experiment — to choose N (or clusters) for a target MDE at 80–90% power.
- Invoked by
/preregisterfor RCTs — the AEA RCT Registry and most IRBs require a power/MDE justification;/preregister'saea-rctstyle follows this skill's phases to fill that section (this skill is user-invoked only, so it is read and followed, not called). - During R&R — when a referee asks "was this study adequately powered to detect the effect you claim?"
- Designing a Monte Carlo — to set
Rand sample sizes before handing off to/simulation-study.
Inputs
$ARGUMENTS may carry flags; missing pieces are elicited in Phase 0.
--mode mde|n|power— solve for MDE given N+power, N given MDE+power, or power given N+MDE. Defaultmde.--design rct|cluster|multiarm|sim— two-arm RCT, clustered RCT (ICC), multiple arms, or simulation-based. Default inferred from the elicited design.--input <path>— a spec from/interview-me(underquality_reports/specs/) to pull the RQ, outcome, and design from.
Workflow
Phase 0 — Elicit the design
Gather the design parameters; ask once for anything missing rather than fabricating. Required:
- Estimand & test: primary outcome, one- vs two-sided test,
alpha(default 0.05), and whether the target is a difference in means, a proportion, or a regression coefficient. - Two of {effect size, N, power}: the effect as a raw difference and in standardized units (Cohen's d = effect / SD) — record both;
powerdefault 0.80. - Baseline mean and SD (or baseline proportion for a binary outcome) — needed to translate raw ↔ standardized effects.
- Allocation: treated:control ratio (default 1:1; unequal allocation costs power — note it).
- Clustering: if randomization is at a group level (village, school, clinic), the ICC (ρ), the average cluster size (m), and number of clusters. Compute the design effect
DEFF = 1 + (m − 1)·ρand the effective N. - Multiplicity: number of arms / primary outcomes; the correction (Bonferroni, Holm, or none) and whether power is per-comparison or familywise.
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.
- 1mo ago Changed 338f316f7ebf
- 1mo ago First seen · 131 lines · 150 tokens per session scan A 55cf542be4a5
power-analysis is a skill published in the GitHub repository pedrohcgs/claude-code-my-workflow (1,672 stars, last pushed 1mo ago), licensed MIT. It adds 150 tokens to every session and 2,920 once invoked, about $0.0006 per session on Opus 5.5. 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.
Other skills, from other repositories
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.
fin-submit-check
A pre-submission checklist for an academic paper and its LaTeX files. It checks journal requirements such as anonymous authorship, length, spacing, margins, citations, figures, data availability, and other submission details.