Borrowing it
Nothing to install: this file belongs to cmu-phil/py-tetrad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cmu-phil/py-tetrad/main/.claude/skills/tetrad_analysis/SKILL.mdgit clone --depth 1 https://github.com/cmu-phil/py-tetradWrote 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/cmu-phil/py-tetrad/tetrad_analysis)<a href="https://agentmods.dev/skills/cmu-phil/py-tetrad/tetrad_analysis"><img src="https://agentmods.dev/badge/skills/cmu-phil/py-tetrad/tetrad_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/cmu-phil/py-tetrad/tetrad_analysis"><img src="https://agentmods.dev/badge/skills/cmu-phil/py-tetrad/tetrad_analysis.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00186 | $0.02177 |
| Opus 5 | $0.00093 | $0.01089 |
| Sonnet 5 | $0.00037 | $0.00435 |
| Haiku 4.5 | $0.00019 | $0.00218 |
Grade A, and why
tetrad-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 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tetrad Real-Data Analysis
A workflow for applying Tetrad's causal discovery algorithms to real data
without producing confidently wrong answers. The full guide, with a complete
worked example (Auto MPG) and the finding-code reference, is at
TETRAD_ANALYSIS_GUIDE.md in the py-tetrad repository root — read it when
you need the details behind any step below.
The principle (non-negotiable)
Agents assist; algorithms discover; scientists judge (Zheng, Verma, Gill, Dai, Spirtes & Zhang 2026, arXiv:2606.23608). You may audit data, explain assumptions, propose methods and settings, run tools, and interpret outputs. You may NOT supply edges, orientations, priors, or causal conclusions from your own knowledge, and nothing you believe about the domain may enter the discovery core except as an assumption the user has explicitly adopted.
Rules of engagement. Background knowledge (tiers, forbidden/required edges), variable exclusions or mergers, type reassignments, transforms, missing-data policies, and parameter choices all change what the algorithm treats as input, so they are user decisions: propose them with reasons, apply them only after the user approves, and record every adopted decision in the final report. Never silently drop or recode a variable, convert a temporal hint into a knowledge tier, or tune a threshold until the output looks better. Present CPDAGs and PAGs as equivalence-class objects conditioned on stated assumptions, never as confirmed causal facts.
The workflow: Audit → Decide → Search → Diagnose → Report
Never run one algorithm with default settings and report the graph.
0. Provenance (ask the user; no software can answer these)
What does each variable mean and in what units? How was the sample collected (selection effects)? Is there a defensible partial time order? What plausible common causes are NOT measured — name them? Did the data-generating regime change during collection? Write the answers down; they become the Decisions section of the report.
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 Changed · +8 lines 816012925106
- 9d ago First seen · 165 lines · 186 tokens per session scan A 91e52a50749b
tetrad-analysis is a skill published in the GitHub repository cmu-phil/py-tetrad (98 stars, last pushed today), licensed MIT. It adds 186 tokens to every session and 2,177 once invoked, about $0.0009 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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