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 Learning-Bayesian-Statistics/baygent-skills --skill causal-inferencegit clone --depth 1 https://github.com/Learning-Bayesian-Statistics/baygent-skillsWrote 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/learning-bayesian-statistics/baygent-skills/causal-inference)<a href="https://agentmods.dev/skills/learning-bayesian-statistics/baygent-skills/causal-inference"><img src="https://agentmods.dev/badge/skills/learning-bayesian-statistics/baygent-skills/causal-inference/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/learning-bayesian-statistics/baygent-skills/causal-inference"><img src="https://agentmods.dev/badge/skills/learning-bayesian-statistics/baygent-skills/causal-inference.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 29 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 29 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 29 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 35 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 | $0.00172 | $0.02972 |
| Opus 5 | $0.00086 | $0.01486 |
| Sonnet 5 | $0.00034 | $0.00594 |
| Haiku 4.5 | $0.00017 | $0.00297 |
Grade B, and why
causal-inference scanned grade B with 1 finding 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 9d 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.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
ls ~/.claude/skills/bayesian-workflow/SKILL.md 2>/dev/null || ls .claude/skills/bayesian-workflow/SKILL.md 2>/dev/null How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Causal Inference
Dependencies
This skill requires the bayesian-workflow skill for all PyMC modeling steps (priors, sampling, diagnostics, calibration, reporting).
Detect it:
ls ~/.claude/skills/bayesian-workflow/SKILL.md 2>/dev/null || ls .claude/skills/bayesian-workflow/SKILL.md 2>/dev/null
If not found, install it:
git clone https://github.com/Learning-Bayesian-Statistics/baygent-skills.git /tmp/baygent-skills
cp -r /tmp/baygent-skills/bayesian-workflow ~/.claude/skills/
For all PyMC modeling steps (priors, sampling, diagnostics, calibration, reporting), follow the bayesian-workflow skill.
Workflow overview
Every causal analysis follows this sequence. Steps 1-4 are the thinking phase (no code). Steps 5-8 are the doing phase. Think before you do.
- Formulate the causal question — Propose precise estimand (ATE, ATT, LATE, etc.). ⚠️ ASK USER TO CONFIRM.
- Draw the DAG — Propose causal graph with nodes, edges, and explicit non-edges. ⚠️ ASK USER TO CONFIRM. See references/dags-and-identification.md
- Identify — Determine identification strategy (backdoor, front-door, IV, RDD, DiD). ⚠️ ASK USER TO CONFIRM untestable assumptions. See references/dags-and-identification.md
- Choose design — Match problem to method using table below. ⚠️ ASK USER TO CONFIRM. See references/quasi-experiments.md or references/structural-models.md
- Estimate — Build and fit the model. Delegate all PyMC mechanics to bayesian-workflow skill.
- Refute — MANDATORY. Run design-specific robustness checks. See references/refutation.md
- Interpret — Effect size + decision-relevant HDIs + probability of direction.
- Report — Generate
<treatment>-on-<outcome>/report.mdusing the canonical template in references/reporting.md. Runscripts/check_refutation.pyto turn refutation outcomes into pass/marginal/fail ratings, calibrated causal language (causal / suggestive / associational / descriptive), and an ordered next-steps list. Use that output to fill the report's section 7 (causal language calibration) and Suggested Next Steps.
What ships with it
7 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.
- 9d ago First seen · 170 lines · 172 tokens per session scan B 96d32915d5d6
causal-inference is a skill published in the GitHub repository Learning-Bayesian-Statistics/baygent-skills (174 stars, last pushed 5d ago), licensed MIT. It adds 172 tokens to every session and 2,972 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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