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/james-traina/compound-science/bayesian-estimationnpx skills add James-Traina/compound-science --skill bayesian-estimationgit clone --depth 1 https://github.com/James-Traina/compound-scienceWhat 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 | $0.00224 | $0.03105 |
| Opus 5 | $0.00112 | $0.01553 |
| Sonnet 5 | $0.00045 | $0.00621 |
| Haiku 4.5 | $0.00022 | $0.00311 |
Grade A, and why
bayesian-estimation 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 2d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bayesian Estimation
Reference for Bayesian estimation in quantitative social science: from prior elicitation to MCMC implementation to posterior reporting. Covers the full workflow of specifying a Bayesian model, running inference, diagnosing convergence, and communicating results — with applications to structural models, hierarchical designs, and small-sample settings.
When to Use This Skill
Use when the user is:
- Specifying priors and setting up a Bayesian model in Stan, PyMC, NumPyro, brms, or rstanarm
- Running MCMC and diagnosing R-hat, ESS, divergences, or trace plots
- Implementing hierarchical (multilevel) models with partial pooling
- Adding Bayesian inference to a structural model (BLP, dynamic discrete choice, DSGE)
- Reporting credible intervals, posterior predictive checks, or model comparison statistics
- Eliciting priors from calibration targets or literature benchmarks
- Debugging sampling pathologies: divergences, low acceptance rates, poor mixing
Skip when:
- The model is large-N and well-identified (frequentist MLE/GMM is more efficient and faster)
- The task is pure structural estimation without Bayesian components (use
structural-modelingskill) - The user needs classical causal inference (use
causal-inferenceskill)
When to Use Bayesian Estimation
Advantages
Small samples: Priors act as regularization. With N < 100 observations and several parameters, MLE can overfit or fail to converge. A weakly informative prior is often equivalent to several additional observations of prior knowledge.
Hierarchical structure: When data have natural groupings (markets, countries, firms), Bayesian partial pooling is more efficient than either pooling all groups (ignores variation) or fitting each group separately (ignores shared structure). Random effects Bayesian models borrow strength across groups.
Uncertainty propagation: The posterior is a full distribution. Downstream quantities (elasticities, welfare changes, counterfactuals) inherit full uncertainty without a delta method approximation.
What ships with it
3 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.
- 2d ago First seen · 222 lines · 224 tokens per session scan A 65bf5bc17fe6
bayesian-estimation is a skill published in the GitHub repository James-Traina/compound-science (13 stars, last pushed 5mo ago), licensed MIT. It adds 224 tokens to every session and 3,105 once invoked, about $0.0011 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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