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/structural-modelingnpx skills add James-Traina/compound-science --skill structural-modelinggit 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 | $0.00128 | $0.02514 |
| Opus 5 | $0.00064 | $0.01257 |
| Sonnet 5 | $0.00026 | $0.00503 |
| Haiku 4.5 | $0.00013 | $0.00251 |
Grade A, and why
structural-modeling 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 3d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Structural Modeling
Reference for implementing structural econometric models: from economic model to moment conditions to estimated parameters. Covers the full workflow of taking a theoretical model, deriving its empirical content, and recovering structural parameters from data.
When to Use This Skill
Use when the user is:
- Specifying a structural model and deriving moment conditions
- Implementing NFXP or MPEC estimation routines
- Working with BLP-style demand systems (random coefficients logit)
- Building dynamic discrete choice models (Rust, Hotz-Miller CCP)
- Estimating auction models (first-price, ascending, common value)
- Debugging convergence failures in structural estimation
- Choosing between estimation approaches for a given model
Skip when:
- The task is reduced-form causal inference (use
causal-inferenceskill) - The task is pure simulation design (use
numerical-auditoragent) - The user just needs standard regression (statsmodels/linearmodels suffice)
Quick Reference: Structural Methods
| Method | Use Case | Key Package | Estimator |
|---|---|---|---|
| NFXP | Dynamic discrete choice (small state space) | scipy.optimize |
MLE / GMM |
| MPEC | Dynamic discrete choice (large state space, slow inner loop) | cyipopt (IPOPT) |
MLE / GMM |
| BLP | Differentiated products demand with RC logit | pyblp |
GMM (2-step) |
| CCP (Hotz-Miller) | Dynamic models, counterfactuals not needed | scipy |
2-step semiparametric |
| GPV | First-price auctions, nonparametric values | scipy |
Nonparametric |
| Ascending auction | English auctions, private values | scipy |
MLE on order statistics |
The Structural Estimation Workflow
Every structural estimation follows the same logical arc:
Economic Model → Equilibrium/Decision Rule → Observable Implications
→ Moment Conditions → Estimator → Optimization → Inference
Step 1: Model Specification
Define primitives clearly before writing any code:
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.
- 3d ago First seen · 198 lines · 128 tokens per session scan A 8321fb9befe1
structural-modeling is a skill published in the GitHub repository James-Traina/compound-science (13 stars, last pushed 5mo ago), licensed MIT. It adds 128 tokens to every session and 2,514 once invoked, about $0.0006 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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