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 James-Traina/compound-science --skill empirical-playbookgit clone --depth 1 https://github.com/James-Traina/compound-scienceWrote 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/james-traina/compound-science/empirical-playbook)<a href="https://agentmods.dev/skills/james-traina/compound-science/empirical-playbook"><img src="https://agentmods.dev/badge/skills/james-traina/compound-science/empirical-playbook/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/james-traina/compound-science/empirical-playbook"><img src="https://agentmods.dev/badge/skills/james-traina/compound-science/empirical-playbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00105 | $0.03209 |
| Opus 5 | $0.00053 | $0.01605 |
| Sonnet 5 | $0.00021 | $0.00642 |
| Haiku 4.5 | $0.00011 | $0.00321 |
Grade A, and why
empirical-playbook 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 10d 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applied Micro Toolkit
Reference for applied micro research design: method selection, diagnostics, inference, pitfalls, reporting standards, and power analysis.
When to Use This Skill
Use when the user is:
- Choosing between empirical methods for a causal question
- Evaluating which identification strategy fits their data and setting
- Running standard diagnostic tests and unsure which ones apply
- Designing a study and needs to calculate statistical power
- Reviewing or critiquing an empirical strategy
- Preparing the "Empirical Strategy" section of a paper
- Downloading macroeconomic or cross-national data (see
references/data-sources.mdfor FRED/World Bank API access)
Skip when:
- Implementation details for a specific method are needed (use
causal-inferenceskill for IV, DiD, RDD, SC, matching) - The task is structural estimation (use
structural-modelingskill) - The task is manuscript preparation or journal logistics (use
submission-guideskill) - The task is formal identification proof (use
identification-proofsskill) - The task is Bayesian model specification (use
bayesian-estimationskill)
After selecting a method, the econometric-reviewer agent can review the implementation and the identification-critic agent can evaluate the identification argument.
Method Selection Decision Tree
Start with the fundamental question: What source of variation identifies the causal effect?
Step 1: What is your source of variation?
| Source of Variation | Method Family | Key Assumption |
|---|---|---|
| Randomized assignment (with full compliance) | Experimental analysis (OLS on treatment indicator) | Random assignment |
| Randomized assignment (with imperfect compliance) | IV / 2SLS using random assignment as instrument | Exclusion restriction, monotonicity |
| Policy change at a sharp threshold | Sharp RDD | Continuity of potential outcomes at cutoff |
| Policy change at a threshold with imperfect compliance | Fuzzy RDD (= IV at the cutoff) | Continuity + monotonicity at cutoff |
| Policy change at a point in time, with affected and unaffected groups | Difference-in-differences | Parallel trends |
| Staggered policy adoption across units over time | Staggered DiD (Callaway-Sant'Anna, Sun-Abraham, etc.) | Parallel trends (conditional on group and time) |
| Rare event affecting a single unit, long pre-treatment data | Synthetic control | Pre-treatment fit implies post-treatment counterfactual |
| Exogenous shifter of treatment that does not affect outcome directly | IV / 2SLS / GMM | Exclusion restriction, relevance, monotonicity |
| Rich set of observables that plausibly captures all confounders | Matching, IPW, AIPW (selection on observables) | Conditional independence (no unobserved confounders) |
| No credible exogenous variation | Sensitivity analysis, bounds, partial identification | Depends on bounding assumptions |
What ships with it
5 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.
- 10d ago First seen · 247 lines · 105 tokens per session scan A ebca7758d0c6
empirical-playbook is a skill published in the GitHub repository James-Traina/compound-science (13 stars, last pushed 5mo ago), licensed MIT. It adds 105 tokens to every session and 3,209 once invoked, about $0.0005 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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