CtrlK
BlogDocsLog inGet started
Tessl Logo

mmm-modeling

Media Mix Modeling with PyMC-Marketing. Use when building MMMs, specifying adstock/saturation transformations, setting priors, fitting multidimensional (geo-level) models, computing channel contributions, ROAS, running budget optimization, calibrating with lift tests, or performing sensitivity analysis. Covers the MMM class, GeometricAdstock, LogisticSaturation, BudgetOptimizerWrapper, and ArviZ diagnostics for marketing models.

72

Quality

89%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-structured, code-rich skill body that encodes genuinely non-obvious PyMC-Marketing MMM patterns and points cleanly to a complete set of reference files. Its main weaknesses are minor: a few placeholder-bearing snippets and an implicit (rather than explicit) diagnostic failure-recovery loop.

Suggestions

Add an explicit validation feedback loop after the diagnostics block (e.g., 'If divergences > 0 or R-hat >= 1.01: raise target_accept / reparameterize priors, then refit and re-check') to convert the implicit diagnostic gate into a concrete recovery path.

Replace or annotate the placeholder-bearing snippets (coords, spend_shares, sampler_config={...}, yaml_path=...) with concrete minimal values or a clear note pointing to the reference file that supplies them.

Trim the long mmm.summary method enumeration (or move it into references/plot.md or a dedicated summary reference) so the overview stays lean.

DimensionReasoningScore

Conciseness

The body is dense and encodes domain knowledge Claude would not already know (fit on full dataset, time-slice CV for stability, prior choices, BudgetOptimizerWrapper vs BudgetOptimizer), but the long mmm.summary method enumeration and a few framing sentences could be trimmed or moved to a reference — efficient with minor over-explanation.

4 / 5

Actionability

Heavily code-driven with copy-paste-ready snippets for the central operations (Quick Start, Model Specification, Diagnostics, Budget Optimization, Lift Test), though a few blocks use placeholders ([...], coords, spend_shares, sampler_config={...}, yaml_path=...) rather than fully executable values, leaving minor gaps.

4 / 5

Workflow Clarity

A clear sequenced 'Typical MMM Workflow' diagram plus ordered per-section steps, with stated validation thresholds (divergences must be 0, R-hat must be < 1.01); the recovery/feedback loop for failed diagnostics is only implicit rather than an explicit 'if failing, fix and refit' gate, keeping it just below 5.

4 / 5

Progressive Disclosure

Each section is a concise overview with clearly signaled, one-level-deep references to real bundle files (all 9 referenced references/*.md files exist), content is appropriately split between inline overview and detailed references, and navigation is easy with no nested references.

5 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, concrete description that explicitly pairs capabilities with natural 'Use when…' trigger phrases and named API components. It is comprehensive, distinct, and free of fluff or first/second-person voice.

DimensionReasoningScore

Specificity

Lists many concrete actions (specifying adstock/saturation, setting priors, fitting geo-level models, computing contributions/ROAS, budget optimization, lift-test calibration, sensitivity analysis) plus named API entities (MMM, GeometricAdstock, LogisticSaturation, BudgetOptimizerWrapper, ArviZ) — comprehensive coverage matching the anchor-5 example.

5 / 5

Completeness

Explicitly answers both 'what' (Media Mix Modeling with PyMC-Marketing covering the MMM class and named components) and 'when' via a concrete 'Use when building MMMs, specifying adstock/saturation transformations…' clause, matching the anchor-5 example.

5 / 5

Trigger Term Quality

Uses natural domain terms a marketing-analytics user would actually say ('Media Mix Modeling', 'MMMs', 'ROAS', 'budget optimization', 'lift tests', 'sensitivity analysis', 'geo-level') along with the brand 'PyMC-Marketing', giving comprehensive keyword coverage including synonyms.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear PyMC-Marketing-MMM niche with distinct named-component triggers (GeometricAdstock, BudgetOptimizerWrapper) and domain-specific terms (ROAS, lift tests, adstock) that minimize conflict with generic or sibling modeling skills.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
pymc-labs/pymc-marketing
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.