simba-prior-conventions

simba-prior-conventions is a skill for Claude Code, Codex from getsimba-ai/simba-mcp. It costs 60 tokens per session (669 once invoked), scanned A, original, MIT.

Instructions for changing the default assumptions, called priors, in Simba marketing mix models. Priors are starting assumptions the model uses before learning from the supplied data.

In plain words
What is it for?
Use it when overriding assumptions about advertising carryover, saturation, marginal effects, or effect half-life in a Simba model.
Why use it?
They help set valid channel-specific assumptions and avoid rejected inputs or silently misplaced settings that could produce an unintended model.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when overriding assumptions about advertising carryover, saturation, marginal effects, or effect half-life in a Simba model.

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Install with agentmods
npx agentmods add skills/getsimba-ai/simba-mcp/simba-prior-conventions
Install

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.

Any agent
npx skills add getsimba-ai/simba-mcp --skill simba-prior-conventions
Clone the repo
git clone --depth 1 https://github.com/getsimba-ai/simba-mcp

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 669 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00060 $0.00669
Opus 5 $0.00030 $0.00334
Sonnet 5 $0.00012 $0.00134
Haiku 4.5 $0.00006 $0.00067

Measured 8d ago against content hash 8fe14ce40d36, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

simba-prior-conventions 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 8d 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.

skills/simba-prior-conventions/SKILL.md · 55 lines

How it starts

The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Simba prior payload conventions

The merge model

Priors are smart defaults (cost shares, industry benchmarks via total_media_effect, channel-type detection) with per-channel overrides merged ON TOP. Each priors[] entry names a channel (matching channels[].name) plus ONLY the fields to override — everything else keeps its smart default.

Strict rejection (#630)

Unknown keys in a priors[] entry are rejected with a 400 naming the field — they used to be dropped silently, fitting a hybrid model. Common misses: beta/beta_meanmean, beta_sdsd, sat_shapesat_shape_mean. name and parameter are rejected too. The same strictness applies to config.sampler and config.var_priors — but NOT to the request root or config itself, where unknown/misplaced keys are silently ignored (so placement mistakes fit a wrong model without error).

Anchor families — pick ONE per concern

Carryover (adstock):

  • Preferred: half_life_lower/half_life_upper (periods until effect halves) over the legacy decay_lower/decay_upper.
  • theta_mean/theta_sd only with adstock_type="delayed"; dual_weight_mean/dual_weight_sd only with "dual_geometric".
  • Adstock types are geometric, delayed, dual_geometric — there is no power-law adstock.

Saturation:

  • Preferred: half_saturation_mean/half_saturation_sd — the 50%-of-maximum-response point in the channel's ACTIVITY units. Cannot be combined with the legacy alpha_sd/scalars pair in the same override.
  • sat_shape_mean/sat_shape_sd only with saturation_type="generalized_log" (small = near-logarithmic, 1.0 ≈ michaelis_menten).
  • half_marginal_mean/half_marginal_sd (generalized_log ONLY): the activity level where MARGINAL returns have halved. Use it instead of half_saturation at near-logarithmic curvature — the 50% point overflows below sat_shape ≈ 0.00098 and 400s, while the half-marginal point is finite at every shape. Cannot be combined with the other two anchors.

Read the full file on GitHub · 55 lines

Changes

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.

  1. 8d ago First seen · 55 lines · 60 tokens per session scan A 8fe14ce40d36

Subscribe to this mod's changes

simba-prior-conventions is a skill published in the GitHub repository getsimba-ai/simba-mcp (0 stars, last pushed 13d ago), licensed MIT. It adds 60 tokens to every session and 669 once invoked, about $0.0003 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 2026-08-31.

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