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 getsimba-ai/simba-mcp --skill simba-prior-conventionsgit clone --depth 1 https://github.com/getsimba-ai/simba-mcpWrote 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/getsimba-ai/simba-mcp/simba-prior-conventions)<a href="https://agentmods.dev/skills/getsimba-ai/simba-mcp/simba-prior-conventions"><img src="https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-prior-conventions/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/getsimba-ai/simba-mcp/simba-prior-conventions"><img src="https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-prior-conventions.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 | $0.00060 | $0.00669 |
| Opus 5 | $0.00030 | $0.00334 |
| Sonnet 5 | $0.00012 | $0.00134 |
| Haiku 4.5 | $0.00006 | $0.00067 |
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.
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_mean → mean, beta_sd → sd, sat_shape →
sat_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 legacydecay_lower/decay_upper. theta_mean/theta_sdonly withadstock_type="delayed";dual_weight_mean/dual_weight_sdonly 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 legacyalpha_sd/scalarspair in the same override. sat_shape_mean/sat_shape_sdonly withsaturation_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.
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.
- 8d ago First seen · 55 lines · 60 tokens per session scan A 8fe14ce40d36
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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