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-optimizer-runsgit 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-optimizer-runs)<a href="https://agentmods.dev/skills/getsimba-ai/simba-mcp/simba-optimizer-runs"><img src="https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-optimizer-runs/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-optimizer-runs"><img src="https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-optimizer-runs.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 | $0.00037 | $0.00285 |
| Opus 5.5 | $0.00015 | $0.00114 |
| Sonnet 5.5 | $0.00007 | $0.00057 |
| Haiku 4.5 | $0.00004 | $0.00028 |
Grade A, and why
simba-optimizer-runs 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 25d 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.
What it actually says
Optimise budgets
- Read runs before constructing an optimiser request or quoting its results.
- Fetch exact activity-column channel keys and the scenario template when needed. Confirm budget, horizon, constraints and objective before launching.
- Preserve percentage bounds, positive period CPM arrays and matching channel sets. Profit requires a stored or explicitly supplied margin.
- Poll the returned run_id with backoff. Reconcile uncertain submissions through run history rather than blindly repeating a write.
- Quote the columns appropriate to the question: solver decision values and fitted-convention comparison values have different meanings.
- Check constraint fidelity and uncertainty. Missing evidence is not a valid recommendation, and an optimiser output is not approval to spend.
For advanced parameters or sections not covered here, read the full tool contract before calling.
Role availability: marketer/full can submit explicitly authorised planning runs; reviewer inspects saved evidence. Use saved-run comparison and executable examples. Campaign daily-equivalent suggestions use the campaigns/budgets guidance section and create no optimiser run.
What ships with it
4 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.
- 25d ago Changed · +2 lines 5356b57a6c8b
- 28d ago Changed · -36 lines · -28 tokens per session 2211c6afaf5b
- 1mo ago First seen · 54 lines · 65 tokens per session scan A f9291d34f2c2
simba-optimizer-runs is a skill published in the GitHub repository getsimba-ai/simba-mcp (3 stars, last pushed 14d ago), licensed MIT. It adds 37 tokens to every session and 285 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
aetre
High-performance Bayesian Value-of-Information (VOI) triage, Kingman capacity throttling, and Pareto venture dealflow decision engine.
mmm-api-reference
Exact pymc-marketing API reference for MMM code, verified against v1.1.0. Use when writing or reviewing pymc-marketing code, checking constructor signatures, method names, return types, import paths, or plotting/evaluation/optimisation calls. Also use when migrating 0.x code to 1.x, debugging ImportError or…
mmm-channel-semantics
How each type of marketing channel should be represented in an MMM — its role (paid media, reach-and-frequency, organic, non-media treatment, control), whether it is driven by spend or exposure, its carryover and saturation behaviour, and whether a ROAS is even defined for it. Use when deciding what a column means…
mmm-baseline-and-trend
The baseline of an MMM — intercept, trend, seasonality and controls — and how it competes with media for variance. Use when the baseline is negative, implausibly large or small, when deciding between a fixed intercept, a linear trend, changepoints, knots or a time-varying intercept, when seasonality is absorbing…
mmm-causal-design
Causal identification for MMM — what to control for, what never to control for, and why. Use when deciding which variables to include, when a channel's estimate looks structurally wrong, when handling mediators (search volume, site visits, brand metrics), colliders, funnel effects or reverse causality, when building a…
mmm-data-engineering
Building the MMM dataset: sourcing, joining, aggregating, handling missing data, currency and inflation, fiscal calendars, taxonomy mapping, outliers and reproducible pipelines. Use when assembling data from platform exports and finance systems, deciding how to fill gaps, choosing daily vs weekly, aligning spend to…