simba-results-analysis

simba-results-analysis is a skill for Claude Code, Codex from getsimba-ai/simba-mcp. It costs 14 tokens per session (572 once invoked), scanned A, original, MIT.

A guide for reading saved Simba marketing-mix modelling results. Marketing-mix modelling estimates how marketing channels contributed to outcomes, including return on investment and uncertainty.

In plain words
What is it for?
Use it to examine channel contribution, ROI, marginal ROI, uncertainty, diagnostics and windowed statistics from a saved model.
Why use it?
It helps interpret existing results without fitting a new model, while keeping units, time windows, missing evidence and limitations clear.

Skill for Claude CodeCodex

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

Good fit Use it to examine channel contribution, ROI, marginal ROI, uncertainty, diagnostics and windowed statistics from a saved model.

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Install with agentmods
npx agentmods add skills/getsimba-ai/simba-mcp/simba-results-analysis
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-results-analysis
Clone the repo
git clone --depth 1 https://github.com/getsimba-ai/simba-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for simba-results-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-results-analysis/github.svg)](https://agentmods.dev/skills/getsimba-ai/simba-mcp/simba-results-analysis)
Your own site
<a href="https://agentmods.dev/skills/getsimba-ai/simba-mcp/simba-results-analysis"><img src="https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-results-analysis/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.

agentmods 80×15 button for simba-results-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/getsimba-ai/simba-mcp/simba-results-analysis"><img src="https://agentmods.dev/badge/skills/getsimba-ai/simba-mcp/simba-results-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 572 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 $0.00014 $0.00572
Opus 5.5 $0.00006 $0.00229
Sonnet 5.5 $0.00003 $0.00114
Haiku 4.5 $0.00001 $0.00057

Measured 10d ago against content hash aee954579a5f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

simba-results-analysis 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.

skills/simba-results-analysis/SKILL.md · 52 lines

What it actually says

Analyse saved results

Reuse guidance sections already supplied in context. Fetch only missing sections.

Use any supplied model identifier directly in get_model_results. Do not call list_models or another discovery/status tool first: the result read checks the identifier. Discover only if no identifier was supplied or that read rejects it, and only when discovery is authorised.

Choose sufficient sections:

  • Revenue/ROI: channel_summary; coefficients for individual periods.
  • Diagnostics: model_stats and r_hat for a screening rule. If the question contrasts that screen with a coefficient or posterior summary, also request posterior before describing the summary. Do not infer the summary from r_hat names.
  • Decomposition: contributions, model_config and channel_map. Verify which components are media before classifying them; names alone are insufficient.
  • Current/historical marginal ROI: mroi_summary/mroi_periods respectively.
  • Add channel_map for unverified identity or media classification.

Use requested dates, never guessed bounds/year. Inspect dated rows when needed. When the answer names a channel, include channel_map in that same read. Pass granularity only when the question asks for a calendar bucket. The token is week, never weekly. When the question names JSON fields, the answer is only that JSON object. No prose before or after it. A requested section absent from the returned results is not an unknown quantity. For availability, use false and reason not_returned unless the payload already gave a documented reason. For convergence, use unknown and reason not_returned. Null is only for a numeric field the returned evidence does not establish. Before answering, identify every requested output, including combined totals as well as individual values. Supply each once; check none is missing. Keep the requested explanation within 100 words unless more detail is requested or needed for coverage. Check that each formula describes the metric it labels; omit unrelated formulas. Stop once answered: no unsolicited methodology, tool-use or safety footer. Before sending, check every explanatory clause against a returned field, a shown calculation or the documented contract. Remove plausible but unestablished interpretations. If actions are explicitly requested, check the actual call record, including refused attempts; never reconstruct motives.

Apply interpretation; consult the full contract for other sections/parameters.

Role availability: all profiles can inspect saved results and native views with JSON fallback. Executable view examples preserve missing points and KPI units. Campaign and actual-data reporting use their focused Skills or the campaigns/reporting guidance topics.

Files

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.

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. 10d ago Changed · +34 lines · -18 tokens per session aee954579a5f
  2. 14d ago First seen · 18 lines · 32 tokens per session scan A 19d8c7c2a3df

Subscribe to this mod's changes

simba-results-analysis is a skill published in the GitHub repository getsimba-ai/simba-mcp (3 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 572 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 2026-09-30.

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