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 agentmods add commands/brainbytes-dev/everything-claude-marketing/analyticsgit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWhat 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 | $0.00018 | $0.01315 |
| Opus 5 | $0.00009 | $0.00658 |
| Sonnet 5 | $0.00004 | $0.00263 |
| Haiku 4.5 | $0.00002 | $0.00131 |
Grade A, and why
analytics 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 2d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analytics
Interpret marketing data, diagnose performance changes, build reporting frameworks, and turn numbers into actionable insights that drive decisions.
What This Command Does
The /analytics command helps you make sense of marketing data. Whether you are investigating a sudden metric change, building a reporting dashboard, or designing a measurement framework from scratch, this command translates raw data into clear insights and concrete next steps. It goes beyond describing what happened to explain why it likely happened and what to do about it.
The command delegates to the analytics-interpreter agent, which applies statistical thinking, marketing attribution models, and diagnostic frameworks to analyze your data and produce actionable recommendations.
When to Use
- A key metric changed significantly and you need to diagnose why
- You want to build a marketing dashboard or reporting framework
- You need to interpret campaign performance data and make optimization decisions
- You are setting up tracking, attribution, or measurement for a new initiative
- You want to calculate ROI, LTV, CAC, or other marketing economics metrics
- You need to present marketing performance to leadership with clear narratives
- You want to design experiments with proper measurement methodology
How It Works
- Data Assessment — Reviews the data you provide, identifies what is available and what is missing
- Diagnostic Analysis — Applies structured diagnostic frameworks to identify root causes of performance changes
- Contextual Interpretation — Considers external factors (seasonality, market changes, algorithm updates) alongside internal factors
- Insight Extraction — Identifies the most significant findings and their business implications
- Recommendation Development — Produces specific, prioritized actions based on the analysis
- Framework Design — When requested, builds measurement frameworks, KPI hierarchies, or reporting templates
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.
- 2d ago First seen · 141 lines · 18 tokens per session scan A 9d884f7be9cb
analytics is a command published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 18 tokens to every session and 1,315 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.