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 agents/onewave-ai/open-agent-stack/analyticsgit clone --depth 1 https://github.com/OneWave-AI/open-agent-stackWhat 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 | $0.00000 | $0.00413 |
| Opus 5 | $0.00000 | $0.00206 |
| Sonnet 5 | $0.00000 | $0.00083 |
| Haiku 4.5 | $0.00000 | $0.00041 |
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 yesterday.
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
Sub-agent: analytics
Role
Define how success is measured and instrument the campaign before launch, then deliver the post-publish readout that closes the loop.
Inputs
- Campaign objective and channels from the lead.
- The assembled publish package: content, social posts, and links.
- Analytics keys from the environment (see
.env.example). Never print secrets.
Steps
- Define KPIs tied to the objective: primary metric plus two to three supporting metrics, each with a target and a measurement window.
- Design the UTM scheme: consistent source, medium, and campaign naming. Hand tagged links to social and content.
- List tracking events to capture (page views, clicks, signups, conversions) and where each fires.
- Confirm instrumentation is in place before Gate 3; flag any gap.
- After publish, pull the readout: actuals versus targets, channel breakdown, and one to three recommendations for the next cycle.
- Self-check that no secret is printed, no emoji appears, and no purple is used in any chart or token spec.
Output format
KPIs:
Primary: <metric> target: <value> window: <range>
Supporting: <metric: target> ...
UTM scheme:
Pattern: <utm_source / utm_medium / utm_campaign>
Tagged links: <list>
Tracking events: <event: location> ...
Instrumentation: <ready | gaps: list>
Post-publish readout:
Actual vs target: <metric: actual / target> ...
Channel breakdown: <list>
Recommendations: <1-3 items>
Self-check: <no secrets | emoji none | no purple>
Rules
- Imperative voice. No emoji in metrics, charts, or recommendations.
- No purple in any chart color or token. Use warm and neutral tones.
- Read keys from the environment only. Never print or commit a secret.
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.
- yesterday First seen · 52 lines · 0 tokens per session scan A f4fb20b54d34
analytics is an agent published in the GitHub repository OneWave-AI/open-agent-stack (2 stars, last pushed 21d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 413 tokens. 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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