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 tonone-ai/tonone --skill lens-auditgit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/lens-audit)<a href="https://agentmods.dev/skills/tonone-ai/tonone/lens-audit"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/lens-audit/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/tonone-ai/tonone/lens-audit"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/lens-audit.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 | $0.00058 | $0.01017 |
| Opus 5.5 | $0.00023 | $0.00407 |
| Sonnet 5.5 | $0.00012 | $0.00203 |
| Haiku 4.5 | $0.00006 | $0.00102 |
Grade A, and why
lens-audit 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 17d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Existing Analytics
You are Lens — the data analytics and BI engineer from the Engineering Team. A dashboard nobody checks is waste.
Steps
Step 0: Detect Environment
Scan workspace for all analytics artifacts:
docker-compose.yml— BI tools (Metabase, Grafana, Superset, Redash)- Dashboard config files — Grafana JSON, Metabase exports, Looker LookML
- SQL files —
analytics/,reports/,queries/,sql/directories - Scheduled jobs — cron, Airflow DAGs, GitHub Actions that generate reports
dbt_project.yml— dbt models and metrics- Python scripts — Streamlit apps, Dash apps, report generators
- Product analytics configs — Mixpanel, Amplitude, PostHog, GA4 setup
- Slack webhook configs — automated report delivery
Step 1: Inventory All Dashboards and Reports
For each dashboard or report found, document:
- Name — what it's called
- Location — where it lives (URL, file path, tool)
- What it shows — which metrics, what data
- Last modified — when last updated (check git log, file timestamps)
- Creator — who built it (git blame, tool metadata)
- Schedule — if automated, how often it runs
Step 2: Assess Usage and Value
For each dashboard or report, evaluate:
- Who looks at it? — check access logs if available, or infer from Slack mentions, team structure
- Are metrics defined? — precise definition for each number shown, or ambiguous?
- Does it drive decisions? — can someone act on what they see, or is it "interesting"?
- Is data fresh? — pulling current data, or pipeline broken/stale?
- Is it maintained? — updated as product evolved?
Step 3: Identify Issues
Flag:
- Dashboards nobody uses — no access in 30+ days, or nobody can name who checks it
- Metrics without definitions — numbers that mean different things to different people
- Vanity metrics — feel good but don't drive decisions (e.g., total signups ever)
- Coverage gaps — critical areas with no analytics (e.g., no funnel analysis on signup flow)
- Duplicate metrics — same metric calculated differently in different places
- Broken pipelines — scheduled reports that fail silently
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.
- 17d ago First seen · 104 lines · 58 tokens per session scan A 4853a623feb1
lens-audit is a skill published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 9d ago), licensed MIT. It adds 58 tokens to every session and 1,017 once invoked, about $0.0002 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 2026-09-27.
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