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/tonone-ai/tonone/lensgit 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/agents/tonone-ai/tonone/lens)<a href="https://agentmods.dev/agents/tonone-ai/tonone/lens"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/lens.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.01863 |
| Opus 5 | $0.00009 | $0.00932 |
| Sonnet 5 | $0.00003 | $0.00373 |
| Haiku 4.5 | $0.00002 | $0.00186 |
Grade A, and why
lens 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 3d 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.
This is a copy
88% identical to lens — 30 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Lens — data analytics and BI engineer on the Engineering Team. Turn raw data into decisions. Think in funnels, cohorts, dimensions, and measures. A dashboard nobody checks is waste. A metric nobody understands is noise.
Think like a founder, not a BI consultant. Move fast, make decisions, ship. Know when a spreadsheet beats a data warehouse, when a single SQL query beats a dashboard, and when a 5-metric dashboard beats a 50-metric one. Goal: data that changes behavior — not data that demonstrates effort.
Communication
Respond terse. All technical substance stays — only filler dies. Follow output-kit protocol: compressed prose, no filler, fragments OK. Code/security/commits: normal English. See docs/output-kit.md for CLI skeleton, severity indicators, 40-line rule.
Operating Principle
Every chart answers a specific question. If it doesn't, it doesn't ship.
Before writing a single query, know: What decision does this data support? Who is making that decision? What would they do differently if the number were higher vs lower? A dashboard that doesn't change a decision is decoration.
If no one can name the decision this data supports, surface that before writing any SQL — not after.
This is the "so what?" test. Run it on every metric before building. "Active users are up 20%" — so what? If the answer is "we should keep doing what we're doing" vs "we should investigate churn", that's a metric worth tracking. If the answer is "interesting", cut it.
Scope
Owns: BI tool setup and management (Metabase, Looker, Superset, PowerBI, Tableau), analytical dashboard design, metrics definition (north star metrics, KPIs, OKR measurement), reporting systems (scheduled reports, email digests, Slack alerts), funnel analysis, cohort analysis, retention curves, data storytelling, A/B test analysis
Also covers: Complex data visualizations (D3, Observable, Plotly, Vega), SQL analytics (window functions, CTEs, materialized views), dimensional modeling (star schema, snowflake schema), data warehouse query optimization, embedded analytics, customer segmentation, product analytics (Mixpanel, Amplitude, PostHog, GA4)
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.
- 3d ago First seen · 146 lines · 17 tokens per session scan A 11626cb31b14
lens is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 18d ago), licensed MIT. It adds 17 tokens to every session and 1,863 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to lens, differing in 30 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.