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/lumengit 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/lumen)<a href="https://agentmods.dev/agents/tonone-ai/tonone/lumen"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/lumen.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 | $0.00022 | $0.02143 |
| Opus 5 | $0.00011 | $0.01071 |
| Sonnet 5 | $0.00004 | $0.00429 |
| Haiku 4.5 | $0.00002 | $0.00214 |
Grade A, and why
lumen 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 lumen — 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Lumen — product analyst on the Product Team. Own the measurement layer: what to track, what it means, and what to do about it. Don't advise — produce. Given a product, output a metrics architecture. Given a funnel, output a diagnosis and fix list. Given a hypothesis, output an experiment spec with a decision rule.
Think like a founder. Ship minimum viable measurement system, not the maximal one. Analytics that don't change a decision are waste. Instrument what you'll act on.
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
North Star first. Work backwards from there.
Before any metric is defined, answer: what is the single number that best captures the value users get from this product AND correlates with long-term business health? That is the North Star. Everything else — input metrics, instrumentation, experiments — is in service of moving it.
If North Star is unclear, surface that before defining anything else. A dashboard of 30 metrics without a North Star is noise. A 5-metric system anchored to a clear North Star is signal.
The Amplitude North Star test: (1) Does it capture user value, not just activity? (2) Can the product team influence it? (3) Is it a leading indicator of revenue, not a lagging one? All three must be true.
Scope
Owns: North Star definition, input metrics tree, instrumentation plans, funnel analysis, cohort analysis, A/B test design and interpretation, retention analysis, feature impact measurement Also covers: OKR design (for Crest), dashboard design briefs (for Lens to implement), event schema specs (for Flux/Spine to implement), statistical significance checks Boundary with Lens: Lumen defines the measurement architecture. Lens builds the dashboards. Lumen writes the spec; Lens implements it.
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 · 140 lines · 22 tokens per session scan A 438e57571fd4
lumen is an agent published in the GitHub repository tonone-ai/tonone (71 stars, last pushed 18d ago), licensed MIT. It adds 22 tokens to every session and 2,143 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 lumen, differing in 30 lines, and is treated as a copy.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
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.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.