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.
git 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/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/agents/tonone-ai/tonone/lumen"><img src="https://agentmods.dev/badge/agents/tonone-ai/tonone/lumen.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 | $0.00022 | $0.02143 |
| Opus 5.5 | $0.00009 | $0.00857 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo ago First seen · 140 lines · 22 tokens per session scan A 438e57571fd4
lumen is an agent published in the GitHub repository tonone-ai/tonone (76 stars, last pushed 13d ago), licensed MIT. It adds 22 tokens to every session and 2,143 once invoked, about $0.0001 per session on Opus 5.5. 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
security-reviewer
Read-only, separate-context security reviewer. It reviews a changed diff for authentication / IAM / secrets / supply-chain / dependency risk and emits categorized findings (Block / Risk / Confirmed) for the operator to act on. Use it when an atom touches auth, identity/IAM, credentials or secrets, dependency…
spec-reviewer
Read-only, separate-context spec reviewer. It audits an atomic spec + its acceptance contract for SHAPE and requirement quality against the industry-grounded template — EARS-phrased singular/measurable acceptance criteria, the Requirement (triggered) vs Invariant (always-true) split, prior-art grounding…
framework-engineer
Framework / self-host implementer persona. Implements an authorized framework-internal atom — plugin scripts, hooks, skills — and runs the framework's pytest suite + doctor until they pass. Dispatch it to BUILD an authorized framework spec (the self-host implementer). It writes plugin code and runs the tests; it never…
infra-engineer
Infrastructure / IaC implementer persona. Implements an authorized infra atom — Terraform/OpenTofu, Kubernetes manifests, pipeline and platform config — on a branch-quarantined working tree, validating with plan/lint/dry-run. Dispatch it to BUILD an authorized infra spec. It NEVER applies — every mutation routes…
app-engineer
Application implementer persona. Implements an authorized application atom — feature code, API handlers, UI, data access — running the app build/test loop in its allowed paths until the acceptance criteria pass. Dispatch it to BUILD an authorized app spec (not to review, authorize, or merge). Writes code and runs…
qa-engineer
Test author / runner persona. Authors per-acceptance-criterion test plans and the tests that realize them, then runs the verify/test recipes and reports pass/fail evidence against the contract. Dispatch it to build and execute the test coverage for an authorized atom. It writes and runs tests in a confined tree; it…