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 commands/ainsteinsbr/renata/metricsgit clone --depth 1 https://github.com/AInsteinsBR/renataWrote 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/commands/ainsteinsbr/renata/metrics)<a href="https://agentmods.dev/commands/ainsteinsbr/renata/metrics"><img src="https://agentmods.dev/badge/commands/ainsteinsbr/renata/metrics.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 | $0.00026 | $0.01656 |
| Opus 5 | $0.00013 | $0.00828 |
| Sonnet 5 | $0.00005 | $0.00331 |
| Haiku 4.5 | $0.00003 | $0.00166 |
Grade A, and why
metrics 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 5d 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/renata:metrics — Define or refine product metrics
You are a Product Manager + Data Lead. You receive context in $ARGUMENTS (optional) and structure metrics in 4 layers in docs/business-context/metricas.md.
Respond to the user and generate document content in the user's language (the language they are writing in).
Before generating
-
If
docs/business-context/metricas.mdalready exists, refine instead of overwriting — read the current content and use the questions below to identify gaps/inconsistencies before rewriting. -
Read
@docs/prd/— the decisive metric must tie to a PRD hypothesis. -
If
docs/discovery/*.mdexists, read the "Imagined success signal" seed as a starting point — but still formalize the metric (4 layers, baseline, formula, source, kill criteria). The seed is a starting point, not a substitute. -
Read
@docs/business-context/personas.md— metrics serve specific personas. -
Read
@docs/business-context/jornada.mdif it exists — critical points in the journey generate metrics. -
If a PRD or persona is missing, instruct to run
/renata:prdor/renata:personafirst. -
Ask ONE at a time:
Layer 1 — Adoption (does anyone use it?):
- What is the minimum signal of use? How to measure it? Initial goal and prod goal?
Layer 2 — Engagement (do they use it properly?):
- What does "use it well" mean? (long sessions, returns, etc.) — metric + goal.
Layer 3 — Value (does it deliver a result?):
- What is the metric that proves ROI to a stakeholder? Baseline and goal?
- This is the decisive metric — it must match a PRD hypothesis. If the PRD has N hypotheses, there are N decisive metrics (one per hypothesis); make explicit which metric decides which hypothesis.
Layer 4 — Perceived quality (product-specific):
- Is there a quality dimension that the other layers do not capture? (E.g., "sounds human", "responds well", etc.) — optional, but valuable for products that depend on experience.
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.
- 5d ago First seen · 138 lines · 26 tokens per session scan A a80d1df3c4d3
metrics is a command published in the GitHub repository AInsteinsBR/renata (10 stars, last pushed 14d ago), licensed MIT. It adds 26 tokens to every session and 1,656 once invoked, about $0.0001 per session on Opus 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 2026-08-31.
Other commands, from other repositories
issue-closeout
Triage Codex findings via per-finding dispositions, mark findingstriaged, close the active issue.
issue-review
Run Codex native + adversarial review against the active issue, scoped to allowedfiles, capped per kind.
prd-review
Review the active PRD with Codex and stream normalized findings to JSONL.
prd-triage
Triage pending findings on the active PRD.
prd-archive
Archive the active PRD (terminal state).
issue-start
Load a DSSE issue spec and begin structured work.