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/dewtech-technologies/dare-methodWrote 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/rules/dewtech-technologies/dare-method/skill-quality-telemetry)<a href="https://agentmods.dev/rules/dewtech-technologies/dare-method/skill-quality-telemetry"><img src="https://agentmods.dev/badge/rules/dewtech-technologies/dare-method/skill-quality-telemetry/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/rules/dewtech-technologies/dare-method/skill-quality-telemetry"><img src="https://agentmods.dev/badge/rules/dewtech-technologies/dare-method/skill-quality-telemetry.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 | $0.00000 | $0.02097 |
| Opus 5.5 | $0.00000 | $0.00839 |
| Sonnet 5.5 | $0.00000 | $0.00419 |
| Haiku 4.5 | $0.00000 | $0.00210 |
Grade A, and why
skill-quality-telemetry 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.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Quality Telemetry DARE
Você é um engenheiro de plataforma especialista em observabilidade de qualidade. Esta skill agrega as métricas de todas as skills DARE aplicadas no projeto (ax, layered-design, llm-integration, frontend-design, realtime) e detecta regressões.
O que essa skill faz
- Coleta — roda collectors de cada skill filha em paralelo
- Agrega — junta tudo em um snapshot estruturado
- Persiste —
tmp/dare_metrics.json(append-only) - Compara — diff contra
tmp/dare_metrics_baseline.json - Reporta — markdown report no CI ou stdout
- Bloqueia — exit code ≠ 0 se regressão crítica detectada
Arquitetura
┌────────────────────────────────────────────────────────────┐
│ collect.ts (orquestrador) │
└────────────────────────────────────────────────────────────┘
↓ paralelo
┌───────────────┬──────────────┬──────────────┬──────────────┐
│ ax collector │ layered │ llm │ realtime … │
└───────────────┴──────────────┴──────────────┴──────────────┘
↓
┌────────────────────────────────────────────────────────────┐
│ Aggregator → snapshot { skill: { M-01: value, status } } │
└────────────────────────────────────────────────────────────┘
↓
┌────────────────────────────────────────────────────────────┐
│ tmp/dare_metrics.json (append) + Regression detector │
└────────────────────────────────────────────────────────────┘
↓
┌────────────────────────────────────────────────────────────┐
│ Reporter → markdown / JSON / GH Actions step summary │
└────────────────────────────────────────────────────────────┘
Métricas (da própria skill)
| ID | Métrica | Como medir |
|---|---|---|
| M-01 | 100% dos builds incluem coleta | skill instalada = sempre true |
| M-02 | 0 regressões passam despercebidas | test -f tmp/dare_metrics_baseline.json |
| M-03 | Histórico mantido | test -f tmp/dare_metrics.json e tem N entradas |
| M-04 | Workflow GH Actions existe | test -f .github/workflows/dare-metrics.yml |
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 · 249 lines · 0 tokens per session scan A c95f478cac3b
skill-quality-telemetry is a cursor rule published in the GitHub repository dewtech-technologies/dare-method (5 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,097 tokens. 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 2026-09-03.
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