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-telemetry)<a href="https://agentmods.dev/rules/dewtech-technologies/dare-method/skill-telemetry"><img src="https://agentmods.dev/badge/rules/dewtech-technologies/dare-method/skill-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-telemetry"><img src="https://agentmods.dev/badge/rules/dewtech-technologies/dare-method/skill-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 | $0.01553 |
| Opus 5.5 | $0.00000 | $0.00621 |
| Sonnet 5.5 | $0.00000 | $0.00311 |
| Haiku 4.5 | $0.00000 | $0.00155 |
Grade A, and why
skill-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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rastreamento de Telemetria (Cursor - Tokens e Modelos)
Você é um especialista em monitoramento e observabilidade. Seu objetivo é rastrear e registrar o consumo de tokens e modelos de IA em cada etapa do Método DARE para fins de auditoria, monitoramento de performance e análise de uso. O Cursor é a IA utilizada (por compliance), então registre qual modelo do Cursor foi usado (GPT-4, Claude, Gemini, etc).
Modelos Disponíveis no Cursor
O Cursor suporta múltiplos modelos de IA. Registre qual foi utilizado em cada etapa:
| Modelo | Provedor | Características | Melhor Para |
|---|---|---|---|
| GPT-4 Turbo | OpenAI | Rápido e versátil | Tarefas gerais, código |
| Claude 3.5 Sonnet | Anthropic | Análise profunda | Análise complexa, segurança |
| Gemini 2.0 Flash | Ultra rápido | Tarefas simples, processamento rápido | |
| Modelos Locais | Customizados | Privacidade total | Dados sensíveis |
Estrutura de Rastreamento
Cada etapa do DARE deve registrar as seguintes informações em um arquivo de telemetria:
Arquivo de Telemetria: DARE/TELEMETRY.md
Este arquivo centraliza todas as métricas de consumo. Ele deve ser atualizado ao final de cada comando executado.
# Telemetria do Projeto: [Nome do Projeto]
## Resumo Executivo
- **Projeto:** [Nome]
- **Data de Início:** [Data]
- **Tokens Totais Processados:** [Número] (monitoramento de uso)
- **IA Utilizada:** Cursor (por compliance)
- **Modelos do Cursor Utilizados:** [Lista de modelos]
- **Tempo Total de Execução:** [Tempo]
## Detalhamento por Etapa
### 1. Design (`/generate-design`)
- **Data/Hora:** [Timestamp]
- **Modelo do Cursor:** GPT-4 Turbo (ou Claude, Gemini)
- **Tokens Estimados:** 7,390
- **Tempo de Execução:** 45 segundos
- **Comando Executado:** `/generate-design "Criar API de autenticação"`
- **Observações:** [Qualidade da resposta, ajustes necessários, etc]
### 2. Blueprint (`/generate-blueprint`)
- **Data/Hora:** [Timestamp]
- **Modelo do Cursor:** GPT-4 Turbo (ou Claude, Gemini)
- **Tokens Estimados:** 21,373
- **Tempo de Execução:** 2 minutos
- **Arquivo Processado:** DARE/DESIGN.md
- **Observações:** [Qualidade da arquitetura, ajustes necessários, etc]
### 3. Tasks (`/generate-tasks`)
- **Data/Hora:** [Timestamp]
- **Modelo do Cursor:** GPT-4 Turbo (ou Claude, Gemini)
- **Tokens Estimados:** 33,912
- **Tempo de Execução:** 3 minutos 20 segundos
- **Arquivo Processado:** DARE/BLUEPRINT.md
- **Tasks Geradas:** 12
- **Observações:** [Qualidade das tasks, clareza das especificações, etc]
### 4. Execute Tasks (`/execute-task`)
- **Task 001: Migration de Users**
- Data/Hora: [Timestamp]
- Modelo do Cursor: GPT-4 Turbo (ou Claude, Gemini)
- Tokens Estimados: 7,801
- Tempo: 1 minuto 30 segundos
- Tentativas (Ralph Loop): 1
- Status: ✓ Sucesso
- **Task 002: AuthController**
- Data/Hora: [Timestamp]
- Modelo do Cursor: GPT-4 Turbo (ou Claude, Gemini)
- Tokens Estimados: 11,357
- Tempo: 2 minutos
- Tentativas (Ralph Loop): 2
- Status: ✓ Sucesso
[... mais tasks ...]
## Análise de Tokens Processados
| Etapa | Tokens Estimados | % do Total | Tempo Total |
|-------|------------------|-----------|-------------|
| Design | 7,390 | 5% | 45 seg |
| Blueprint | 21,373 | 15% | 2 min |
| Tasks | 33,912 | 24% | 3 min 20 seg |
| Execute (12 tasks) | 85,234 | 56% | 25 min |
| **TOTAL** | **147,909** | **100%** | **~31 min** |
## Modelos do Cursor Utilizados
- **GPT-4 Turbo:** 147,909 tokens (100%)
- **Claude 3.5 Sonnet:** 0 tokens (0%)
- **Gemini 2.0 Flash:** 0 tokens (0%)
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 · 157 lines · 0 tokens per session scan A ee092f885a20
skill-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 1,553 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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ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
prefer-assertions-over-defensive-checks
Prefer assertions over defensive checks when data is guaranteed to be valid.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.