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 skills/dewtech-technologies/dare-method/dare-telemetrynpx skills add dewtech-technologies/dare-method --skill dare-telemetrygit clone --depth 1 https://github.com/dewtech-technologies/dare-methodWhat 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 | $0.00069 | $0.01859 |
| Opus 5 | $0.00034 | $0.00929 |
| Sonnet 5 | $0.00014 | $0.00372 |
| Haiku 4.5 | $0.00007 | $0.00186 |
Grade A, and why
dare-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 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.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DARE Telemetry Skill
Você é um especialista em observabilidade e monitoramento de uso de IA. Seu papel é rastrear consumo de tokens e modelos usados em cada etapa do DARE, mantendo DARE/TELEMETRY.md atualizado.
Diferença com
dare-quality-telemetry:
dare-telemetry(esta) — rastreia uso de IA (modelos, tokens, tempo) por comando DAREdare-quality-telemetry— agrega métricas de qualidade das skills (M-01 a M-04)
Quando usar
- Ao final de cada comando DARE executado
- Para gerar relatório periódico de uso de IA
- Para auditoria de compliance (qual IA foi usada, onde)
- Para otimização de custos (qual etapa consome mais)
Modelos rastreáveis
Independente do IDE/agente:
| Modelo | Provider | Características | Melhor para |
|---|---|---|---|
| Claude Opus 4.7 | Anthropic | Análise profunda, refactor longo | Design, blueprint, security review |
| Claude Sonnet 4.5 | Anthropic | Equilíbrio velocidade/qualidade | Execução de tasks |
| GPT-4 Turbo | OpenAI | Versátil | Tarefas gerais |
| Gemini 2.0 Flash | Ultra rápido | Tasks simples, processamento em batch | |
| Modelos locais (Ollama) | Self-hosted | Privacidade total | Dados sensíveis |
Estrutura DARE/TELEMETRY.md
# Telemetria do Projeto: [Nome]
## Resumo Executivo
- **Projeto:** [Nome]
- **Data de início:** [ISO 8601]
- **Tokens totais processados:** [Número]
- **Modelos utilizados:** [Lista]
- **Tempo total de execução:** [Tempo]
- **Custo estimado:** $[X]
## Detalhamento por Etapa
### 1. Design (`/dare-design`)
- **Data/Hora:** [Timestamp]
- **Modelo:** Claude Opus 4.7
- **Tokens estimados (in/out):** 7,390 / 1,250
- **Tempo de execução:** 45 segundos
- **Comando:** `/dare-design "Criar API de autenticação"`
- **Resultado:** DESIGN.md gerado, 12 RFs, 8 RNFs, 5 RS
- **Observações:** [Ajustes manuais necessários, etc.]
### 2. Blueprint (`/dare-blueprint`)
- **Data/Hora:** [Timestamp]
- **Modelo:** Claude Opus 4.7
- **Tokens estimados (in/out):** 21,373 / 4,800
- **Tempo:** 2 min
- **Arquivo processado:** DARE/DESIGN.md
- **Resultado:** BLUEPRINT.md com 8 fases, 25 tabelas, 4 diagramas Mermaid
### 3. Tasks (`/dare-tasks`)
- **Tokens estimados:** 33,912 / 8,200
- **Tempo:** 3 min 20 seg
- **Arquivo processado:** DARE/BLUEPRINT.md
- **Tasks geradas:** 12
### 4. Execute Tasks (`/dare-execute`)
- **task-001 — Migration de Users**
- Modelo: Claude Sonnet 4.5
- Tokens: 7,801 / 2,500
- Tempo: 1 min 30 seg
- Tentativas (Ralph Loop): 1
- Status: ✓ Sucesso
- **task-002 — AuthController**
- Modelo: Claude Sonnet 4.5
- Tokens: 11,357 / 3,200
- Tempo: 2 min
- Tentativas: 2 (1 falha por typo)
- Status: ✓ Sucesso
## Análise
| Etapa | Tokens in/out | % do total | Tempo |
|---|---|---|---|
| Design | 7,390 / 1,250 | 5% | 45 seg |
| Blueprint | 21,373 / 4,800 | 15% | 2 min |
| Tasks | 33,912 / 8,200 | 24% | 3 min 20 seg |
| Execute (12 tasks) | 85,234 / 24,000 | 56% | 25 min |
| **TOTAL** | **147,909 / 38,250** | **100%** | **~31 min** |
## Modelos utilizados
- Claude Opus 4.7: 62,675 tokens (42%) — design + blueprint + tasks
- Claude Sonnet 4.5: 85,234 tokens (58%) — execução de tasks
## Custo estimado
| Modelo | Tokens in | Tokens out | $/M in | $/M out | Total |
|---|---|---|---|---|---|
| Opus 4.7 | 62,675 | 14,250 | $15 | $75 | $2.01 |
| Sonnet 4.5 | 85,234 | 24,000 | $3 | $15 | $0.62 |
| **Total** | | | | | **$2.63** |
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 · 189 lines · 69 tokens per session scan A 0172c1c2695a
dare-telemetry is a skill published in the GitHub repository dewtech-technologies/dare-method (5 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 1,859 once invoked, about $0.0003 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.
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