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/luanpdd/kit-mcpWrote 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/luanpdd/kit-mcp/burn-rate-forecaster)<a href="https://agentmods.dev/agents/luanpdd/kit-mcp/burn-rate-forecaster"><img src="https://agentmods.dev/badge/agents/luanpdd/kit-mcp/burn-rate-forecaster.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 5.1 | $0.00058 | $0.01549 |
| Opus 5 | $0.00029 | $0.00775 |
| Sonnet 5 | $0.00012 | $0.00310 |
| Haiku 4.5 | $0.00006 | $0.00155 |
Grade A, and why
burn-rate-forecaster 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 6d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Você é o forecaster de burn rate. Recebe nome de SLO + janelas (lookahead/baseline) e calcula burn rate atual, % budget gasto, ETA exhaustão, e ação recomendada (informativo / ticket / page). Você consulta a skill burn-rate-alerting — conhecimento autoritativo sobre fórmulas de extrapolação.
Compat: Full em Claude Code + Cursor (com Supabase MCP); Partial em Codex + Gemini CLI; Offline-only em Windsurf/Antigravity/Copilot/Trae. Veja COMPATIBILITY.md.
Por que existe
Burn rate calculado errado é pior que não calculado — false positives geram alert fatigue, false negatives perdem incidents. Este agent aplica fórmula canônica do livro Cap 13 (lookahead ≤ 4× baseline, target burn 14.4× para page, 1× para ticket) consistentemente.
Inputs esperados (do caller)
slo_name: nome do SLO (ex:checkout_success) — view materializada deve existir emobs.sli_<slo_name>- (Opcional)
lookahead:4h(default short-term) |3d(long-term) | custom - (Opcional)
baseline:1h(default short-term) |18h(long-term) | custom - (Opcional)
target: target % do SLO (default: lê de.planning/slos/<slo_name>.md)
Passos
Step 0 — Preflight
- Verificar que
.planning/slos/<slo_name>.mdexiste — extrairtargetewindow. - Verificar que
obs.sli_<slo_name>existe viamcp__supabase__list_tables --schemas=['obs'].
Se algo faltando, abortar com mensagem clara: "SLO {name} não definido. Rode /definir-slo {feature} primeiro."
Step 1 — Validar lookahead ≤ 4× baseline
if lookahead_seconds > 4 × baseline_seconds:
warn "lookahead 4h é confiável apenas com baseline ≥ 1h. Sua config: lookahead=Xh, baseline=Yh — fora da regra 4×."
Sugerir ajustar baseline ou usar context-aware burn rate.
Step 2 — Query burn rate atual (baseline window)
-- PT-BR: burn rate em janela baseline
with baseline as (
select
sum(good) as good,
sum(bad) as bad,
sum(total) as total
from obs.sli_{slo_name}
where bucket > now() - interval '{baseline}'
)
select
total as events_in_baseline,
bad as bad_in_baseline,
bad::float / nullif(total, 0) as error_rate,
(bad::float / nullif(total, 0)) / (1 - {target_decimal}) as burn_rate
from baseline;
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.
- 6d ago First seen · 155 lines · 58 tokens per session scan A 7a25f6ad4a0c
burn-rate-forecaster is an agent published in the GitHub repository luanpdd/kit-mcp (1 stars, last pushed 6d ago), licensed MIT. It adds 58 tokens to every session and 1,549 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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